Name: Certified Data Science Practitioner
Price: 595 GBP
Availability: InStock

Redhill

<h3><b>Securing Linux systems training course description</b></h3>
      <p>This course teaches you everything you need to know
to build a safe Linux environment. The first section
handles cryptography and authentication with
certificates, openssl, mod_ssl, DNSSEC and filesystem
encryption. Then Host security and hardening is
covered with intrusion detection, and also user
management and authentication. Filesystem Access
control is then covered. Finally network security is
covered with network hardening, packet filtering and
VPNs. </p>
        
				<h5><b>What will you learn</b></h5>
				<ul>
				 
      <li>Secure Linux accounts.</li>
					              <li>Secure Linux file systems.</li>
                   <li>Secure Linux access through the network.</li>
                  </ul>
                  
       
        <h5>
        <b>Securing Linux systems training course details</b> </h5>
				<div>
					<div>
						<ul>
							<li><b> Who will benefit:</b> </li>
							
              <p>Linux technical staff needing to secure their systems.</p>
              
							<li><b>Prerequisites:</b></li>
							
              <p>Linux system administration (LPIC-1)</p>
              
							<li><b>Duration</b></li>
							
              <p>5 days
						</p>
              
							
              
              
						</ul>
					</div>
				</div>
      <h3><b>Securing Linux systems training course contents</b></h3>
      <ul>
				<li><span>Cryptography</span></li><p>
</p><li>Certificates and Public Key Infrastructures</li><p>
				X.509 certificates, lifecycle, fields and certificate
extensions. Trust chains and PKI. openssl. Public and
private keys. Certification authority. Manage server and
client certificates. Revoke certificates and CAs.
</p><li>Encryption, signing and authentication</li><p>
SSL, TLS, protocol versions. Transport layer security
threats, e.g. MITM. Apache HTTPD with mod_ssl for
HTTPS service, including SNI and HSTS. HTTPD with
mod_ssl to authenticate users using certificates. HTTPD
with mod_ssl to provide OCSP stapling. Use OpenSSL
for SSL/TLS client and server tests.
</p><li>Encrypted File Systems</li><p>
Block device and file system encryption. dm-crypt with
LUKS to encrypt block devices. eCryptfs to encrypt file
systems, including home directories and, PAM
integration, plain dm-crypt and EncFS.
</p><li>DNS and cryptography</li><p>
DNSSEC and DANE. BIND as an authoritative name
server serving DNSSEC secured zones. BIND as an
recursive name server that performs DNSSEC validation,
KSK, ZSK, Key Tag, Key generation, key storage, key
management and key rollover, Maintenance and resigning of zones, Use DANE. TSIG.
				</p><li><span>Host Security</span></li><p>
</p><li>Host Hardening</li><p>
BIOS and boot loader (GRUB 2) security. Disable
useless software and services, sysctl for security related
kernel configuration, particularly ASLR, Exec-Shield and
IP / ICMP configuration, Exec-Shield and IP / ICMP
configuration, Limit resource usage. Work with chroot
environments, Security advantages of virtualization.
</p><li>Host Intrusion Detection</li><p>
The Linux Audit system, chkrootkit, rkhunter, including
updates, Linux Malware Detect, Automate host scans
using cron, AIDE, including rule management,
OpenSCAP.
</p><li>User Management and Authentication</li><p>
NSS and PAM, Enforce password policies. Lock
accounts automatically after failed login attempts,
SSSD, Configure NSS and PAM for use with SSSD,
SSSD authentication against Active Directory, IPA,
LDAP, Kerberos and local domains, Kerberos and local
domains, Kerberos tickets.
</p><li>FreeIPA Installation and Samba Integration</li><p>
FreeIPA, architecture and components. Install and
manage a FreeIPA server and domain, Active Directory
replication and Kerberos cross-realm trusts, sudo,
autofs, SSH and SELinux integration in FreeIPA.
				</p><li><span>Access Control</span></li><p>
</p><li>Discretionary Access Control</li><p>
File ownership and permissions, SUID, SGID. Access
control lists, extended attributes and attribute classes.
</p><li>Mandatory Access Control</li><p>
TE, RBAC, MAC, DAC. SELinux, AppArmor and Smack.
</p><li>etwork File Systems</li><p>
NFSv4 security issues and improvements, NFSv4
server and clients, NFSv4 authentication mechanisms
(LIPKEY, SPKM, Kerberos), NFSv4 pseudo file system,
NFSv4 ACLs. CIFS clients, CIFS Unix Extensions, CIFS
security modes (NTLM, Kerberos), mapping and
handling of CIFS ACLs and SIDs in a Linux system.<br />
				</p><li><span>Network Security</span></li><p>
</p><li>Network Hardening</li><p>
FreeRADIUS, nmap, scan methods. Wireshark, filters
and statistics. Rogue router advertisements and DHCP
messages.
</p><li>Network Intrusion Detection</li><p>
ntop, Cacti, bandwidth usage monitoring, Snort, rule
management, OpenVAS, NASL.
</p><li>Packet Filtering</li><p>
Firewall architectures, DMZ, netfilter, iptables and
ip6tables, standard modules, tests and targets. IPv4 and
IPv6 packet filtering. Connection tracking, NAT. IP sets
and netfilter rules, nftables and nft. ebtables. conntrackd
</p><li>Virtual Private Networks</li><p>
OpenVPN server and clients for both bridged and routed
VPN networks. IPsec server and clients for routed VPN
networks using IPsec-Tools / racoon. L2TP.
</p>
      </ul>

Systems & Network Training

Network & Security

Network Engineer

Securing Linux systems

General Admission

<h3><b>LTE optimization training course description</b></h3>
      <p>  This course gives delegates an understanding of the
Key Performance Indicators used within the 3G LTE
(4G) network environment. We investigate the 3GPP
standards for KPIs (including TR 32.814, TS 32.410 &amp;
TS 32.455) these cover GERAN, UMTS &amp; LTE
environments. The course details the optimisation
procedures and the use of Self optimisation, Selforganising
&amp; Self-healing equipment now being
deployed across all releases of mobile networks.</p>
        
				<h5><b>What will you learn</b></h5>
				<ul>
				 
			
                 <li>Explain the RF optimisation flowchart.</li>
																				<li>Describe the importance of Reference Signal
Received Power (RSRP).</li>
                    <li>List many of the 3GPP recommended KPIs.</li>
                    <li>Describe the concept of APN AMBR and UE AMBR
within LTE.</li> 
                    
                    <li>Describe the use of planning and optimisation
computer tools.</li>
                  </ul>
        <h5>
        <b>LTE optimization training course details</b> </h5>
				<div>
					<div>
						<ul>
							<li><b> Who will benefit:</b> </li>
							
              <p>Anyone working with LTE.</p>
              
							<li><b>Prerequisites:</b></li>
							
              <p>Essential LTE
							</p>
              
							<li><b>Duration</b></li>
							
              <p>2 days
						</p>
              
							
              
              
						</ul>
					</div>
				</div>
      <h3><b>LTE optimization training course contents</b></h3>
      <ul>
				<li>Introduction to the cellular environment </li><p>
The architecture, 2G (GSM), GPRS/EDGE, The NGMN (Next Generation Mobile Network), media gateway controller, 3GPP Rel99 the 3G introduction, HSPA deployments, 3G LTE (4G).

</p><li>Cellular network procedures </li><p>
GSM/GPRS call setup, GPRS data call setup, 3G UMTS data call setup, Data call (Voice over LTE) in LTE (using IMS), Circuit Switched Fall Back (CSFB) in LTE, IP packet session in LTE.

</p><li>Introduction to the standards </li><p>
The 3GPP specifications body, The numbering structure for KPIs, 2G KPIs, 3G/UMTS KPIs, 3G LTE KPIs, IMS KPIs.

</p><li>Introduction to the KPI definitions </li><p>
KPI definitions, accessibility KPIs, retainability KPIs, mobility KPIs, utilisation KPIs.

</p><li>In-depth overview of the air Interfaces </li><p>
GSM/GPRS FDMA &amp; TDMA solution, 3G UMTS WCDMA solution, HSPA+ sharing spare power (Codes), The 3G LTE uplink &amp; downlink interface explained, Initial attach procedures explained.

</p><li>Problem analysis </li><p>
Reference Signal Received Power (RSRP), Signal to Interference Noise Ratio (SINR), handover success rates, power adjustments, classification of coverage problems, weak coverage &amp; coverage holes, lack of dominant cells, cross coverage, improper tilt settings, uplink/downlink imbalance, signal quality, Azimuths &amp; tilts to reduce interference, Handover failure due to interference, Service drop causes.

</p><li>Mobile RF performance in 2G/3G </li><p>
Performance counters, KPIs, testing &amp; measurement, Drive testing and survey, Data collection and post processing of data, LTE service optimization, bandwidth, poor coverage, Quality, optimization process, KPI optimization, Root Cause Analysis (RCA) applied to RF issues, optimization tools and software.

</p><li>Advanced LTE network planning &amp; optimization </li><p>
LTE UE measurements (RSRP/RSRQ), LTE capacity planning, RF configuration parameters, LTE cell selection/reselection planning, LTE radio network KPIs, LTE user-centric KPIs, LTE network performance KPIs, LTE system utilization KPIs, LTE RF channel performance predictions, LTE channel information processing, LTE channel multiplexing, Physical layer and structure, MIMO in LTE, LTE resource plan LTE and Self-Organizing Networks (SON).

</p><li>Radio network optimization work flow </li><p>
Work flow, the existing network, Optimization team establishment and cluster division, Single site verification, Alarm check, Cell state check, Radio parameters check, Site verification, Statistics analysis, Coverage problem analysis.

</p><li>Mobile internet and QoS issues </li><p>
Cellular QoS reference models. How QoS impact on KPIs and measurements, Introduction to mobile internet, The concept of shared access technologies, Support for QoS in the WCDMA environment, The PDP context model, Supporting end-to-end QoS, EPS bearer concepts, Default EPS bearer, Dedicated EPS bearer, APN-AMBR, UE-AMBR.</p>
      </ul>

LTE optimisation and capability

<h3><b>CWAP training course description</b></h3>
      <p>This WiFi analysis course consists of hands-on
learning using the latest enterprise wireless LAN
analysis and troubleshooting tools. The course takes an
in-depth look at the functionality of WLANs, intended
operation of the 802.11 protocol and Wi-Fi Alliance
specifications, WLAN frame formatting and structure,
troubleshooting methodology, and protocol analysis. It
also includes extensive training in modern spectrum
analysis with a focus on advanced RF behaviour
analysis, data collection methods, interpreting spectrum
plots and charts, and understanding advanced features
of WLAN spectrum analysers.</p>
        
				<h5><b>What will you learn</b></h5>
				<ul>
				 
       <li>Analyse WiFi frames using Wireshark.</li>
 <li> Explain 802.11 protocol operation.</li>
 <li>Troubleshoot WiFi networks using Wireshark.</li>
 <li>Troubleshoot WiFi networks using spectrum
analysers.
</li>
                  </ul>
       
        <h5>
        <b>CWAP training course details</b> </h5>
				<div>
					<div>
						<ul>
							<li><b> Who will benefit:</b> </li>
							
            Technical Network Staff<br />
					Anyone looking to become a CWAP
              
							<li><b>Prerequisites:</b></li>
							
              <p> Certified Wireless Network Administrator</p>
              
							<li><b>Duration</b></li>
							
              <p>4 days
						</p>
              
							
              
              
						</ul>
					</div>
				</div>
      <h3><b>CWAP training course contents</b></h3>
				<p><b>Principles of WLAN Communication</b><br />
802.11 Working Group, OSI reference model and the 802.11 PHY
and MAC, Communication sublayers and data units, WLAN
architecture components, Organization of station forwarding
Addressing and internetworking operation, Modern WLAN product
architectures.</p>
			
<p><b>Physical (PHY) and MAC Layer Formats and Technologies</b><br />
Physical layer functions, Preamble function and format, Header
purpose and structure, Analysis of PHY problems, Physical PPDU
formats, 802.11b, 802.11a, 802.11g, 802.11n, MAC frame
components, MAC encapsulation, Fields and subfields of the MAC
header, Frame Control, Frame types and subtypes and their uses,
Addressing, Frame body, Data frame format, Control frame format,
Management frame format, Information elements and fields.</p>
			
<p><b>Beaconing and synchronization</b><br />
Scanning, Client state machine, 802.11 contention, QoS,
Admission control, Band steering and airtime fairness mechanisms
Fragmentation, Acknowledgments and Block acknowledgments,
Protection mechanisms and backward compatibility, Power
management, Dynamic Frequency Selection (DFS) and Transmit
Power Control (TPC), Security components, methods, and
exchanges, Roaming procedures exchanges, Future protocol
enhancements.</p>
	
<p><b>802.11n</b><br />
Transmit beamforming, Spatial multiplexing, Maximal Ratio
Combining (MRC), Space-Time Block Coding, 40 MHz channels,
Frame aggregation, HT-OFDM format, Modulation and Coding
Schemes (MCS), HT frame formatting and more.</p>
	
<p><b>Protocol Analysis Tools and Methodology</b><br />
Troubleshooting methodology, Protocol analyser types, Analysis
NIC/adapter selection and constraints, Interpreting results based
on location, Analyzer settings and features, Filtering and channel
scanning, Interpreting decodes, Using advanced analysis features,
Assessing WLAN health and behaviour factors, Evaluating network
statistics, Troubleshooting common problems, Wired analysis to
support wireless network issues.</p>
	
<p><b>Spectrum Analysis Tools and Methodology</b><br />
Radio frequency behaviour review, Visualizing RF domains using
spectrum measurement tools, Spectrum analyser types and
operation, Analyser specifications and characteristics,
Understanding spectrum data presentation, Interpreting plots and
charts, Common WLAN spectrum analyser features, Identifying
transmit patterns, Device classification and network impact,
Recognizing transmit signatures.</p>
	
<h3>Hands on lab exercises</h3>
<b>Wireshark Setup, Use, and In-Depth Analysis</b><br />
Wireshark is fundamental to troubleshooting. Labs include:<br />
				
<p><br />
- Capabilities, configuration, and data display<br />
- Opening, collecting, saving, and modifying capture files.<br />
- Filtering traffic, and using colouring rules as analysis aides.<br />
- Live captures based on a set of desired collection criteria.<br />
- Identify and isolate network problems.<br />
- Conversation analysis.<br />
- Remote packet capture with an AP.<br />
</p><br />
<b>Understanding Frame Components</b><br />
Familiarity with the frame structure and contents is essential in real
-world troubleshooting efforts. Labs include:<br />
<p><br />
- Understanding the MAC header<br />
- Comparing the three major frame types and their subtypes<br />
- Analysing frame formats of individual frame types<br />
- Analysing 802.11n frame components<br />
- Additional information is reported by protocol analysers<br />
- Information not visible in protocol analysers<br />
</p><br />
<b>Frame Exchanges</b><br />
Understanding frame exchange rules and behaviors is critical to
identifying expected and unexpected. It is also necessary to
understand what is normal so that aberrations can be properly
troubleshot. Labs include:<br />
<p><br />
- Connectivity exchanges and sequences<br />
- Legacy and modern security exchanges<br />
- ERP and HT protection mechanisms<br />
- Power save behaviour<br />
- Acknowledgments, block acknowledgments, and
supporting action frames<br />
- Dynamic rate switching<br />
- Band steering</p><br />
				
