3795 Engineer courses in Aberystwyth delivered Online

Learning Outcomes After completing this course, learners will be able to: Learn Python for data analysis using NumPy and Pandas Acquire a clear understanding of data visualisation using Matplotlib, Seaborn and Pandas Deepen your knowledge of Python for interactive and geographical potting using Plotly and Cufflinks Understand Python for data science and machine learning Get acquainted with Recommender Systems with Python Enhance your understanding of Python for Natural Language Processing (NLP) Description Whether you are from an engineering background or not you still can efficiently work in the field of data science and the machine learning sector, if you have proficient knowledge of Python. Since Python is the easiest and most used programming language, you can start learning this language now to advance your career with the Data Science And Machine Learning Using Python : A Bootcamp course. This course will give you a thorough understanding of the Python programming language. Moreover, it will show how can you use Python for data analysis and machine learning. Alongside that, from this course, you will get to learn data visualisation, and interactive and geographical plotting by using Python. The course will also provide detailed information on Python for data analysis, Natural Language Processing (NLP) and much more. Upon successful completion of this course, get a CPD- certificate of achievement which will enhance your resume and career. Certificate of Achievement After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  Method of Assessment After completing this course, you will be provided with some assessment questions. To pass that assessment, you need to score at least 60%. Our experts will check your assessment and give you feedback accordingly. Career Path After completing this course, you can explore various career options such as Web Developer Software Engineer Data Scientist Machine Learning Engineer Data Analyst In the UK professionals usually get a salary of £25,000 - £30,000 per annum for these positions. Course Content Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Machine Learning using Python : A Bootcamp 00:00:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations 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, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production

Apache training course description A hands on training course covering installation, configuration and management of the Apache web server. What will you learn Install Apache. Configure Apache. Manage Apache. Build static and dynamic web sites with Apache. Secure Apache. Apache training course details Who will benefit: Technical staff working with Apache. Prerequisites: TCP/IP foundation for engineers. UNIX fundamentals Duration 3 days Apache training course contents Installing Apache What is Apache? Apache versions, history, downloading Apache, source distribution, compilation, binary distribution, installation, platform considerations. Hands on Downloading and installing Apache. Controlling the Apache server Running Apache, automatic Apache start, starting, stopping, restarting Apache. Checking Apache status. Hands on Server control. Configuration Serving webpages, setting the document root, applying configuration changes, Configuration files, httpd.conf, syntax, directives, modules, utilities, turning features on/off. Hands on basic Apache configuration. More configuration MIME, URL mapping, content negotiation, indexing, performance tuning. Logging log file content, configuration, log file locations, error logging, browser errors, error page configuration, forbidden index pages. Hands on Log files. Security File permissions, .htaccess, protecting files with passwords, password files, authentication, restricting access by IP address. Secure HTTP HTTPS, installing mod_ssl, certificates, configuring mod_ssl, http and https coexistence Virtual hosts Multiple sites on one server, separate configuration files, IP based, name based, port based, virtual host names, enabling, defining, configuring, aliases, testing, https virtual hosts. Hands on Virtual hosts. Dynamic sites Dynamic sites, CGI, PHP, PERL, CGI programs, example CGI scripts, Apache and CGI, CGI parameters, CGI issues, PHP, mod_php, Perl and Apache, mod_perl, installing mod_perl. Hands on CGI, PHP and Perl with Apache. Modules What are modules, standard modules, loading modules, mod_speling, mod_rewrite, other special purpose modules, URL rewriting, redirection, URL transformation, browser dependent pages. Hands on Working with modules.