<b>Troubleshooting Common Problems</b><br /> This lab exposes students
to hands-on troubleshooting skills by setting up common problems
in WLANs and allowing students to attempt to solve them.
<br />
<p><br />
- Trouleshooting connectivity exchanges<br />
- Troubleshooting 802.1X and EAP exchanges<br />
	- Troubleshooting roaming<br /></p>
				
<b>Spectrum Analyzer Setup, Use, and In-Depth Analysis</b><br />
Specifically, it will explore the plots and charts used to display
spectrum data and how to interpret this data to define a
transmitter's impact on the network. The following are covered:
				
<p><br />
- Installing the analyser and using display and navigation<br />
- The 'RF perspective' provided by each plot and chart<br />
- Using built-in features and automated device identification<br />
- Characterizing the behaviours of an interference source<br />
- Assessing the impact of an interference source<br />
- Determining the impact of transmitter proximity on
interference.<br />
- Identifying signatures of common transmitters<br />
- Remote spectrum analysis with an AP<br /></p>

Certified Wireless Analysis Professional

London

<h2>Lean Six Sigma Black Belt Certification Program: In-House Training</h2>
 <p>This course is specifically for people wanting to become Lean Six Sigma Black Belts, who are already Lean Six Sigma practitioners.</p>
 <p>If advanced statistical analysis is needed to identify root causes and optimal process improvements, (Lean) Six Sigma Green Belts typically ask Black Belts or Master Black Belts to conduct these analyses. This course will change that. Green Belts wanting to advance their statistical abilities will have a considerable amount of hands-on practice in techniques such as Statistical Process Control, MSA, Hypothesis Testing, Correlation and Regression, Design of Experiments, and many others.</p>
 <p>Participants will also work throughout the course on a real-world improvement project from their own business environment. This provides participants with hands-on learning and provides the organization with an immediate ROI once the project is completed. IIL instructors will provide free project coaching throughout the course.</p><h3>What you Will Learn</h3> 
 <p>At the end of this program, you will be able to:</p> 
 <ul> 
 <li>Use Minitab for advanced data analysis</li> 
 <li>Develop appropriate sampling strategies</li> 
 <li>Analyze differences between samples using Hypothesis Tests</li> 
 <li>Apply Statistical Process Control to differentiate common cause and special cause variation</li> 
 <li>Explain and apply various process capability metrics</li> 
 <li>Conduct Measurement System Analysis and Gage R&amp;R studies for both discrete and continuous data</li> 
 <li>Conduct and analyze simple and multiple regression analysis</li> 
 <li>Plan, execute, and analyze designed experiments</li> 
 <li>Drive sustainable change efforts through leadership, change management, and stakeholder management</li> 
 <li>Successfully incorporate advanced analysis techniques while moving projects through the DMAIC steps</li> 
 <li>Explain the main concepts of Design for Six Sigma including QFD</li> 
 </ul><section>
                    <h3>Introduction: DMAIC Review</h3> <ul>  <li>IIL Black Belt Certification Requirements</li>  <li>Review Project Selection</li>  <li>Review Define</li>  <li>Review Measure</li>  <li>Review Analyze</li>  <li>Review Improve</li>  <li>Review Control</li>  </ul>  <h3>Introduction: Minitab Tool</h3> <ul>  <li>Introduction to Minitab</li>  <li>Minitab basic statistics and graphs</li>  <li>Special features</li>  <li>Overview of Minitab menus</li>  </ul>  <h3>Introduction: Sampling</h3> <ul>  <li>The Central Limit Theorem</li>  <li>Confidence Interval of the mean</li>  <li>Sample size for continuous data (mean)</li>  <li>Confidence Interval for proportions</li>  <li>Sample size for discrete data (proportions)</li>  <li>Sampling strategies (review)</li>  <li>Appendix: CI and sample size for confidence levels other than 95%</li>  </ul>  <h3>Hypothesis Testing: Introduction</h3> <ul>  <li>Why use advanced stat tools?</li>  <li>What are hypothesis tests?</li>  <li>The seven steps of hypothesis tests</li>  <li>P value errors and hypothesis tests</li>  </ul>  <h3>Hypothesis Testing: Tests for Averages</h3> <ul>  <li>1 factor ANOVA and ANOM</li>  <li>Main Effect Plots, Interaction Plots, and Multi-Vari Charts</li>  <li>2 factor ANOVA and ANOM</li>  </ul>  <h3>Hypothesis Testing: Tests for Standard Deviations</h3> <ul>  <li>Testing for equal variance</li>  <li>Testing for normality</li>  <li>Choosing the right hypothesis test</li>  </ul>  <h3>Hypothesis Testing: Chi Square and Other Hypothesis Test</h3> <ul>  <li>Chi-square test for 1 factor</li>  <li>ANOM test for 1 factor</li>  <li>Chi-square test for 2 factors</li>  <li>Exercise hypothesis tests - shipping</li>  <li>Non-parametric tests</li>  </ul>  <h3>Analysis: Advanced Control Charts</h3> <ul>  <li>Review of Common Cause and Special Cause Variation</li>  <li>Review of the Individuals Control Charts</li>  <li>How to calculate Control Limits</li>  <li>Four additional tests for Special Causes</li>  <li>Control Limits after Process Change</li>  <li>Discrete Data Control Charts</li>  <li>Control Charts for Discrete Proportion Data</li>  <li>Control Charts for Discrete Count Data</li>  <li>Control Charts for High Volume Processes with Continuous Data</li>  </ul>  <h3>Analysis: Non-Normal Data</h3> <ul>  <li>Test for normal distribution</li>  <li>Box-Cox Transformation</li>  <li>Box-Cox Transformation for Individuals Control Charts</li>  </ul>  <h3>Analysis: Time Series Analysis</h3> <ul>  <li>Introduction to Time Series Analysis</li>  <li>Decomposition</li>  <li>Smoothing: Moving Average</li>  <li>Smoothing: EWMA</li>  </ul>  <h3>Analysis: Process Capability</h3> <ul>  <li>Process capability</li>  <li>Discrete Data: Defect metrics</li>  <li>Discrete Data: Yield metrics</li>  <li>Process Capability for Continuous Data: Sigma Value</li>  <li>Short- and long-term capabilities</li>  <li>Cp, Cpk, Pp, Ppk capability indices</li>  </ul>  <h3>Analysis: Measurement System Analysis</h3> <ul>  <li>What is Measurement System Analysis?</li>  <li>What defines a good measurement system?</li>  <li>Gage R&amp;R Studies</li>  <li>Attribute / Discrete Gage R&amp;R</li>  <li>Continuous Gage R&amp;R</li>  </ul>  <h3>Regression Analysis: Simple Correlation</h3> <ul>  <li>Correlation Coefficient</li>  <li>Simple linear regression</li>  <li>Checking the fit of the Regression Model</li>  <li>Leverage and influence analysis</li>  <li>Correlation and regression pitfalls</li>  </ul>  <h3>Regression Analysis: Multiple Regression Analysis</h3> <ul>  <li>Introduction to Multiple Regression</li>  <li>Multicollinearity</li>  <li>Multiple Regression vs. Simple Linear Regression</li>  </ul>  <h3>Regression Analysis: Multiple Regression Analysis with Discrete Xs</h3> <ul>  <li>Introduction</li>  <li>Creating indicator variables</li>  <li>Method 1: Going straight to the intercepts</li>  <li>Method 2: Testing for differences in intercepts</li>  </ul>  <h3>Logistic Regression: Logistic Regression</h3> <ul>  <li>Introduction to Logistic Regression</li>  <li>Logistic Regression - Adding a Discrete X</li>  </ul>  <h3>Design of Experiments: Introduction</h3> <ul>  <li>Design of Experiment</li>  <li>OFAT experimentation</li>  <li>Full factorial design</li>  <li>Fractional factorial design</li>  <li>DOE road map, hints, and suggestions</li>  </ul>  <h3>Design of Experiments: Full Factorial Designs</h3> <ul>  <li>Creating 2k Full Factorial designs in Minitab</li>  <li>Randomization</li>  <li>Replicates and repetitions</li>  <li>Analysis of results: Factorial plots</li>  <li>Analysis of results: Factorial design</li>  <li>Analysis of results: Fits and Residuals</li>  <li>Analysis of results: Response Optimizer</li>  <li>Analysis of results: Review</li>  </ul>  <h3>Design of Experiments: Pragmatic Approaches</h3> <ul>  <li>Designs with no replication</li>  <li>Fractional factorial designs</li>  <li>Screening Design of Experiment Case Study Repair Time</li>  <li>Blocking</li>  </ul>  <h3>Closing: Organizational Change Management</h3> <ul>  <li>Organizational change management</li>  <li>Assuring project sponsorship</li>  <li>Emphasizing shared need for change</li>  <li>Mobilizing stakeholder commitment</li>  </ul>  <h3>Closing: Project Management for Lean Six Sigma</h3> <ul>  <li>Introduction to project management</li>  <li>Project management for Lean Six Sigma</li>  <li>The project baseline plan</li>  <li>Work Breakdown Structure (WBS)</li>  <li>Resource planning</li>  <li>Project budget</li>  <li>Project risk</li>  <li>Project schedule</li>  <li>Project executing</li>  <li>Project monitoring and controlling and Closing</li>  </ul>  <h3>Closing: Design for Lean Six Sigma</h3> <ul>  <li>Introduction to Design for Lean Six Sigma (DMADV)</li>  <li>Introduction to Quality Function Deployment (QFD)</li>  </ul>  <h3>Summary and Next Steps</h3> <ul>  <li>IIL's Lean Six Sigma Black Belt Certification Program also prepares you to pass the IASSC Certified Black Belt Exam (optional)</li>  </ul>


                </section>

IIL Europe Ltd

Lean Six Sigma Black Belt Certification Program: In-House Training

Loughborough

<div>
		

		<div>
			
			<p>This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.</p>
			<div><i></i> 5 Days 1 Exam</div>
			<div><i></i>  £595.00 incl. VAT</div>
			
			
		</div>
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		<div>
			<p>Want to run this event in-house?  Enquire about running this event in-house </p>
			
		</div>

		<div>
			<p>Interested in attending? Have a suggestion about running this event near you?<br />Register your interest now</p>
			
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		<h3>Description</h3>
		<p></p><h3>Overview:</h3>
<p>Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organisations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organisations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.</p>
<h3>Course Objectives:</h3>
<p>In this course, you will implement AI techniques in order to solve business problems. You will:</p>
<p>• Specify a general approach to solve a given business problem that uses applied AI and ML.<br />• Collect and refine a dataset to prepare it for training and testing. <br />• Train and tune a machine learning model. <br />• Finalize a machine learning model and present the results to the appropriate audience. <br />• Build linear regression models. <br />• Build classification models. <br />• Build clustering models. <br />• Build decision trees and random forests. <br />• Build support-vector machines (SVMs). <br />• Build artificial neural networks (ANNs). <br />• Promote data privacy and ethical practices within AI and ML projects. </p>
<p> </p>
<p></p>
	
		<h3>Modules/Lessons</h3>
		<p></p><h4><span>Lesson 1: Solving Business Problems Using AI and ML</span></h4>
<p>Topic A: Identify AI and ML Solutions for Business Problems<br />Topic B: Formulate a Machine Learning Problem<br />Topic C: Select Appropriate Tools  </p>
<h4>Lesson 2: Collecting and Refining the Dataset</h4>
<p>Topic A: Collect the Dataset<br />Topic B: Analyze the Dataset to Gain Insights<br />Topic C: Use Visualizations to Analyze Data<br />Topic D: Prepare Data</p>
<h4>Lesson 3: Setting Up and Training a Model</h4>
<p>Topic A: Set Up a Machine Learning Model<br />Topic B: Train the Model </p>
<h4>Lesson 4: Finalizing a Model </h4>
<p>Topic A: Translate Results into Business Actions<br />Topic B: Incorporate a Model into a Long-Term Business Solution </p>
<h4>Lesson 5: Building Linear Regression Models</h4>
<p>Topic A: Build a Regression Model Using Linear Algebra<br />Topic B: Build a Regularized Regression Model Using Linear Algebra<br />Topic C: Build an Iterative Linear Regression Model </p>
<h4>Lesson 6: Building Classification Models</h4>
<p>Topic A: Train Binary Classification Models<br />Topic B: Train Multi-Class Classification Models<br />Topic C: Evaluate Classification Models<br />Topic D: Tune Classification Models</p>
<h4>Lesson 7: Building Clustering Models</h4>
<p>Topic A: Build k-Means Clustering Models<br />Topic B:  Build  Hierarchical  Clustering  Mode</p>
<h4>Lesson 8: Building Advanced Models</h4>
<p>Topic A: Build  Decision Tree Models<br />Topic B: Build Random Forest Model Lesson</p>
<h4>Lesson  9: Building Supports Vector Machines</h4>
<p>Topic A: Build SVM Models for Classification<br />Topic B:  Build SVM Models for Regression</p>
<h4>Lesson 10: Building Artificial Neural Networks</h4>
<p>Topic A: Build Multilayer Perceptrons (MLP)<br />Topic B: Build Convolutional Neural Networks (CNN)</p>
<h4>Lesson 11: Promoting Data Privacy and Ethical Practices</h4>
<p>Topic A: Protect Data Privacy<br />Topic B: Promote Ethical  Practices<br />Topic C: Establish Data Privacy and Ethics Policies</p>
<p></p>
	
		<h3>Prerequisites</h3>
		<p></p><p>To ensure your success in this course, you should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing.</p>
<p>You should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++.</p>
<p></p>
	
		<h3>Target Student</h3>
		<p></p><p>The skills covered in this course converge on three areas-software development, applied math and statistics, and business analysis. Target students for this course may be strong in one or two or these of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models, to business problems.  So the target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning.   A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. </p>
<p>This course is also designed to assist students in preparing for the Certified Artificial Intelligence (AI) Practitioner certification.</p>
<p></p>

Mpi Learning - Professional Learning And Development Provider

Machine Learning (ML)

NLP Practitioner

Trainers Course

Azure Machine Learning

Microsoft AI-900

AI (Artificial Intelligence)

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.

Certified Artificial Intelligence Practitioner

<h3><b>Peering at LINX training course description</b></h3>
        <p>A one or two day induction course covering the technical procedures of LINX along with correct interaction with LINX and LINX members. This course focuses on the implementation of the technologies within the LINX network. For those already competent in BGP the one day version of this course is sufficient.</p>
        
				<h5><b>What will you learn</b></h5>
				<ul>
				 
      <li>Explain the role of LINX in the Internet.</li>
					  <li>Correctly connect to LINX.</li>
                      <li>Recognise the correct procedures for peering at LINX.</li>
                      <li>Use the current Best Current Practices.</li>
                  </ul>
        <h5>
        <b>Peering at LINX training course details</b> </h5>
				<div>
					<div>
						<ul>
							<li><b> Who will benefit:</b> </li>
							
              <p>Technical staff of new LINX members.<br />
                    Technical staff of companies preparing to join LINX.<br />
                    Non technical staff may also benefit from this course.<br />
              </p>
              
							<li><b>Prerequisites:</b></li>
							
              <p>None.<br />
							(For technical staff with BGP knowledge 1 day)</p>
              
							<li><b>Duration</b></li>
							
              <p>1 to 2 days</p>
              
							
              
              
						</ul>
					</div>
				</div>
      <h3><b>Peering at LINX training course contents</b></h3>
      <ul>
<li>Introduction and review</li><p>
Networks, The Internet, IP, routers, Autonomous
systems, the structure of the Internet, the role of
Internet Exchanges. BGP and peering. LINX,
EuroIX, RIPE. What you get when you join LINX.
LINX products: Connexions, Virtual PoPs,
				Colocation resales, LINX from anywhere.</p><li>
LINX infrastructure</li><p>
The original architecture, The growth of LINX, the
current topology. LINX London locations. Dual LAN
topology. LON1, LON2. Use of MPLS to connect
London sites. Regional peering: LINX NoVa, LINX
Manchester, LINX Wales, LINX Scotland, ManxIX,
JEDIX.

</p><li>Connecting with LINX</li><p>
nnecting with LINX
Locations. 1/10/100/400G. Interface specifications.
Link aggregation. The racks and space provided,
access to the racks, connecting WAN circuits into
LINX. The connection form, How to link your ISP
with LINX, switch assignments, limits on traffic,
average measured traffic, getting statistics from
LINX, Using looking glass. Allowed traffic. Port
security
</p><li>Configuration hints</li><p>
LINX IP details, IPv4, IPv6, PTR records, Cisco
base configuration, Juniper base configuration.
		  
</p><li>Peering with other LINX members</li><p>
Preparing for peering, the peering template, setting
up the peering, RFC 2142, the RIPE database,
contacts, peeringdb.net, solving downed BGP
sessions, escalation procedures.
		  
</p><li>Peering configuration hints</li><p>
Cisco BGP configuration, Juniper BGP
configuration.
		  
</p><li>LINX additional services</li><p>
Private interconnect, LINX time service. NTP,
Strata.

</p><li>The LINX route server</li><p>
Bi lateral peering, Multi lateral peering. How the
LINX route server is configured. Use of
communities on the route server, template for
peering with the route server.

</p><li>Summary</li><p>
Getting further information, the LINX website, the
	LINX mailing lists.
	</p>

      </ul>

Peering at LINX

<div>
</div>
<section>
<div>
<h2><strong>About this Virtual Instructor Led Training (VILT)</strong><em> </em></h2>
<p>A decision to drill an exploration well with the objective to find a new oil or gas field must be based on sound assessment of the prospect risk and of the volumes. What is the chance that a well will find hydrocarbons, and how much could it be? Risk and volume assessments form the basis for decisions to drill a well or not, and as such form the link between subsurface evaluation and the business aspects of the petroleum industry.</p>
<p>This Virtual Instructor Led Training (VILT) course explains how risks and volumes can be assessed in a realistic manner, based on a sound understanding of the geological details of the prospect as well as its regional geological setting and current play understanding.</p>
<p><strong>Participants of this VILT course will receive a softcopy of <em>Risk and Volume Assessment Handbook</em> which explains the concepts that are associated with probabilistic Risk &amp; Volume (R &amp; V) Assessment and contains many practical recommendations on how to translate geological understanding into meaningful inputs for probabilistic R &amp;V assessments.</strong> The book is fully compatible with any probabilistic R &amp; V tool in the industry.</p>
</div>
</section>
<section>
<div id="accordion">
<div>
<div>

Training Objectives 

</div>
<div id="collapseOne">
<div>
<p>By the end of this VILT course, participants will be able to understand:</p>
<ul>
<li>The fundamentals of risk and volumes assessment; translating geological understanding into reasonable numbers and ranges.</li>
<li>The difference between risk and uncertainty.</li>
<li>Fundamentals of statistics; including explanation of distribution curves, understanding of expectation curves, do's and don'ts for adding risked volumes, and Bayes theorem.</li>
<li>Uncertainty of trap, reservoir, seal and charge, illustrated by examples.</li>
<li>Guidelines and exercises for estimating risks realistically and consistently.</li>
<li>Calculating volume ranges for prospects and for portfolios of prospects; how to add prospect volumes for a correct representation of prospect portfolios.</li>
<li>Incorporation of geophysical evidence (DHIs) in a realistic risk assessment.</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Target Audience 

</div>
<div id="collapseTwo">
<div>
<p>This VILT course has been designed in the first place for geoscientists working in exploration, for prospect portfolio analysts and for their direct supervisors. It will also benefit staff from disciplines working closely with exploration staff, such as reservoir engineers, petrophysicists and geophysicists.</p>
</div>
</div>
</div>
<div>
<div>

Course Level 

</div>
<div id="collapsecl">
<div>
<ul>
<li>Intermediate</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Training Methods 

</div>
<div id="collapseThree">
<div>
<p><strong>Learning, methods and tools</strong></p>
<p>The VILT course will be delivered online in <strong>5 half-day sessions comprising 4 hours per day</strong>, with 2 breaks of 10 minutes per day. It is the intention to have at least 2 smaller exercises per day. Time will be reserved for recapitulation, questions and discussions.</p>
<p>VILT will be conducted either via Zoom or Microsoft Teams. Presenting materials can easily be done on this platform. When participants need to ask a question, they can raise their hand, write notes or interrupt the Instructor by using their microphone. The presenter can switch to a screen where he/she can see all participants (also when each participant is sitting in another location e.g. at home). There is also a whiteboard functionality that can be used as one would use a flip chart. Exercises will be done on an online platform which provides each participant with a private work area that can be accessed by the Instructor to discuss the exercise in a similar manner as in a classroom course.</p>
<p>Each topic is introduced by a lecture, and learning is re-enforced by practical exercises and discussions. Handout material in electronic format will be provided.</p>
</div>
</div>
</div>
<div>
<div>

Trainer

</div>
<div id="collapseFive">
<div>
<p></p>
<p>Dr. Jan de Jager has a PhD in Geology from the University of Utrecht. He joined Shell in 1979 as an exploration geologist, and worked in several locations around the world such as Netherlands, Gabon, USA, Australia, Argentina, and Malaysia in technical and management positions. During the last 10 years of his career, he was responsible for the quality assurance of Shell's exploration prospects in many parts of the world and for upgrading and replenishing Shell's global exploration portfolio. During this period, he had also developed extensive expertise in Prospect Risk and Volume assessments for which he ran successful internal training programmes. Following his retirement from Shell in 2010, Dr Jan de Jager took on a position as part-time professor at the University of Amsterdam and also serves as a consultant exploration advisor for various E&amp;P companies.</p>
</div>
</div>
</div>
<div>
<div>

POST TRAINING COACHING SUPPORT (OPTIONAL)

</div>
<div id="collapseCS">
<div>
<p>To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.<br />
Request for further information about post training coaching support and fees applicable for this.</p>
</div>
</div>
</div>
<div>
<div>

Accreditions And Affliations

</div>
<div id="collapseSeven">
<div>
               </div>
</div>
</div>
</div>
</section>

EnergyEdge - Training for a Sustainable Energy Future

Renewable Energy

Oil & Gas

Prospect Risks & Volumes Assessment

Team of 10, pp

Normal Fee, pp

<div>
</div>
<section>
<div>
<h2><strong>About this Virtual Instructor Led Training (VILT)</strong></h2>
<p><span>This 5 half-day Virtual Instructor Led Training (VILT) course will assist energy professionals in the planning and operation of a power system from renewable energy sources. The VILT course will discuss key operating requirements for an integrated, reliable and stable power system.</span></p>
<p><span>The unique characteristics of renewable energy are discussed from a local, consumer centric and system perspective, bringing to life the ever-changing paradigm in delivering energy to customers. The course will explore the technical challenges associated with interconnecting and integrating hundreds of gigawatts of solar power onto the electricity grid in a safe and reliable way.</span></p>
<p><span>With references to international case studies, the VILT course will also demonstrate the state of the art methodologies used in forecasting solar power. The flexibility of the invertor-based resources will facilitate higher penetrations of photovoltaic, battery electricity storage systems and demand response while co-optimizing customer resources. The contribution of inverter-based generators that provides voltage support, frequency response and regulation (droop response), reactive power and power quality with a high level of accuracy and fast response will be addressed.</span></p>
<p><span>Furthermore, this VILT course will also describe how microgrids' controllers can allow for a fully automated energy management. Distributed energy resources are analyzed in detail from a technical and financial aspect and will address the best known cost based methodologies such as project financing and cost recovery.</span></p>
</div>
</section>
<section>
<div id="accordion">
<div>
<div>

Training Objectives 

</div>
<div id="collapseOne">
<div>
<p><span>Upon completion of this VILT course, participants will be able to:</span></p>
<ul>
<li><span>Learn about renewable energy resources, their applications and methods of analysis of renewable energy issues.</span></li>
<li><span>Review the operational flexibility of renewable energy at grid level, distribution network and grid edge devices.</span></li>
<li><span>Understand and analyze energy performance from main renewable energy systems.</span></li>
<li><span>Get equipped on the insights into forecasting models for solar energy.</span></li>
<li><span>Predict solar generation from weather forecasts using machine learning.</span></li>
<li><span>Explore operational aspects of a complex power system with variability from both the supply &amp; demand sides.</span></li>
<li><span>Manage the impact of the design of a Power Purchase Agreement (PPA) on the power system operation.</span></li>
</ul>
</div>
</div>
</div>
<div>
<div>

Target Audience 

</div>
<div id="collapseTwo">
<div>
<p><span>Engineers, planners and operations professionals from the following organizations:</span></p>
<ul>
<li><span>Energy aggregators who would like to understand the system operations of renewable energy power plants</span></li>
<li><span>Renewable energy power system operator</span></li>
<li><span>Energy regulatory agencies who aim to derive strategies and plans based on the feedback obtained from the power system operations</span></li>
</ul>
</div>
</div>
</div>
<div>
<div>

Course Level 

</div>
<div id="collapsecl">
<div>
<ul>
<li>Basic or Foundation</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Training Methods 

</div>
<div id="collapseThree">
<div>
<p><span>The VILT course will be delivered online in 5 half-day sessions comprising 4 hours per day, including time for lectures, discussion, quizzes and short classroom exercises.</span></p>
<p>Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total).</p>
</div>
</div>
</div>
<div>
<div>

Trainer

</div>
<div id="collapseFive">
<div>
<p><strong>Your first expert course leader</strong> is a Utility Executive with extensive global experience in power system operation and planning, energy markets, enterprise risk and regulatory oversight. She consults on energy markets integrating renewable resources from planning to operation. She led complex projects in operations and conducted long term planning studies to support planning and operational reliability standards. Specializing in Smart Grids, Operational flexibilities, Renewable generation, Reliability, Financial Engineering, Energy Markets and Power System Integration, she was recently engaged by the Inter-American Development Bank/MHI in Guyana. She was the Operations Expert in the regulatory assessment in Oman. She is a registered member of the Professional Engineers of Ontario, Canada. She is also a contributing member to the IEEE Standards Association, WG Blockchain P2418.5.</p>
<p>With over 25 years with Ontario Power Generation (Revenue $1.2 Billion CAD, I/S 16 GW), she served as Canadian representative in CIGRE, committee member in NSERC (Natural Sciences and Engineering Research Council of Canada), and Senior Member IEEE and Elsevier since the 90ties. Our key expert chaired international conferences, lectured on several continents, published a book on Reliability and Security of Nuclear Power Plants, contributed to IEEE and PMAPS and published in the Ontario Journal for Public Policy, Canada. She delivered seminars organized by the Power Engineering Society, IEEE plus seminars to power companies worldwide, including Oman, Thailand, Saudi Arabia, Malaysia, Indonesia, Portugal, South Africa, Japan, Romania, and Guyana.</p>
<p><strong>Your second expert course leader </strong>is the co-founder and Director of Research at Xesto Inc. Xesto is a spatial computing AI startup based in Toronto, Canada and it has been voted as Toronto's Best Tech Startup 2019 and was named one of the top 10 'Canadian AI Startups to Watch' as well as one of 6th International finalists for the VW Siemens Startup Challenge, resulting in a partnership. His latest app Xesto-Fit demonstrates how advanced AI and machine learning is applied to the e-commerce industry, as a result of which Xesto has been recently featured in TechCrunch.</p>
<p>He specializes in both applied and theoretical machine learning and has extensive experience in both industrial and academic research. He is specialized in Artificial Intelligence with multiple industrial applications. At Xesto, he leads projects that focus on applying cutting edge research at the intersection of spatial analysis, differential geometry, optimization of deep neural networks, and statistics to build scalable rigorous and real time performing systems that will change the way humans interact with technology. In addition, he is a Ph.D candidate in the Mathematics department at UofT, focusing on applied mathematics. His academic research interests are in applying advanced mathematical methods to the computational and statistical sciences. He earned a Bachelor's and MSc in Mathematics, both at the University of Toronto.</p>
<p>Having presented at research seminars as well as instructing engineers on various levels, he has the ability to distill advanced theoretical concept to diverse audiences on all levels. In addition to research, our key expert is also an avid traveler and plays the violin.</p>
</div>
</div>
</div>
<div>
<div>

POST TRAINING COACHING SUPPORT (OPTIONAL)

</div>
<div id="collapseCS">
<div>
<p>To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.<br />
Request for further information about post training coaching support and fees applicable for this.</p>
</div>
</div>
</div>
<div>
<div>

Accreditions And Affliations

</div>
<div id="collapseSeven">
<div>
               </div>
</div>
</div>
</div>
</section>

Large Scale Solar & Energy Storage - System Operations

<div>
</div>
<section>
<div>
<h2><strong>About this Virtual Instructor Led Training (VILT)</strong></h2>
<p>This Virtual Instructor Led Training (VILT) course presents advanced methodologies that implement demand response and energy conservation programs in light of the integration of new technologies, regulatory changes and the accelerated penetration of renewable energy resources.</p>
<p>This VILT course provides examples and case studies from North American and European jurisdictions covering the operational flexibilities on the demand side including requirements for new building codes to achieve zero net energy. The course describes a public agency's goals and objectives for conserving and otherwise reducing energy consumption and managing its demand for energy. This course presents the demand response implemented for economics and system security such as system balancing and relieving transmission congestion, or for system adequacy. The course also presents the principal attributes of conservation programs and the associated success criteria. In a system with increased penetration of renewable resources, demand response provides flexibility to system operators, helping them to maintain the reliability and the security of supply.</p>
<p>Demand response is presented as a competitive alternative to additional power sources, enhancing competition and liquidity in electricity markets. The unique characteristics are discussed from a local, consumer centric and also from a system perspective bringing to life the ever changing paradigm for delivery energy to customers. Interoperability aspects and standards are discussed, as well as the consumer centric paradigm of Transactive Energy with IOT enabled flexibilities at system level, distribution networks and microgrids.</p>
<p>The VILT course introduces the blockchain as a new line of defense against cyber threats and its increasing application in P2P transactions and renewable certificates. Our trainer's industry experience spans three decades with one of the largest Canadian utilities where she led or contributed to large operational studies and energy policies and decades of work with IEEE, NSERC and CIGRE. Our key expert also approaches to the cross sectional, interdisciplinary state of the art methodologies brings real life experience of recent industry developments.</p>
</div>
</section>
<section>
<div id="accordion">
<div>
<div>

Training Objectives 

</div>
<div id="collapseOne">
<div>
<ul>
<li>Innovative Digital Technologies</li>
<li>How systems Facilitate Operational Flexibility on the Demand Side</li>
<li>The Ecosystem of Demand Side Management Programs</li>
<li>Advanced Machine Learning techniques with examples from CAISO</li>
<li>Regulatory Policy Context and how to reduce regulatory barriers</li>
<li>Industry Examples from NERC and ENTSO</li>
<li>Relevant Industry standards: IEEE and IEC</li>
<li>Manage Congestion with Distributed Operational Flexibilities: Grid to Distribution Controls; examples from NERC (NA) and ENTSO (Europe)</li>
<li>Grid solutions with IEC 61850 communication protocols</li>
<li>Decentralized grid controls</li>
<li>The New Grid with accelerated V2G and Microgrids</li>
<li>How DSM is and will be applied in Your System: Examples and discussions</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Target Audience 

</div>
<div id="collapseTwo">
<div>
<ul>
<li>Regulators and government agencies advising on public energy conservation programs</li>
<li>All professionals interested in expanding their expertise, or advancing their career, or take on management and leadership roles in the rapidly evolving energy sector</li>
<li>Energy professionals implementing demand side management, particularly in power systems with increased renewable penetration, to allow the much needed operational flexibility paramount to maintaining the reliability and stability of the power system and in the same time offering all classes of customers flexible and economical choices</li>
<li>Any utility professional interested in understanding the new developments in the power industry</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Course Level 

</div>
<div id="collapsecl">
<div>
<ul>
<li>Basic or Foundation</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Training Methods 

</div>
<div id="collapseThree">
<div>
<p>The VILT course will be delivered online in <strong>5 half-day sessions comprising 4 hours per day, with 2 x 10 minutes break per day</strong>, including time for lectures, discussion, quizzes and short classroom exercises.</p>
<p>Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total).</p>
</div>
</div>
</div>
<div>
<div>

Trainer

</div>
<div id="collapseFive">
<div>
<p><strong>Your first expert course leader</strong> is a Utility Executive with extensive global experience in power system operation and planning, energy markets, enterprise risk and regulatory oversight. She consults on energy markets integrating renewable resources from planning to operation. She led complex projects in operations and conducted long term planning studies to support planning and operational reliability standards. Specializing in Smart Grids, Operational flexibilities, Renewable generation, Reliability, Financial Engineering, Energy Markets and Power System Integration, she was recently engaged by the Inter-American Development Bank/MHI in Guyana. She was the Operations Expert in the regulatory assessment in Oman. She is a registered member of the Professional Engineers of Ontario, Canada. She is also a contributing member to the IEEE Standards Association, WG Blockchain P2418.5.</p>
<p>With over 25 years with Ontario Power Generation (Revenue $1.2 Billion CAD, I/S 16 GW), she served as Canadian representative in CIGRE, committee member in NSERC (Natural Sciences and Engineering Research Council of Canada), and Senior Member IEEE and Elsevier since the 90ties. Our key expert chaired international conferences, lectured on several continents, published a book on Reliability and Security of Nuclear Power Plants, contributed to IEEE and PMAPS and published in the Ontario Journal for Public Policy, Canada. She delivered seminars organized by the Power Engineering Society, IEEE plus seminars to power companies worldwide, including Oman, Thailand, Saudi Arabia, Malaysia, Indonesia, Portugal, South Africa, Japan, Romania, and Guyana.</p>
<p><strong>Your second expert course leader </strong>is the co-founder and Director of Research at Xesto Inc. Xesto is a spatial computing AI startup based in Toronto, Canada and it has been voted as Toronto's Best Tech Startup 2019 and was named one of the top 10 'Canadian AI Startups to Watch' as well as one of 6th International finalists for the VW Siemens Startup Challenge, resulting in a partnership. His latest app Xesto-Fit demonstrates how advanced AI and machine learning is applied to the e-commerce industry, as a result of which Xesto has been recently featured in TechCrunch.</p>
<p>He specializes in both applied and theoretical machine learning and has extensive experience in both industrial and academic research. He is specialized in Artificial Intelligence with multiple industrial applications. At Xesto, he leads projects that focus on applying cutting edge research at the intersection of spatial analysis, differential geometry, optimization of deep neural networks, and statistics to build scalable rigorous and real time performing systems that will change the way humans interact with technology. In addition, he is a Ph.D candidate in the Mathematics department at UofT, focusing on applied mathematics. His academic research interests are in applying advanced mathematical methods to the computational and statistical sciences. He earned a Bachelor's and MSc in Mathematics, both at the University of Toronto.</p>
<p>Having presented at research seminars as well as instructing engineers on various levels, he has the ability to distill advanced theoretical concept to diverse audiences on all levels. In addition to research, our key expert is also an avid traveler and plays the violin.</p>
</div>
</div>
</div>
<div>
<div>

POST TRAINING COACHING SUPPORT (OPTIONAL)

</div>
<div id="collapseCS">
<div>
<p>To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.<br />
Request for further information about post training coaching support and fees applicable for this.</p>
</div>
</div>
</div>
<div>
<div>

Accreditions And Affliations

</div>
<div id="collapseSeven">
<div>
               </div>
</div>
</div>
</div>
</section>

Demand Side Management - Integration of New Technologies, Regulatory Changes & Renewable Energy Resources

<div>
		

		<div>
			
			<p>This course will enable you to bring value to the business by putting data science concepts into practice. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations.</p>
			<div><i></i> 5 Days - 1 Exam</div>
			<div><i></i>  £595.00 incl. VAT</div>
			
			
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		<h3>Description</h3>
		<p></p><p>For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice. This course includes hands-on activities for each topic area.</p>
<h3>Course Objectives: </h3>
<p>In this course, you will implement data science techniques in order to address business issues. You will:</p>
<p>• Use data science principles to address business issues. <br />• Apply the extract, transform, and load (ETL) process to prepare datasets. <br />• Use multiple techniques to analyze data and extract valuable insights. <br />• Design a machine learning approach to address business issues. <br />• Train, tune, and evaluate classification models. <br />• Train, tune, and evaluate regression and forecasting models. <br />• Train, tune, and evaluate clustering models. <br />• Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance.</p>
<p></p>
	
		<h3>Target Student</h3>
		<p></p><p>This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming.  However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. </p>
<p>Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business.  This course is also designed to assist students in preparing for the Certified Data Science Practitioner certification.</p>
<p></p>
	
		<h3>Prerequisites</h3>
		<p></p><p>To ensure your success in this course, you should have at least a high-level understanding of fundamental data science concepts, including, but not limited to: types of data, data science roles, the overall data science lifecycle, and the benefits and challenges of data science.</p>
<p>You should have also have experience with high-level programming languages like Python. Being comfortable using fundamental Python data science libraries like NumPy and pandas is highly recommended.</p>
<p>In addition to programming, you should also have experience working with databases, including querying languages like SQL. </p>
<p></p>
	
		<h3>Modules/Lessons</h3>
		<p></p><h4>Lesson 1: Addressing Business Issues with Data Science</h4>
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<p>Topic A: Initiate a Data Science Project<br />Topic B: Formulate a Data Science Problem</p>
<h4>Lesson 2: Extracting, Transforming, and Loading Data</h4>
<p>Topic A: Extract Data <br />Topic B: Transform Data <br />Topic C: Load Data</p>
<h4>Lesson 3: Analyzing Data</h4>
<p>Topic A: Examine Data<br />Topic B: Explore the Underlying Distribution of Data <br />Topic C: Use Visualizations to Analyze Data<br />Topic D: Preprocess Data</p>
<h4>Lesson 4: Designing a Machine Learning Approach</h4>
<p>Topic A: Identify Machine Learning Concepts<br />Topic B: Test a Hypothesis</p>
</div>
<h4>Lesson 5: Developing Classification Models</h4>
<p>Topic A: Train and Tune Classification Models <br />Topic B: Evaluate Classification Models</p>
<h4>Lesson 6: Developing Regression Models</h4>
<p>Topic A: Train and Tune Regression Models <br />Topic B: Evaluate Regression Models</p>
<h4>Lesson 7: Developing Clustering Models</h4>
<p>Topic A: Train and Tune Clustering Models <br />Topic B: Evaluate Clustering Models</p>
<h4>Lesson 8: Finalizing a Data Science Project</h4>
<p>Topic A: Communicate Results to Stakeholders <br />Topic B: Demonstrate Models in a Web App <br />Topic C: Implement and Test Production Pipelines</p>
<p></p>

This course will enable you to bring value to the business by putting data science concepts into practice. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations.

Certified Data Science Practitioner

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="color: rgb(44, 130, 201); box-sizing: border-box;" fr-original-style="color: rgb(44, 130, 201);">Mental Health First Aid Refresher 4-hour training sessions</span></strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Can you remember ALGEE?&nbsp;</strong>Mental Health Action Plan.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Empower Mental Health First Aiders and Champions to maintain their skills with regular Refresher training. Just like physical first aid, we recommend that you refresh your skills every two to three years. (Certificate expires after 3 years). &nbsp;If it&#39;s time to update your skills, book onto an MHFA Refresher course now and feel confident that you are performing your vital role safely.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(44, 130, 201); box-sizing: border-box;" fr-original-style="color: rgb(44, 130, 201);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Revisit the key areas from the full course. Keep up to date with the latest statistics and information.</strong></span></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">If you are a Mental Health First Aider or MHFA Champion you have skills for life that support you and the people around you. &nbsp;Mental Health First Aid training can be kept up to date through this four hour MHFA Refresher course, which will empower you to: &nbsp;Update your knowledge of mental health and what influences it Update your awareness of current mental health supports available Practise applying the Mental Health First Aid Action Plan. Learning takes place through a mix of presentations, group discussions and workshop activities. We limit numbers to 20 people per course so that the instructor can keep people safe and supported, to get the most from their learning experience with us.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This course is for those that have completed the MHFA England Mental Health First Aid or Champion course.&nbsp;</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><u fr-original-style="" style="box-sizing: border-box;"><a href="https://www.mindmapswellbeing.com/faq-s/" target="_blank" rel="noopener noreferrer" fr-original-style="" style="user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;"><span style="color: rgb(44, 130, 201); box-sizing: border-box;" fr-original-style="color: rgb(44, 130, 201);">FAQs</span></a></u></p>

Mental Health First Aid Refresher 4-hour training sessions
Can you remember ALGEE? Mental Health Action Plan.
Empower Mental Health First Aiders and Champions to maintain their skills with regular Refresher training. Just like physical first aid, we recommend that you refresh your skills every two to three years. (Certificate expires after 3 years). If it's time to update your skills, book onto an MHFA Refresher course now and feel confident that you are performing your vital role safely.
Revisit the key areas from the full course. Keep up to date with the latest statistics and information.
If you are a Mental Health First Aider or MHFA Champion you have skills for life that support you and the people around you. Mental Health First Aid training can be kept up to date through this four hour MHFA Refresher course, which will empower you to: Update your knowledge of mental health and what influences it Update your awareness of current mental health supports available Practise applying the Mental Health First Aid Action Plan. Learning takes place through a mix of presentations, group discussions and workshop activities. We limit numbers to 20 people per course so that the instructor can keep people safe and supported, to get the most from their learning experience with us.
This course is for those that have completed the MHFA England Mental Health First Aid or Champion course. 
FAQs

Mindmaps Wellbeing

Mental Health Awareness

Mental Health First Aid

Approved Training Organisation (ATO)

Health & Wellbeing

Occupational Health & Wellbeing

Mental Health

Accredited Mental Health First Aid

Wellbeing

Mental Resilience

Mental Health At Work

Mental Health Nurse

Empower Mental Health First Aiders and Champions to maintain their skills with regular Refresher training. Just like physical first aid, we recommend that you refresh your skills every two to three years. (Certificate expires after 3 years).  If it's time to update your skills, book onto an MHFA Refresher course now and feel confident that you are performing your vital role safely.

MHFA England Refresher 

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>The Microsoft Security Operations Analyst collaborates with organizational stakeholders to secure information technology systems for the organization. Their goal is to reduce organizational risk by rapidly remediating active attacks in the environment, advising on improvements to threat protection practices, and referring violations of organizational policies to appropriate stakeholders. Responsibilities include threat management, monitoring, and response by using a variety of security solutions across their environment. The role primarily investigates, responds to, and hunts for threats using Microsoft Sentinel, Microsoft Defender for Cloud, Microsoft 365 Defender, and third-party security products. Since the Security Operations Analyst consumes the operational output of these tools, they are also a critical stakeholder in the configuration and deployment of these technologies.</p><p></p><p></p><p>Learn how to investigate, respond to, and hunt for threats using Microsoft Sentinel, Microsoft Defender for Cloud, and Microsoft 365 Defender. In this course you will learn how to mitigate cyberthreats using these technologies. Specifically, you will configure and use Microsoft Sentinel as well as utilize Kusto Query Language (KQL) to perform detection, analysis, and reporting. The course was designed for people who work in a Security Operations job role and helps learners prepare for the exam SC-200: Microsoft Security Operations Analyst.</p><p></p>Prerequisites<p></p><li>Basic understanding of Microsoft 365</li>  <li>Fundamental understanding of Microsoft security, compliance, and identity products</li>   <li> Intermediate understanding of Windows 10</li>  <li> Familiarity with Azure services, specifically Azure SQL Database and Azure Storage</li>   <li> Familiarity with Azure virtual machines and virtual networking</li>   <li>Basic understanding of scripting concepts.</li><p></p></div><div id="course-pills-2"><h3>1 - Introduction to Microsoft 365 threat protection</h3> <ul> <li>Explore Extended Detection &amp; Response (XDR) response use cases</li> <li>Understand Microsoft Defender XDR in a Security Operations Center (SOC)</li> <li>Explore Microsoft Security Graph</li> <li>Investigate security incidents in Microsoft Defender XDR</li> </ul> <h3>2 - Mitigate incidents using Microsoft 365 Defender</h3> <ul> <li>Use the Microsoft Defender portal</li> <li>Manage incidents</li> <li>Investigate incidents</li> <li>Manage and investigate alerts</li> <li>Manage automated investigations</li> <li>Use the action center</li> <li>Explore advanced hunting</li> <li>Investigate Microsoft Entra sign-in logs</li> <li>Understand Microsoft Secure Score</li> <li>Analyze threat analytics</li> <li>Analyze reports</li> <li>Configure the Microsoft Defender portal</li> </ul> <h3>3 - Protect your identities with Microsoft Entra ID Protection</h3> <ul> <li>Microsoft Entra ID Protection overview</li> <li>Detect risks with Microsoft Entra ID Protection policies</li> <li>Investigate and remediate risks detected by Microsoft Entra ID Protection</li> </ul> <h3>4 - Remediate risks with Microsoft Defender for Office 365</h3> <ul> <li>Automate, investigate, and remediate</li> <li>Configure, protect, and detect</li> <li>Simulate attacks</li> </ul> <h3>5 - Safeguard your environment with Microsoft Defender for Identity</h3> <ul> <li>Configure Microsoft Defender for Identity sensors</li> <li>Review compromised accounts or data</li> <li>Integrate with other Microsoft tools</li> </ul> <h3>6 - Secure your cloud apps and services with Microsoft Defender for Cloud Apps</h3> <ul> <li>Understand the Defender for Cloud Apps Framework</li> <li>Explore your cloud apps with Cloud Discovery</li> <li>Protect your data and apps with Conditional Access App Control</li> <li>Walk through discovery and access control with Microsoft Defender for Cloud Apps</li> <li>Classify and protect sensitive information</li> <li>Detect Threats</li> </ul> <h3>7 - Respond to data loss prevention alerts using Microsoft 365</h3> <ul> <li>Describe data loss prevention alerts</li> <li>Investigate data loss prevention alerts in Microsoft Purview</li> <li>Investigate data loss prevention alerts in Microsoft Defender for Cloud Apps</li> </ul> <h3>8 - Manage insider risk in Microsoft Purview</h3> <ul> <li>Insider risk management overview</li> <li>Create and manage insider risk policies</li> <li>Investigate insider risk alerts</li> <li>Take action on insider risk alerts through cases</li> <li>Manage insider risk management forensic evidence</li> <li>Create insider risk management notice templates</li> </ul> <h3>9 - Investigate threats by using audit features in Microsoft Defender XDR and Microsoft Purview Standard</h3> <ul> <li>Explore Microsoft Purview Audit solutions</li> <li>Implement Microsoft Purview Audit (Standard)</li> <li>Start recording activity in the Unified Audit Log</li> <li>Search the Unified Audit Log (UAL)</li> <li>Export, configure, and view audit log records</li> <li>Use audit log searching to investigate common support issues</li> </ul> <h3>10 - Investigate threats using audit in Microsoft Defender XDR and Microsoft Purview (Premium)</h3> <ul> <li>Explore Microsoft Purview Audit (Premium)</li> <li>Implement Microsoft Purview Audit (Premium)</li> <li>Manage audit log retention policies</li> <li>Investigate compromised email accounts using Purview Audit (Premium)</li> </ul> <h3>11 - Investigate threats with Content search in Microsoft Purview</h3> <ul> <li>Explore Microsoft Purview eDiscovery solutions</li> <li>Create a content search</li> <li>View the search results and statistics</li> <li>Export the search results and search report</li> <li>Configure search permissions filtering</li> <li>Search for and delete email messages</li> </ul> <h3>12 - Protect against threats with Microsoft Defender for Endpoint</h3> <ul> <li>Practice security administration</li> <li>Hunt threats within your network</li> </ul> <h3>13 - Deploy the Microsoft Defender for Endpoint environment</h3> <ul> <li>Create your environment</li> <li>Understand operating systems compatibility and features</li> <li>Onboard devices</li> <li>Manage access</li> <li>Create and manage roles for role-based access control</li> <li>Configure device groups</li> <li>Configure environment advanced features</li> </ul> <h3>14 - Implement Windows security enhancements with Microsoft Defender for Endpoint</h3> <ul> <li>Understand attack surface reduction</li> <li>Enable attack surface reduction rules</li> </ul> <h3>15 - Perform device investigations in Microsoft Defender for Endpoint</h3> <ul> <li>Use the device inventory list</li> <li>Investigate the device</li> <li>Use behavioral blocking</li> <li>Detect devices with device discovery</li> </ul> <h3>16 - Perform actions on a device using Microsoft Defender for Endpoint</h3> <ul> <li>Explain device actions</li> <li>Run Microsoft Defender antivirus scan on devices</li> <li>Collect investigation package from devices</li> <li>Initiate live response session</li> </ul> <h3>17 - Perform evidence and entities investigations using Microsoft Defender for Endpoint</h3> <ul> <li>Investigate a file</li> <li>Investigate a user account</li> <li>Investigate an IP address</li> <li>Investigate a domain</li> </ul> <h3>18 - Configure and manage automation using Microsoft Defender for Endpoint</h3> <ul> <li>Configure advanced features</li> <li>Manage automation upload and folder settings</li> <li>Configure automated investigation and remediation capabilities</li> <li>Block at risk devices</li> </ul> <h3>19 - Configure for alerts and detections in Microsoft Defender for Endpoint</h3> <ul> <li>Configure advanced features</li> <li>Configure alert notifications</li> <li>Manage alert suppression</li> <li>Manage indicators</li> </ul> <h3>20 - Utilize Vulnerability Management in Microsoft Defender for Endpoint</h3> <ul> <li>Understand vulnerability management</li> <li>Explore vulnerabilities on your devices</li> <li>Manage remediation</li> </ul> <h3>21 - Plan for cloud workload protections using Microsoft Defender for Cloud</h3> <ul> <li>Explain Microsoft Defender for Cloud</li> <li>Describe Microsoft Defender for Cloud workload protections</li> <li>Enable Microsoft Defender for Cloud</li> </ul> <h3>22 - Connect Azure assets to Microsoft Defender for Cloud</h3> <ul> <li>Explore and manage your resources with asset inventory</li> <li>Configure auto provisioning</li> <li>Manual log analytics agent provisioning</li> </ul> <h3>23 - Connect non-Azure resources to Microsoft Defender for Cloud</h3> <ul> <li>Protect non-Azure resources</li> <li>Connect non-Azure machines</li> <li>Connect your AWS accounts</li> <li>Connect your GCP accounts</li> </ul> <h3>24 - Manage your cloud security posture management?</h3> <ul> <li>Explore Secure Score</li> <li>Explore Recommendations</li> <li>Measure and enforce regulatory compliance</li> <li>Understand Workbooks</li> </ul> <h3>25 - Explain cloud workload protections in Microsoft Defender for Cloud</h3> <ul> <li>Understand Microsoft Defender for servers</li> <li>Understand Microsoft Defender for App Service</li> <li>Understand Microsoft Defender for Storage</li> <li>Understand Microsoft Defender for SQL</li> <li>Understand Microsoft Defender for open-source databases</li> <li>Understand Microsoft Defender for Key Vault</li> <li>Understand Microsoft Defender for Resource Manager</li> <li>Understand Microsoft Defender for DNS</li> <li>Understand Microsoft Defender for Containers</li> <li>Understand Microsoft Defender additional protections</li> </ul> <h3>26 - Remediate security alerts using Microsoft Defender for Cloud</h3> <ul> <li>Understand security alerts</li> <li>Remediate alerts and automate responses</li> <li>Suppress alerts from Defender for Cloud</li> <li>Generate threat intelligence reports</li> <li>Respond to alerts from Azure resources</li> </ul> <h3>27 - Construct KQL statements for Microsoft Sentinel</h3> <ul> <li>Understand the Kusto Query Language statement structure</li> <li>Use the search operator</li> <li>Use the where operator</li> <li>Use the let statement</li> <li>Use the extend operator</li> <li>Use the order by operator</li> <li>Use the project operators</li> </ul> <h3>28 - Analyze query results using KQL</h3> <ul> <li>Use the summarize operator</li> <li>Use the summarize operator to filter results</li> <li>Use the summarize operator to prepare data</li> <li>Use the render operator to create visualizations</li> </ul> <h3>29 - Build multi-table statements using KQL</h3> <ul> <li>Use the union operator</li> <li>Use the join operator</li> </ul> <h3>30 - Work with data in Microsoft Sentinel using Kusto Query Language</h3> <ul> <li>Extract data from unstructured string fields</li> <li>Extract data from structured string data</li> <li>Integrate external data</li> <li>Create parsers with functions</li> </ul> <h3>31 - Introduction to Microsoft Sentinel</h3> <ul> <li>What is Microsoft Sentinel?</li> <li>How Microsoft Sentinel works</li> <li>When to use Microsoft Sentinel</li> </ul> <h3>32 - Create and manage Microsoft Sentinel workspaces</h3> <ul> <li>Plan for the Microsoft Sentinel workspace</li> <li>Create a Microsoft Sentinel workspace</li> <li>Manage workspaces across tenants using Azure Lighthouse</li> <li>Understand Microsoft Sentinel permissions and roles</li> <li>Manage Microsoft Sentinel settings</li> <li>Configure logs</li> </ul> <h3>33 - Query logs in Microsoft Sentinel</h3> <ul> <li>Query logs in the logs page</li> <li>Understand Microsoft Sentinel tables</li> <li>Understand common tables</li> <li>Understand Microsoft Defender XDR tables</li> </ul> <h3>34 - Use watchlists in Microsoft Sentinel</h3> <ul> <li>Plan for watchlists</li> <li>Create a watchlist</li> <li>Manage watchlists</li> </ul> <h3>35 - Utilize threat intelligence in Microsoft Sentinel</h3> <ul> <li>Define threat intelligence</li> <li>Manage your threat indicators</li> <li>View your threat indicators with KQL</li> </ul> <h3>36 - Connect data to Microsoft Sentinel using data connectors</h3> <ul> <li>Ingest log data with data connectors</li> <li>Understand data connector providers</li> <li>View connected hosts</li> </ul> <h3>37 - Connect Microsoft services to Microsoft Sentinel</h3> <ul> <li>Plan for Microsoft services connectors</li> <li>Connect the Microsoft Office 365 connector</li> <li>Connect the Microsoft Entra connector</li> <li>Connect the Microsoft Entra ID Protection connector</li> <li>Connect the Azure Activity connector</li> </ul> <h3>38 - Connect Microsoft Defender XDR to Microsoft Sentinel</h3> <ul> <li>Plan for Microsoft Defender XDR connectors</li> <li>Connect the Microsoft Defender XDR connector</li> <li>Connect Microsoft Defender for Cloud connector</li> <li>Connect Microsoft Defender for IoT</li> <li>Connect Microsoft Defender legacy connectors</li> </ul> <h3>39 - Connect Windows hosts to Microsoft Sentinel</h3> <ul> <li>Plan for Windows hosts security events connector</li> <li>Connect using the Windows Security Events via AMA Connector</li> <li>Connect using the Security Events via Legacy Agent Connector</li> <li>Collect Sysmon event logs</li> </ul> <h3>40 - Connect Common Event Format logs to Microsoft Sentinel</h3> <ul> <li>Plan for Common Event Format connector</li> <li>Connect your external solution using the Common Event Format connector</li> </ul> <h3>41 - Connect syslog data sources to Microsoft Sentinel</h3> <ul> <li>Plan for syslog data collection</li> <li>Collect data from Linux-based sources using syslog</li> <li>Configure the Data Collection Rule for Syslog Data Sources</li> <li>Parse syslog data with KQL</li> </ul> <h3>42 - Connect threat indicators to Microsoft Sentinel</h3> <ul> <li>Plan for threat intelligence connectors</li> <li>Connect the threat intelligence TAXII connector</li> <li>Connect the threat intelligence platforms connector</li> <li>View your threat indicators with KQL</li> </ul> <h3>43 - Threat detection with Microsoft Sentinel analytics</h3> <ul> <li>What is Microsoft Sentinel Analytics?</li> <li>Types of analytics rules</li> <li>Create an analytics rule from templates</li> <li>Create an analytics rule from wizard</li> <li>Manage analytics rules</li> </ul> <h3>44 - Automation in Microsoft Sentinel</h3> <ul> <li>Understand automation options</li> <li>Create automation rules</li> </ul> <h3>45 - Threat response with Microsoft Sentinel playbooks</h3> <ul> <li>What are Microsoft Sentinel playbooks?</li> <li>Trigger a playbook in real-time</li> <li>Run playbooks on demand</li> </ul> <h3>46 - Security incident management in Microsoft Sentinel</h3> <ul> <li>Understand incidents</li> <li>Incident evidence and entities</li> <li>Incident management</li> </ul> <h3>47 - Identify threats with Behavioral Analytics</h3> <ul> <li>Understand behavioral analytics</li> <li>Explore entities</li> <li>Display entity behavior information</li> <li>Use Anomaly detection analytical rule templates</li> </ul> <h3>48 - Data normalization in Microsoft Sentinel</h3> <ul> <li>Understand data normalization</li> <li>Use ASIM Parsers</li> <li>Understand parameterized KQL functions</li> <li>Create an ASIM Parser</li> <li>Configure Azure Monitor Data Collection Rules</li> </ul> <h3>49 - Query, visualize, and monitor data in Microsoft Sentinel</h3> <ul> <li>Monitor and visualize data</li> <li>Query data using Kusto Query Language</li> <li>Use default Microsoft Sentinel Workbooks</li> <li>Create a new Microsoft Sentinel Workbook</li> </ul> <h3>50 - Manage content in Microsoft Sentinel</h3> <ul> <li>Use solutions from the content hub</li> <li>Use repositories for deployment</li> </ul> <h3>51 - Explain threat hunting concepts in Microsoft Sentinel</h3> <ul> <li>Understand cybersecurity threat hunts</li> <li>Develop a hypothesis</li> <li>Explore MITRE ATT&amp;CK</li> </ul> <h3>52 - Threat hunting with Microsoft Sentinel</h3> <ul> <li>Explore creation and management of threat-hunting queries</li> <li>Save key findings with bookmarks</li> <li>Observe threats over time with livestream</li> </ul> <h3>53 - Use Search jobs in Microsoft Sentinel</h3> <ul> <li>Hunt with a Search Job</li> <li>Restore historical data</li> </ul> <h3>54 - Hunt for threats using notebooks in Microsoft Sentinel</h3> <ul> <li>Access Azure Sentinel data with external tools</li> <li>Hunt with notebooks</li> <li>Create a notebook</li> <li>Explore notebook code</li> </ul></div>

Nexus Human

Microsoft Certification

Microsoft AZ-900

Microsoft AZ-104

Microsoft SC-200

Microsoft AZ-303

Microsoft AZ-700

SC-200T00 Microsoft Security Operations Analyst

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					<div><p><span>This course covers everything you must know to stand against the tough competition. The future is truly yours to seize with this Statistics. Enrol today and complete the course to achieve a certificate that can change your career forever.</span></p></div>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2>Details
</h2>		</div>
				</div>
				<div>
				<div>
			<h4>Perks of Learning with IOMH</h4>  		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
							<i></i>						</span>
										<span>One-to-one support from a dedicated tutor throughout your course.</span>
									</li>
								<li>
											<span>
							<i></i>						</span>
										<span>Study online - whenever and wherever you want.</span>
									</li>
								<li>
											<span>
							<i></i>						</span>
										<span>Instant Digital/ PDF certificate</span>
									</li>
								<li>
											<span>
							<i></i>						</span>
										<span>100% money back guarantee</span>
									</li>
								<li>
											<span>
							<i></i>						</span>
										<span>12 months access</span>
									</li>
						</ul>
				</div>
				</div>
				<div>
				<div>
					<div><p><span>This course covers everything you must know to stand against the tough competition. The future is truly yours to seize with this Statistics. Enrol today and complete the course to achieve a certificate that can change your career forever.</span></p></div>
				</div>
				</div>
				<div>
				<div>
			<h4>Process of Evaluation</h4>  		</div>
				</div>
				<div>
				<div>
					<div><p>After studying the course, your skills and knowledge will be tested with an MCQ exam or assignment. You have to get a score of 60% to pass the test and get your certificate.</p></div>
				</div>
				</div>
				<div>
				<div>
			<h4>Certificate of Achievement</h4>  		</div>
				</div>
				<div>
				<div>
					<div><p><span>After completing the </span><b>Statistics course</b><span>, you will receive your</span><b> CPD-accredited Digital/PDF Certificate for £5.99. </b><span>To get the </span><b>hardcopy certificate for £12.99, </b><span>you must also pay the shipping charge of just</span><b> £3.99 (UK) and £10.99 (International).</b></p></div>
				</div>
				</div>
				<div>
				<div>
			<h4>Who Is This Course for?</h4>  		</div>
				</div>
				<div>
				<div>
					<div><p><span>This Statistics is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand. </span></p><p><span>On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level. </span></p><p><span>This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education.</span></p></div>
				</div>
				</div>
				<div>
				<div>
			<h4>Requirements</h4>  		</div>
				</div>
				<div>
				<div>
					<div><p><span>There is no prerequisite to enrol in this course. You don't need any educational qualification or experience to enrol in the Statistics course.</span></p><p><span>Do note: you must be at least 16 years old to enrol.</span></p><p><span>Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course.</span></p></div>
				</div>
				</div>
				<div>
				<div>
			<h4>Career Path</h4>  		</div>
				</div>
				<div>
				<div>
					<div><p><span>The certification and skills you get from this </span><span>Statistics Course</span><span> can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors.</span></p></div>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
						</div>
			</div>
		</div>
								</div>
						<div id="menu1">
							<div>
											<div>
				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table>				<tbody><tr>
					<td colspan="4">Module 01: Introduction to Statistics </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Statistics</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 02: Measuring Central Tendency</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Measuring Central Tendency</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 03: Measures of Dispersion</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Measures of Dispersion</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 04: Correlation and Regression Analysis</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Correlation and Regression Analysis</td>
					<td> </td>
					<td><span><i></i> 00:35:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 05: Probability </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Probability</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 06: Sampling </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Sampling</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 07: Charts and Graphs</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Charts and Graphs</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 08: Hypothesis Testing </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Hypothesis Testing</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 09: Ten Common Statistical Mistakes </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Ten Common Statistical Mistakes</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Assignment</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assignment - Statistics</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
									</div>
						</div>
						<div id="menu2">
							
						</div>

Overview
Join our Statistics course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Statistics course has everything you need to get a great start in this sector.
Improving and moving forward is key to getting ahead personally. The Statistics course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now!
You Will Learn Following Things:
Learn strategies to boost your workplace efficiency.
Hone your skills to help you advance your career.
Acquire a comprehensive understanding of various topics and tips.
Learn in-demand skills that are in high demand among UK employers
This course covers everything you must know to stand against the tough competition. The future is truly yours to seize with this Statistics. Enrol today and complete the course to achieve a certificate that can change your career forever.
Details

Perks of Learning with IOMH
One-to-one support from a dedicated tutor throughout your course.
Study online - whenever and wherever you want.
Instant Digital/ PDF certificate
100% money back guarantee
12 months access
This course covers everything you must know to stand against the tough competition. The future is truly yours to seize with this Statistics. Enrol today and complete the course to achieve a certificate that can change your career forever.
Process of Evaluation
After studying the course, your skills and knowledge will be tested with an MCQ exam or assignment. You have to get a score of 60% to pass the test and get your certificate.
Certificate of Achievement
After completing the Statistics course, you will receive your CPD-accredited Digital/PDF Certificate for £5.99. To get the hardcopy certificate for £12.99, you must also pay the shipping charge of just £3.99 (UK) and £10.99 (International).
Who Is This Course for?
This Statistics is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand. 
On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level. 
This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education.
Requirements
There is no prerequisite to enrol in this course. You don't need any educational qualification or experience to enrol in the Statistics course.
Do note: you must be at least 16 years old to enrol.
Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course.
Career Path
The certification and skills you get from this Statistics Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors.
 Course Curriculum

Module 01: Introduction to Statistics			
	Introduction to Statistics		00:18:00
Module 02: Measuring Central Tendency			
	Measuring Central Tendency		00:18:00
Module 03: Measures of Dispersion			
	Measures of Dispersion		00:11:00
Module 04: Correlation and Regression Analysis			
	Correlation and Regression Analysis		00:35:00
Module 05: Probability			
	Probability		00:16:00
Module 06: Sampling			
	Sampling		00:19:00
Module 07: Charts and Graphs			
	Charts and Graphs		00:21:00
Module 08: Hypothesis Testing			
	Hypothesis Testing		00:25:00
Module 09: Ten Common Statistical Mistakes			
	Ten Common Statistical Mistakes		00:30:00
Assignment			
	Assignment - Statistics		00:00:00

IOMH - Institute of Mental Health

Statistics

Maths

Teacher

Professional Development

<p>Did you know that the demand for statisticians in the UK is projected to increase by 34% by 2026? In fact, statistics is one of the fastest-growing fields in the country, with an estimated 58,000 new jobs expected to be created in the next decade. This makes it a great time to invest in your future by enrolling in the Statistics QLS Level 3 &amp; 4 course.</p> <p>Through this course, you will gain a comprehensive understanding of statistical concepts and methods, including probability theory, regression analysis, hypothesis testing, and more. With a curriculum that spans from basic to higher knowledge, the course is designed to meet the needs of learners at all levels, from beginners to advanced users.</p> <p>The Statistics QLS Level 3 &amp; 4 course is endorsed by Quality Licence Scheme, which means that you can be assured of the highest quality of education and training. Upon completion of the course, you will receive a Level 4 Endorsed Certificate hardcopy free of charge, which can greatly enhance your career prospects in fields such as finance, healthcare, marketing, and more.</p> <p>In addition to the Level 4 Endorsed Certificate, learners who enrol in the Statistics QLS Level 3 &amp; 4 course will receive <strong>5 career-guided courses free</strong> with the QLS course, including career development plan fundamentals, networking skills for personal success, boost your confidence and self-esteem, CV writing and job searching, and learn to reduce and manage stress.</p> <p>So why wait? Take the first step towards a brighter future and enrol in the Statistics QLS Level 3 &amp; 4 course today. Whether you're looking to enhance your career prospects or simply want to gain a deeper understanding of statistics, this course has everything you need to succeed.</p> <p><strong>Learning Outcomes:</strong></p> <ul> <li>Develop an understanding of the fundamental principles of statistics, including the measurement of central tendency and dispersion.</li><li>Gain knowledge of correlation and regression analysis, including how to interpret statistical relationships between variables.</li><li>Understand probability and its application in statistical analysis.</li><li>Learn about sampling methods and their use in statistical analysis.</li><li>Develop the ability to create and interpret charts and graphs for data analysis.</li><li>Gain knowledge of hypothesis testing and how to apply statistical tests to analyse data.</li><li>Identify and avoid common statistical mistakes in data analysis.</li></ul><p><strong>Diploma in Statistics - QLS Level 3 &amp; 4's Description:</strong></p> <p><strong>Level 3</strong> curriculum provides an introduction to statistics, covering the fundamental principles of measurement of central tendency, dispersion, correlation, and regression analysis. Students will learn how to apply statistical analysis to a range of data sets. They will also gain knowledge of how to create meaningful conclusions and interpretations from statistical data.</p> <p><strong>Level 4</strong> curriculum covers more advanced statistical methods, including probability theory, sampling methods, charts and graphs, and hypothesis testing. Students will learn how to apply these methods to real-world scenarios and develop their skills in data analysis. They will also gain knowledge of common statistical mistakes and how to avoid them.</p> <p>This course is designed for individuals who are interested in pursuing a career in data analysis or research. It is suitable for individuals who are looking to gain knowledge of the fundamental principles of statistics or who want to expand their knowledge in statistical methods.</p> <p><strong>This bundle comprises the following courses:</strong></p> <p><strong>QLS Endorsed Course:</strong></p> <ul> <li><strong>Course 01: </strong>Diploma in Statistics at QLS Level 4</li></ul> <p><strong>CPD QS Accredited Courses:</strong></p> <ul> <li><strong>Course 01:</strong> Career Development Plan Fundamentals</li><li><strong>Course 02:</strong> Networking Skills for Personal Success</li><li><strong>Course 03:</strong> Boost Your Confidence and Self-Esteem</li><li><strong>Course 04:</strong> CV Writing and Job Searching</li><li><strong>Course 05:</strong> Learn to Reduce and Manage Stress</li></ul> <p><strong>Certificate</strong></p> <p>In pursuing this QLS-endorsed course, learners attain a profound understanding and amplify their professional prowess, enriching their curriculum vitae.</p> <p>The Quality Licence Scheme stands as a reputable and distinguished endorsement, emblematic of exceptional standards and excellence throughout the UK.</p> <p>Upon completion, learners will receive an Diploma in Statistics at QLS Level 4 Endorsed hard copy certificate, making it easy for them to showcase their achievements.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        15 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                             <ul> <li>Individuals interested in pursuing a career in data analysis or research.</li><li>Those looking to gain knowledge of the fundamental principles of statistics.</li><li>People who want to enhance their existing skills in statistical analysis.</li><li>Individuals who want to learn how to apply statistical analysis to real-world scenarios.</li><li>Those looking to expand their knowledge in statistical methods.</li></ul> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <ul> <li>Data Analyst - £25,000 to £50,000 per year</li><li>Research Analyst - £20,000 to £45,000 per year</li><li>Statistician - £30,000 to £60,000 per year</li><li>Business Intelligence Analyst - £25,000 to £55,000 per year</li><li>Market Research Analyst - £20,000 to £45,000 per year</li><li>Financial Analyst - £25,000 to £60,000 per year.</li></ul> 
                        </div>

Imperial Academy

Level 4 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited

Statistics - QLS Level 3 & 4

<h2 id="isPasted" style="color: rgb(57, 54, 79); box-sizing: border-box;" fr-original-style=" color: rgb(57, 54, 79);">About this event</h2><div style="color: rgb(57, 54, 79); box-sizing: border-box;" fr-original-style=" color: rgb(57, 54, 79);"><h3 style="color: rgb(30, 10, 60); box-sizing: border-box;" fr-original-style=" color: rgb(30, 10, 60);">COURSE INTRODUCTION</h3><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">This course covers Tuberculosis, latent and active, diagnosis, screening and testing, groups offered vaccine, symptoms and treatment; within the current regulatory and legal framework. We discuss Mantoux, BCG techniques, IGRA testing, Scar identification and current issues. The course is hands-on and practical and each participant receives a certificate after successfully completing the course.</p><h3 style="color: rgb(30, 10, 60); box-sizing: border-box;" fr-original-style=" color: rgb(30, 10, 60);">AIMS AND OBJECTIVES</h3><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Be able to provide accurate and up to date information about BCG and TB vaccines</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Update staff on current issues</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Provide a safe, effective and high standard of care</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><br fr-original-style="" style="box-sizing: border-box;"></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">COURSE CONTENTS</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; The aims of TB immunisation: national policy and schedules</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; History of TB</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; How common is TB</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Epidemiology and current statistics</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Screening and testing</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Latent TB infection</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Active TB disease</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Signs and symptoms of TB</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Diagnosis</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; IGRA Testing</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Treatment</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Management</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; TB Vaccination</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Practical workshop</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Technique-intradermal</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; BCG Technique</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Mantoux / Technique</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Scar identification</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; The legal aspects of vaccination</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Prescribing vaccines</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Storage and handling of vaccines - cold chain</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Correct administration of vaccines</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Record-keeping and reporting</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Competence Assessment</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Ref: National Minimum Standards for Immunisation Training, PHE and RCN, Immunisation for Infectious Diseases</p><h3 style="color: rgb(30, 10, 60); box-sizing: border-box;" fr-original-style=" color: rgb(30, 10, 60);">WHO SHOULD ATTEND?</h3><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Nurses - Primary Care, Secondary Care</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Occupational Health Staff</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Doctors - Occupational Health</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Military Staff</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Midwives</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; Neonatal Nurses</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">&bull; School Nurses</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Includes All course materials and certificate of attendance</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">AB Health Group awards CPD points / certificate of attendance for each course. &nbsp;If you would prefer an accredited certificate by our accrediting body Aim Qualifications we can organise this. &nbsp;The charge for the certificate including postage is &pound;30.00.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><br fr-original-style="" style="box-sizing: border-box;"></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Annie Barr AB Health Group</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Organiser Description</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Annie Barr is a leading provider of high quality, accredited healthcare training. With a selection of engaging healthcare training that has been developed by experts to equip you and your workforce with the skills, knowledge and competence needed to provide compassionate and high quality care.</p></div>

About this event
COURSE INTRODUCTION
This course covers Tuberculosis, latent and active, diagnosis, screening and testing, groups offered vaccine, symptoms and treatment; within the current regulatory and legal framework. We discuss Mantoux, BCG techniques, IGRA testing, Scar identification and current issues. The course is hands-on and practical and each participant receives a certificate after successfully completing the course.
AIMS AND OBJECTIVES
• Be able to provide accurate and up to date information about BCG and TB vaccines
• Update staff on current issues
• Provide a safe, effective and high standard of care


COURSE CONTENTS
• The aims of TB immunisation: national policy and schedules
• History of TB
• How common is TB
• Epidemiology and current statistics
• Screening and testing
• Latent TB infection
• Active TB disease
• Signs and symptoms of TB
• Diagnosis
• IGRA Testing
• Treatment
• Management
• TB Vaccination
• Practical workshop
• Technique-intradermal
• BCG Technique
• Mantoux / Technique
• Scar identification
• The legal aspects of vaccination
• Prescribing vaccines
• Storage and handling of vaccines - cold chain
• Correct administration of vaccines
• Record-keeping and reporting
• Competence Assessment
Ref: National Minimum Standards for Immunisation Training, PHE and RCN, Immunisation for Infectious Diseases
WHO SHOULD ATTEND?
• Nurses - Primary Care, Secondary Care
• Occupational Health Staff
• Doctors - Occupational Health
• Military Staff
• Midwives
• Neonatal Nurses
• School Nurses
Includes All course materials and certificate of attendance
AB Health Group awards CPD points / certificate of attendance for each course. If you would prefer an accredited certificate by our accrediting body Aim Qualifications we can organise this. The charge for the certificate including postage is £30.00.


Annie Barr AB Health Group
Organiser Description
Annie Barr is a leading provider of high quality, accredited healthcare training. With a selection of engaging healthcare training that has been developed by experts to equip you and your workforce with the skills, knowledge and competence needed to provide compassionate and high quality care.

AB Health Group

General Health

Medical Training

Public Health

BCG/Mantoux and TB - Webinar

TB / BCG Training WEBINAR

<strong>This course covers the fundamental topics of statistical methodology, linear regression, and ordinary least squares that every statistician needs to know.</strong>
        <p>To work with statistics and quantitative reports, you need to have a good understanding of the fundamentals and techniques of statistics. However, learning and applying new statistical techniques can often be a daunting experience and this is where this video course comes in handy. This course is designed to guide you through the basic principles of statistical methodology in a step-by-step way.
You'll start with an introduction to the course along with an overview of the learning objectives. Next, you'll explore the types of regression analysis that exist, and find out how ordinary least squares (OLS) work. To get a deeper understanding of linear regression and OLS, you'll interpret and analyze complicated regression output from OLS. Finally, you'll discover essential tips and techniques that'll help you to confidently perform regression analysis.
By the end of this video, you'll be well-versed with linear regression techniques and the basic principles of statistical methodology.
The code and resources for this course is available at https://github.com/PacktPublishing/Easy-Statistics-Linear-Regression</p>
        <h2>What You Will Learn</h2><p>Understand the basic concept of statistical regression analysis<br>Become familiar with linear regression terminologies<br>Distinguish between different kinds of regression methods<br>Find out the difference between correlation and causation<br>Analyze and integrate complicated regression output from OLS<br>Explore tips and tricks relating to regression analysis</p>
        <h2>Audience</h2><p>If you are a student at any level, an experienced professional, a manager, or a government worker who wants to delve into statistical regression or work with quantitative reports and policy analysis, then this course is for you. As the course is for beginners, it doesn't require you to have any knowledge of mathematics or statistics.</p>
        <h2>Approach</h2><p>With the help of animated graphics and interesting examples, this course will teach you the fundamentals of statistical regression terminologies.</p>
        <h2>Key Features</h2><p>Explore the basic statistical fundamentals of ordinary least squares (OLS) * Understand linear regression and its application * Gain the confidence to interpret complicated regression output</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Easy-Statistics-Linear-Regression</p>
        <h2>About the Author</h2><h3>Franz Buscha</h3><p>Franz Buscha is a professor of economics at the University of Westminster, which he joined after completing his Ph.D. in economics at Lancaster University. He has been involved in numerous funded research projects from research councils and government departments. He has also contributed to a wide range of projects, including policy evaluation and bespoke econometric advice to UK government departments. Franz has published in leading journals and contributed to numerous policy reports. His research has even been covered by various media outlets. He is an experienced online educator and has published several online courses, including LinkedIn Learning. Franz also has a monthly radio program called Policy Matters on Share Radio.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. What is Regression?</h4><p></p><br><h4>3. What is Ordinary Least Squares?</h4><p></p><br><h4>4. Ordinary Least Squares Regression in Practice</h4><p></p><br><h4>5. Tips and Final Thoughts </h4><p></p>

Packt

Stata

This course covers the fundamental topics of statistical methodology, linear regression, and ordinary least squares that every statistician needs to know.

Easy Statistics: Linear Regression

<strong>This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.</strong>
        <p>Working with statistics and quantitative reports requires a good understanding of statistics fundamentals and techniques. However, learning and applying new statistical techniques can often be a daunting experience. This is where this course comes into play.
To make your experience with statistics a pleasant one, this course gives you comprehensive knowledge of basic principles of the statistical methodology, focusing on linear regression and non-linear regression.
The course starts with an introduction to easy statistics and gives you an overview of the course objectives. Next, you will explore the types of regression analysis that exist and find out how ordinary least squares (OLS) works. To gain a deeper understanding of linear regression and OLS, you will learn to interpret and analyze complicated regression output from OLS. You will also focus on Gauss-Markov assumptions and zero conditional mean. Moving ahead, you will cover non-linear regression, exploring how it works, what the different non-linear regression models are, and the major uses. Towards the end, you will learn to work around with regression modeling with the help of practical examples.
By the end of this video, you will be well-versed with linear and non-linear regression and the basic principles of the statistical methodology.

All the resource files are added on GitHub at https://github.com/PacktPublishing/-Easy-Statistics-Linear-and-Non-Linear-Regression</p>
        <h2>What You Will Learn</h2><p>Understand the basic concept of statistical regression analysis<br>Become familiar with linear and non-linear regression terminologies<br>Distinguish between different types of regression methods<br>Analyze and integrate complicated regression output from ordinary least squares (OLS)<br>Find out the difference between logit and probit transformation<br>Model non-linear relationships in a linear regression</p>
        <h2>Audience</h2><p>If you're a student, an experienced professional, a manager, or a government worker who wants to learn linear and non-linear regression, regression modeling, and ordinary least squares, then this course is for you. This is a beginner-level course and does not require any prior knowledge of mathematics or statistics.</p>
        <h2>Approach</h2><p>With the help of eye-catching animated graphics and interesting examples, this course will teach you linear and non-linear regression and regression modeling to ensure that you become well-versed with the fundamentals of statistics and quantitative analysis.</p>
        <h2>Key Features</h2><p>Understand the statistical fundamentals of ordinary least squares (OLS) * Gain the confidence to comfortably interpret complicated regression output from OLS * Explore regression modeling and its application</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/-Easy-Statistics-Linear-and-Non-Linear-Regression</p>
        <h2>About the Author</h2><h3>Franz Buscha</h3><p>Franz Buscha is a professor of economics at the University of Westminster, which he joined after completing his Ph.D. in economics at Lancaster University. He has been involved in numerous funded research projects from research councils and government departments. He has also contributed to a wide range of projects, including policy evaluation and bespoke econometric advice to UK government departments. Franz has published in leading journals and contributed to numerous policy reports. His research has even been covered by various media outlets. He is an experienced online educator and has published several online courses, including LinkedIn Learning. Franz also has a monthly radio program called Policy Matters on Share Radio.</p>
        <h2>Course Outline</h2><h4>1. Linear Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is Easy Statistics: Linear Regression?</p><p style='margin: 0'>This video explains the concept of easy statistics with linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Linear Regression?</p><p style='margin: 0'>This video explains the concept of linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>3. Learning Outcomes</p><p style='margin: 0'>This video explains the various learning outcomes.</p></td></tr><tr><td style='padding: 15px;'><p>4. Whom is this Course for?</p><p style='margin: 0'>This video discusses the target audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>5. Prerequisites</p><p style='margin: 0'>This video explains the various prerequisites of the course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Using Stata</p><p style='margin: 0'>This video shows how to use Stata for linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>7. What is Regression Analysis?</p><p style='margin: 0'>This video explains the concept of regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>8. Get to Know About Linear Regression</p><p style='margin: 0'>This video explains the concept of linear regression in detail.</p></td></tr><tr><td style='padding: 15px;'><p>9. Why is Regression Analysis Useful?</p><p style='margin: 0'>This video explains the importance and uses of regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>10. What Types of Regression Analysis Exist?</p><p style='margin: 0'>This video explains the various types of regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>11. Explaining Regression</p><p style='margin: 0'>This video explains the concept of regression.</p></td></tr><tr><td style='padding: 15px;'><p>12. Lines of Best Fit</p><p style='margin: 0'>This video explains the lines of best fit concept.</p></td></tr><tr><td style='padding: 15px;'><p>13. Causality vs. Correlation</p><p style='margin: 0'>This video illustrates the difference between causality and correlation.</p></td></tr><tr><td style='padding: 15px;'><p>14. What is Ordinary Least Squares (OLS)?</p><p style='margin: 0'>This video explains the concept of OLS.</p></td></tr><tr><td style='padding: 15px;'><p>15. Ordinary Least Squares (OLS) Visual - Part 1</p><p style='margin: 0'>This video is the first part of the two-part video that explains OLS visually.</p></td></tr><tr><td style='padding: 15px;'><p>16. Ordinary Least Squares (OLS) Visual - Part 2</p><p style='margin: 0'>This video is the second part of the two-part video that explains OLS visually.</p></td></tr><tr><td style='padding: 15px;'><p>17. Sum of Squares</p><p style='margin: 0'>This video explains how to calculate the sum of squares using linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>18. Best Linear Unbiased Estimator</p><p style='margin: 0'>This video explains the best linear unbiased estimator.</p></td></tr><tr><td style='padding: 15px;'><p>19. The Gauss-Markov Assumptions</p><p style='margin: 0'>This video discusses the Gauss-Markov assumptions.</p></td></tr><tr><td style='padding: 15px;'><p>20. Homoskedasticity</p><p style='margin: 0'>This video explains the concept of homoskedasticity.</p></td></tr><tr><td style='padding: 15px;'><p>21. No Perfect Collinearity</p><p style='margin: 0'>This video explains the concept of no perfect collinearity.</p></td></tr><tr><td style='padding: 15px;'><p>22. Linear in Parameters</p><p style='margin: 0'>This video focuses on linearity in parameters.</p></td></tr><tr><td style='padding: 15px;'><p>23. Zero Conditional Mean</p><p style='margin: 0'>This video explains what is zero conditional mean.</p></td></tr><tr><td style='padding: 15px;'><p>24. How to Test and Correct for Endogeneity?</p><p style='margin: 0'>This video explains how to test and correct for endogeneity.</p></td></tr><tr><td style='padding: 15px;'><p>25. The Gauss-Markov Assumptions - Recap</p><p style='margin: 0'>This video presents a recap of the Gauss-Markov assumptions.</p></td></tr><tr><td style='padding: 15px;'><p>26. Stata - Applied Examples</p><p style='margin: 0'>This video explains with an example how to use Stata.</p></td></tr><tr><td style='padding: 15px;'><p>27. Final Thoughts and Tips</p><p style='margin: 0'>This video provides some final thoughts and tips with respect to linear regression.</p></td></tr></tbody></table><br><h4>2. Non-Linear Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is Easy Statistics: Non-Linear Regression?</p><p style='margin: 0'>This video explains the concept of easy statistics with non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Non-Linear Regression?</p><p style='margin: 0'>This video explains the concept of non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>3. What are the Main Learning Outcomes?</p><p style='margin: 0'>This video presents the learning outcome of this course.</p></td></tr><tr><td style='padding: 15px;'><p>4. Whom is this Course for?</p><p style='margin: 0'>This video discusses the target audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>5. Prerequisites</p><p style='margin: 0'>This video discusses the various prerequisites for this course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Using Stata</p><p style='margin: 0'>This video explains how to use Stata.</p></td></tr><tr><td style='padding: 15px;'><p>7. What is Non-Linear Regression Analysis?</p><p style='margin: 0'>This video explains the concept of non-linear regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>8. How does Non-Linear Regression Work?</p><p style='margin: 0'>This video explains the working of non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>9. Why is Non-Linear Regression analysis Useful?</p><p style='margin: 0'>This video explains the importance and uses of non-linear regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>10. Types of Non-Linear Regression models</p><p style='margin: 0'>This video explains the various types of non-linear regression models.</p></td></tr><tr><td style='padding: 15px;'><p>11. Maximum Likelihood</p><p style='margin: 0'>This video explains the maximum likelihood estimation technique.</p></td></tr><tr><td style='padding: 15px;'><p>12. Linear Probability Model</p><p style='margin: 0'>This video explains the linear probability model.</p></td></tr><tr><td style='padding: 15px;'><p>13. The Logit and Probit Transformation</p><p style='margin: 0'>This video explains the logit and probit transformation model.</p></td></tr><tr><td style='padding: 15px;'><p>14. Latent Variables</p><p style='margin: 0'>This video focuses on latent variables.</p></td></tr><tr><td style='padding: 15px;'><p>15. What are Marginal Effects?</p><p style='margin: 0'>This video explains the concept of marginal effects.</p></td></tr><tr><td style='padding: 15px;'><p>16. Dummy Explanatory Variables</p><p style='margin: 0'>This video focuses on the dummy explanatory variables.</p></td></tr><tr><td style='padding: 15px;'><p>17. Multiple Non-Linear Regression</p><p style='margin: 0'>This video explains multiple non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>18. Goodness-of-Fit</p><p style='margin: 0'>This video discusses the concept of goodness-of-fit.</p></td></tr><tr><td style='padding: 15px;'><p>19. A Note about Logit Coefficients</p><p style='margin: 0'>This video provides a note about logit coefficients.</p></td></tr><tr><td style='padding: 15px;'><p>20. Tips for Logit and Probit Regression</p><p style='margin: 0'>This video provides some tips for logit and probit regression.</p></td></tr><tr><td style='padding: 15px;'><p>21. Back to the Linear Probability Model</p><p style='margin: 0'>This video explains the linear probability model.</p></td></tr><tr><td style='padding: 15px;'><p>22. Stata - Applied Logit and Probit Examples</p><p style='margin: 0'>This video provides examples of applied logit and probit using Stata.</p></td></tr></tbody></table><br><h4>3. Regression Modelling</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to regression modelling.</p></td></tr><tr><td style='padding: 15px;'><p>2. Regression Modelling - Don't Rush it</p><p style='margin: 0'>This video explains the concept of regression modelling in detail.</p></td></tr><tr><td style='padding: 15px;'><p>3. Non-Linear Shapes in Regression</p><p style='margin: 0'>This video explains the concept of non-linear shapes in regression.</p></td></tr><tr><td style='padding: 15px;'><p>4. Non-Linear Shapes in Regression - Practical Examples</p><p style='margin: 0'>This video explains the concept of non-linear shapes in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>5. How to use and Interpret Interaction Effects?</p><p style='margin: 0'>This video explains how to use and interpret interaction effects.</p></td></tr><tr><td style='padding: 15px;'><p>6. How to use and Interpret Interaction Effects? - Practical Examples</p><p style='margin: 0'>This video explains how to use and interpret interaction effects with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>7. Using Time in Regression</p><p style='margin: 0'>This video explains how to use time in regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. Using Time in Regression - Practical Examples</p><p style='margin: 0'>This video explains how to use time in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>9. Categorical Explanatory Variables in Regression</p><p style='margin: 0'>This video explains the categorical explanatory variables in regression.</p></td></tr><tr><td style='padding: 15px;'><p>10. Categorical Explanatory Variables in Regression - Practical Examples</p><p style='margin: 0'>This video explains the categorical explanatory variables in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>11. Dealing with Multicollinearity in Regression</p><p style='margin: 0'>This video explains how to deal with multicollinearity in regression.</p></td></tr><tr><td style='padding: 15px;'><p>12. Dealing with Multicollinearity in Regression - Practical Examples</p><p style='margin: 0'>This video explains how to deal with multicollinearity in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>13. Dealing with Missing Data in Regression</p><p style='margin: 0'>This video explains how to deal with missing data in regression.</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Missing Data in Regression - Practical Examples</p><p style='margin: 0'>This video explains how to deal with missing data in regression with the help of examples.</p></td></tr></tbody></table>

This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.

Easy Statistics: Linear and Non-Linear Regression

<strong>Master Data Science skills using Python and real time project and go from Beginner to Super Advance level</strong>
        <p>Want to become a good Data Scientist?  Then this is a right course for you.
This course was designed by IT professionals with a Master's in Mathematics and Data Science.  We cover complex theories, algorithms, and coding libraries in a very simple way so they can be easily grasped by any beginner.
We walk you step-by-step through the World of Data science. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advanced level.
All the codes and supporting files for this course are available at: https://github.com/PacktPublishing/Data-Statistics-with-Full-Stack-Python</p>
        <h2>What You Will Learn</h2><p>Master Data Science on Python<br>Learn to use Numpy and Pandas for Data Analysis<br>Learn all the math you need to understand Machine Learning algorithms<br>Real-world case studies<br>Learn to use Matplotlib for Python plotting<br>Learn to use Seaborn for statistical plots<br>Master end-to-end data science solutions<br>Learn all statistical concepts you need to become a Machine Learning ninja </p>
        <h2>Audience</h2><p>This course is for anyone who wants to become a Data Scientist.</p>
        <h2>Approach</h2><p>Exhaustive and packed with step-by-step instructions, working examples, and helpful advice, this course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest.</p>
        <h2>Key Features</h2><p>Master complex theories, algorithms, and coding libraries in a very simple way * Master Data Science in Python</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/data-statistics-with-full-stack-python</p>
        <h2>About the Author</h2><h3>Geekshub Pvt. Ltd. </h3><p>Geekshub is an online education company in the field of big data and analytics. Their aim as a team is to provide the best skill-set to their customers to make them job-ready and prepare them to crack any challenge. They have the best trainers for cutting-edge technologies such as machine learning, deep learning, Natural Language Processing (NLP), reinforcement learning, and data science. Their instructors are people who graduated from IIT, MIT and Standford. They are passionate about teaching the topics using curated real-world case studies that calibrate the learning experience of students.</p>
        <h2>Course Outline</h2><h4>1. Python Fundamentals</h4><p></p><br><h4>2. Numpy</h4><p></p><br><h4>3. Pandas</h4><p></p><br><h4>4. Some Fun With Maths</h4><p></p><br><h4>5. Inferential Statistics</h4><p></p><br><h4>6. Hypothesis Testing</h4><p></p><br><h4>7. Data Visualization</h4><p></p><br><h4>8. Exploratory Data Analysis</h4><p></p><br><h4>9. Simple Linear Regression</h4><p></p><br><h4>10. Real World Problem - Investment Requirement Analysis for a Company</h4><p></p><br><h4>11. Loan Analysis Project</h4><p></p>

Python

Master Data Science skills using Python and real time project and go from Beginner to Super Advance level

Data Statistics with Full Stack Python

<strong>This course is designed for beginners, although we will go deep gradually, and is a highly focused course designed to master your Python skills in probability and statistics, which covers the major part of machine learning or data science-related career opportunities.</strong>
        <p>In today's ultra-competitive business universe, probability and statistics are the most important fields of study. That is because statistical research presents businesses with the data they need to make informed decisions in every business area, whether it is market research, product development, product launch timing, customer data analysis, sales forecast, or employee performance.

But why do you need to master probability and statistics in Python?

The answer is that an expert grip on the concepts of statistics and probability with data science will enable you to take your career to the next level. This course is designed carefully to reflect the most in-demand skills that will help you in understanding the concepts and methodology with regard to Python.

The course is as follows:

Easy to understand
Expressive
Comprehensive
Practical with live coding
About establishing links between probability and machine learning

By the end of this course, you will be able to relate the concepts and theories in machine learning with probabilistic reasoning and understand the methodology of statistics and probability with data science, using real datasets.

The code files and all related files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Mastering-Probability-and-Statistics-in-Python</p>
        <h2>What You Will Learn</h2><p>The importance of statistics and probability in data science<br>The foundations for machine learning and its roots in probability theory<br>The concepts of absolute beginning in-depth with examples in Python<br>Practical explanation and live coding with Python<br>Probabilistic view of modern machine learning<br>Implementation of Bayes' classifier on a real dataset</p>
        <h2>Audience</h2><p>This course is for individuals who want to learn statistics and probability along with its implementation in realistic projects. Data scientists and business analysts and those who want to upgrade their data analysis skills will also get the benefit. People who want to learn statistics and probability with real datasets in data science and are passionate about numbers and programming will get the most out of this course.<br><br>No prior knowledge is needed. You start from the basics and gradually build your knowledge of the subject. A basic understanding of Python will be a plus but not mandatory.</p>
        <h2>Approach</h2><p>This course is designed for beginners, although we will go deep gradually. You will learn theoretical concepts first, followed by its practical implementation in Python. At the end of each module, you will work on the homework/tasks, which will evaluate/further build your learning based on the previous concepts and methods.</p>
        <h2>Key Features</h2><p>Easy explanations, yet complete and comprehensive course * Fundamental, pythonic, and a complete course to master the important concepts used in data science * Practical with live coding of the implementation of the concepts learned theoretically</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Mastering-Probability-and-Statistics-in-Python</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to the Instructor</p><p style='margin: 0'>This video gives you an introduction to AI sciences and to the instructor of this course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Focus of the Course</p><p style='margin: 0'>This video shows you the objective of the course.</p></td></tr></tbody></table><br><h4>2. Probability and Statistics</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Probability Versus Statistics</p><p style='margin: 0'>This video focuses on probability versus statistics.</p></td></tr></tbody></table><br><h4>3. Sets</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Definition of Set</p><p style='margin: 0'>This video demonstrates the definition of a set.</p></td></tr><tr><td style='padding: 15px;'><p>2. Cardinality of a Set</p><p style='margin: 0'>This video explains the cardinality of a set.</p></td></tr><tr><td style='padding: 15px;'><p>3. Subsets, Power Set, and Universal Set</p><p style='margin: 0'>This video explains subsets, power set, and universal set.</p></td></tr><tr><td style='padding: 15px;'><p>4. Python Practice Subsets</p><p style='margin: 0'>This video lets you practice on the subsets that you have learned so far.</p></td></tr><tr><td style='padding: 15px;'><p>5. Power Sets Solution</p><p style='margin: 0'>This video answers the problem statement to generate the power set.</p></td></tr><tr><td style='padding: 15px;'><p>6. Operations</p><p style='margin: 0'>This video explores the different types of operations that can be performed on the sets.</p></td></tr><tr><td style='padding: 15px;'><p>7. Python Practice Operations</p><p style='margin: 0'>This video lets you practice on the types of operations that you have learned so far.</p></td></tr><tr><td style='padding: 15px;'><p>8. Venn Diagrams Operations</p><p style='margin: 0'>This video shows the types of operations that can be performed on the Venn diagrams.</p></td></tr><tr><td style='padding: 15px;'><p>9. Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>4. Experiment</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Random Experiment</p><p style='margin: 0'>This video explains about a random experiment.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outcome and Sample Space</p><p style='margin: 0'>This video explains what is meant by the outcome and sample space of any experiment.</p></td></tr><tr><td style='padding: 15px;'><p>3. Event</p><p style='margin: 0'>This video explains about the event in an experiment.</p></td></tr><tr><td style='padding: 15px;'><p>4. Recap and Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>5. Probability Model</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Probability model</p><p style='margin: 0'>This video talks about probability models.</p></td></tr><tr><td style='padding: 15px;'><p>2. Probability Axioms</p><p style='margin: 0'>This video explains axioms of probability.</p></td></tr><tr><td style='padding: 15px;'><p>3. Probability Axioms Derivations</p><p style='margin: 0'>This video covers derivation from axioms of probability.</p></td></tr><tr><td style='padding: 15px;'><p>4. Probability Models Example</p><p style='margin: 0'>This video shows examples of probability models.</p></td></tr><tr><td style='padding: 15px;'><p>5. More Examples of Probability Models</p><p style='margin: 0'>This video shows a few more examples of probability models.</p></td></tr><tr><td style='padding: 15px;'><p>6. Probability Models Continuous</p><p style='margin: 0'>This video talks about continuous probability models.</p></td></tr><tr><td style='padding: 15px;'><p>7. Conditional Probability</p><p style='margin: 0'>This video explains the concept of conditional probability.</p></td></tr><tr><td style='padding: 15px;'><p>8. Conditional Probability Example</p><p style='margin: 0'>This video shows an example of conditional probability.</p></td></tr><tr><td style='padding: 15px;'><p>9. Conditional Probability Formula</p><p style='margin: 0'>This video covers the formula for conditional probability.</p></td></tr><tr><td style='padding: 15px;'><p>10. Conditional Probability in Machine Learning</p><p style='margin: 0'>This video demonstrates the importance of conditional probability in Machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>11. Conditional Probability Total Probability Theorem</p><p style='margin: 0'>This video covers the law of total probability or total probability theorem.</p></td></tr><tr><td style='padding: 15px;'><p>12. Probability Models Independence</p><p style='margin: 0'>This video explains one of the important probability theories, that is, "Independence".</p></td></tr><tr><td style='padding: 15px;'><p>13. Probability Models Conditional Independence</p><p style='margin: 0'>This video continues with the concept of Independence in the probability theory.</p></td></tr><tr><td style='padding: 15px;'><p>14. Probability Models Bayes' Rule</p><p style='margin: 0'>This video explains the Bayes' rule or theorem in probability theory.</p></td></tr><tr><td style='padding: 15px;'><p>15. Probability Models towards Random Variables</p><p style='margin: 0'>This video talks about random variables.</p></td></tr><tr><td style='padding: 15px;'><p>16. Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>6. Random Variables</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to random variables.</p></td></tr><tr><td style='padding: 15px;'><p>2. Random Variables Examples</p><p style='margin: 0'>This video shows examples of random variables.</p></td></tr><tr><td style='padding: 15px;'><p>3. Bernoulli Random Variables</p><p style='margin: 0'>This video explains discrete random variables and you will also learn about the Bernoulli random variable.</p></td></tr><tr><td style='padding: 15px;'><p>4. Bernoulli Trail Python Practice</p><p style='margin: 0'>This video builds simulation for the Bernoulli random variable.</p></td></tr><tr><td style='padding: 15px;'><p>5. Geometric Random Variable</p><p style='margin: 0'>This video explains the concept of geometric random variable.</p></td></tr><tr><td style='padding: 15px;'><p>6. Geometric Random Variable Normalization Proof Optional</p><p style='margin: 0'>This video will continue to discuss the geometric random variable.</p></td></tr><tr><td style='padding: 15px;'><p>7. Geometric Random Variable Python Practice</p><p style='margin: 0'>This video shows the implementation of geometric random variable in Python.</p></td></tr><tr><td style='padding: 15px;'><p>8. Binomial Random Variables</p><p style='margin: 0'>This video explains the concept of binomial random variables.</p></td></tr><tr><td style='padding: 15px;'><p>9. Binomial Python Practice</p><p style='margin: 0'>This video shows the implementation of binomial random variable in Python.</p></td></tr><tr><td style='padding: 15px;'><p>10. Random Variables in Real Datasets</p><p style='margin: 0'>This video shows the real data sets and helps you build a relation of random variables on real datasets.</p></td></tr><tr><td style='padding: 15px;'><p>11. Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>7. Continuous Random Variables</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Zero Probability to Individual Values</p><p style='margin: 0'>This video talks about continuous random variables.</p></td></tr><tr><td style='padding: 15px;'><p>2. Probability Density Functions</p><p style='margin: 0'>This video explains probability density functions.</p></td></tr><tr><td style='padding: 15px;'><p>3. Uniform Distribution</p><p style='margin: 0'>This video explains uniform distribution.</p></td></tr><tr><td style='padding: 15px;'><p>4. Uniform Distribution Python</p><p style='margin: 0'>This video shows the implementation of uniform distribution in Python.</p></td></tr><tr><td style='padding: 15px;'><p>5. Exponential</p><p style='margin: 0'>This video explains about the exponential random variables.</p></td></tr><tr><td style='padding: 15px;'><p>6. Exponential Python</p><p style='margin: 0'>This video shows the implementation of exponential random variables in Python.</p></td></tr><tr><td style='padding: 15px;'><p>7. Gaussian Random Variables</p><p style='margin: 0'>This video explains about Gaussian random variables.</p></td></tr><tr><td style='padding: 15px;'><p>8. Gaussian Python</p><p style='margin: 0'>This video shows the implementation of Gaussian random variables in Python.</p></td></tr><tr><td style='padding: 15px;'><p>9. Transformation of Random Variables</p><p style='margin: 0'>This video explains about the transformation of random variables.</p></td></tr><tr><td style='padding: 15px;'><p>10. Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>8. Expectations</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Definition</p><p style='margin: 0'>This video defines expectations in detail.</p></td></tr><tr><td style='padding: 15px;'><p>2. Sample Mean</p><p style='margin: 0'>This video helps you compute the mean or average of the data in Python.</p></td></tr><tr><td style='padding: 15px;'><p>3. Law of Large Numbers</p><p style='margin: 0'>This video talks about the law of large numbers.</p></td></tr><tr><td style='padding: 15px;'><p>4. Law of Large Numbers Famous Distributions</p><p style='margin: 0'>This video continues with the concept of law of large numbers.</p></td></tr><tr><td style='padding: 15px;'><p>5. Law of Large Numbers Famous Distributions Python</p><p style='margin: 0'>This video shows the implementation of law of large numbers concept in Python.</p></td></tr><tr><td style='padding: 15px;'><p>6. Variance</p><p style='margin: 0'>In this video, you will learn about the variance concept.</p></td></tr><tr><td style='padding: 15px;'><p>7. Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>9. Project Bayes' Classifier</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Project Bayes' Classifier from Scratch</p><p style='margin: 0'>In this video, you will build the Bayes' classifier in Python from scratch.</p></td></tr></tbody></table><br><h4>10. Multiple Random Variables</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Joint Distributions</p><p style='margin: 0'>This video talks about joint distributions.</p></td></tr><tr><td style='padding: 15px;'><p>2. Multivariate Gaussian</p><p style='margin: 0'>This video explains about multivariate Gaussian.</p></td></tr><tr><td style='padding: 15px;'><p>3. Conditioning Independence</p><p style='margin: 0'>This video demonstrates conditioning in random variables.</p></td></tr><tr><td style='padding: 15px;'><p>4. Classification</p><p style='margin: 0'>This video shows you the objective of the course.</p></td></tr><tr><td style='padding: 15px;'><p>5. Naive Bayes' Classification</p><p style='margin: 0'>This video explains the concepts of Naive Bayes' classifier.</p></td></tr><tr><td style='padding: 15px;'><p>6. Regression</p><p style='margin: 0'>This video talks about the regression model.</p></td></tr><tr><td style='padding: 15px;'><p>7. Curse of Dimensionality</p><p style='margin: 0'>This video talks about curse of dimensionality.</p></td></tr><tr><td style='padding: 15px;'><p>8. Homework</p><p style='margin: 0'>This video is a practice assignment that is bundled with a few questions for you to be solved and it is based upon the concepts learned in this section.</p></td></tr></tbody></table><br><h4>11. Optional Estimation</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Parametric Distributions</p><p style='margin: 0'>This video explains about parametric distributions.</p></td></tr><tr><td style='padding: 15px;'><p>2. Maximum Likelihood Estimate (MLE)</p><p style='margin: 0'>This video explains about the Maximum Likelihood Estimate (MLE).</p></td></tr><tr><td style='padding: 15px;'><p>3. Log Likelihood</p><p style='margin: 0'>This video helps you learn about Log Likelihood.</p></td></tr><tr><td style='padding: 15px;'><p>4. Maximum A Posterior Estimate (MAP)</p><p style='margin: 0'>This video explains Maximum A Posterior Estimate (MAP).</p></td></tr><tr><td style='padding: 15px;'><p>5. Logistic Regression</p><p style='margin: 0'>This video helps you understand logistic regression in detail.</p></td></tr><tr><td style='padding: 15px;'><p>6. Ridge Regression</p><p style='margin: 0'>This video helps you understand ridge regression in detail.</p></td></tr><tr><td style='padding: 15px;'><p>7. Deep Neural Network (DNN)</p><p style='margin: 0'>This video focuses on Deep Neural Network (DNN) concept.</p></td></tr></tbody></table><br><h4>12. Mathematical Derivations for Math Lovers</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Permutations</p><p style='margin: 0'>This video showcases and explains the concept of permutations in detail.</p></td></tr><tr><td style='padding: 15px;'><p>2. Combinations</p><p style='margin: 0'>This video helps you learn about combination in detail.</p></td></tr><tr><td style='padding: 15px;'><p>3. Binomial Random Variable</p><p style='margin: 0'>This video explains about the binomial random variable.</p></td></tr><tr><td style='padding: 15px;'><p>4. Logistic Regression Formulation</p><p style='margin: 0'>This video explains logistic regression formulation.</p></td></tr><tr><td style='padding: 15px;'><p>5. Logistic Regression Derivation</p><p style='margin: 0'>This video explains logistic regression derivation.</p></td></tr></tbody></table>

Data Science

This course is designed for beginners, although we will go deep gradually, and is a highly focused course designed to master your Python skills in probability and statistics, which covers the major part of machine learning or data science-related career opportunities.

Mastering Probability and Statistics in Python

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Definition of Safeguarding Legislation &amp; agreed ways of working</p><p id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Definition of Safeguarding</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Legislation &amp; agreed ways of working</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Every child matters guidance</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Myths and statistics</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Effects on young people</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Leadership/worker skills</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Common indicators of abuse</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Reporting of abuse and potential abuse</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Barriers to reporting abuse</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Understanding the types of abuse</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Recognising acts, signs and symptoms of abuse</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Who is vulnerable / at risk</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The role of dignity in safeguarding</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Legislation and policies and procedure</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">What to do should you have a concern</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Overview of prevent</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">How to respond to an individual should they disclose a concern</p>

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