IPv6 training course description IPv6 is the next generation Internet Protocol. This hands-on course looks at the benefits and features of the new protocol along with an assessment of the likely impact of the protocol and migration strategies. Practical exercises using PCs and routers follow the major sessions in order to reinforce the theory. What will you learn Configure PCs and routers for IPv6. Troubleshoot IPv6 networks. Analyse IPv6 packets. Plan migration strategies for IPv6. Integrate IPv6 and IPv4 networks. IPv6 training course details Who will benefit: Anyone working in the field of networking. Prerequisites: TCP/IP Foundation for engineers Duration 3 days IPv6 training course contents Introduction Reasons for IPv6, IPv4 weaknesses, what is IPv6? IPv4 solutions for solving address wastage, the origins of IPv6. hands on IPv6 on a PC, IPv6 on a router. IPv6 addressing IPv6 address allocation, address format, Prefixes but no masks, address categories, scope zones, aggregatable global unicast, link local, Unicast, Multicast, Anycast. Prefix delegation. hands on Link local addresses, manual address configuration, name resolution. Plug and play Plug and play addressing, ICMP neighbour discovery, router solicitation, DHCPv6, stateful autoconfiguration and stateless autoconfiguration. hands on Plug and play addresses and default gateways. The IPv6 header The IPv4 header, IPv6 header format, QoS, flow control, priority field, extension headers, hop by hop, destinations header, fragmentation header, security, IPsec, AH, ESP, TCP and UDP, ICMPv6. hands on IPv6 packet analysis. Migrating to IPv6 Overview, migration, dual stack, IPv4 compatible addresses, DNS, IPv6 DNS issues, AAAA records, IPv6 reverse delegation, DNS transport, protocol translators, NAT-PT, NAPT-PT, NAT64, DNS64, tunnelling, tunnel establishment, tunnel brokers, Tunnel types. hands on Dual stack operation, tunnelling, IPv6 name resolution. IPv6 routing IPv6 routing, RIPng packet format, RIPng for IPv6, OSPF for IPv6, MBGP, multiprotocol routing, MBGP and multicasts, MBGP and IPv6. hands on Base router setup for IPv6, IPv6 static routes, RIPng, OSPFv3. MBGP

SIP training course description A hands on course covering IP telephony with SIP. The course starts with a brief review of knowledge students should already possess including RTP and RTCP. The main focus is on SIP though, progressing from what SIP is through SIP signalling, call processing and architectures, moving onto more advanced issues including security, multimedia, and interoperability. Hands-on practicals follow each major theory session. What will you learn Explain how SIP works. Analyse SIP packets. Deploy SIP IP telephony solutions. Integrate SIP with other telephony solutions. SIP training course details Who will benefit: Technical staff working with SIP. Prerequisites: Definitive VoIP for engineers Duration 3 days SIP training course contents VoIP review What is VoIP? Brief review of IP, Brief review of telephones and voice. RTP, RTCP, mixers and translators. Hands on Analysing RTP packets. What is SIP? Why SIP? SIP history, SIP standards, SIP capabilities, key services, how SIP works, and a basic SIP call. Hands on Peer to peer SIP. SIP messages SIP sessions, SIP flows, Message structure, INVITE, ACK, BYE, CANCEL, OPTIONS, REGISTER. Extension methods. Response codes. SIP call flows. Hands on Analysing SIP packets. SIP architectures UA client, UA server, Proxy servers, Redirect servers, registrars. SIP phones, gateways, application servers, and other products. Stateful and stateless servers. Various call scenarios. Hands on SIP proxies. SIP addressing URLs, SIP addresses, registration, Location and Directory servers. Address tracking. Hands on SIP and DNS. Supplementary services SIP signalling, signalling compression, Call hold, Call forwarding, Home and away scenarios, transfers, conferences, call control. Hands on Analysing SIP supplementary services. SDP What is SDP? Multimedia, multimedia session announcement, invitation and others. Relationship with SIP. Hands on Video conferencing with SIP. SIP security Access control, Authentication, encryption, firewalls. Hands on SIP authentication. Interoperability Inter working with PSTN, ISUP to SIP mapping, SIP and 3G, SIP-T, SIP and SIGTRAN. SIP and H323. Hands on SIP and gateways. SIP mobility Terminal mobility, service mobility, personal mobility, Mobile IP, SIP signalling flows in 3G.

Definitive VPNs training course description A hands on course covering VPNs from the basics of benefits and Internet vs. Intranet VPNs through to detailed analysis of the technologies involved in VPNs. All the major VPN protocols are covered including PPPoE, L2TP, SSL, IPsec and dynamic VPNs. MPLS L3 VPNs are also covered. What will you learn Describe what a VPN is and explain the difference between different VPN types. Recognise the design and implementation issues involved in implementing a VPN. Explain how the various technologies involved in a VPN work. Describe and implement: L2TP, IPsec, SSL, MPLS L3 VPNs. Evaluate VPN technologies. Definitive VPNs training course details Who will benefit: Network personnel. Prerequisites: IP Security foundation for engineers. Duration 3 days Definitive VPNs training course contents VPN overview What is a VPN? What is an IP VPN? VPNs vs. Private Data Networks, Internet VPNs, Intranet VPNs, Remote access VPNs, Site to site VPNs, VPN benefits and disadvantages. VPN Tunnelling VPN components, VPN tunnels, tunnel sources, tunnel end points, hardware based VPNs, Firewall based VPNs, software based VPNs, tunnelling topologies, tunnelling protocols, which tunnelling protocol should you use? requirements of tunnels. VPN security components Critical VPN security requirements, Encryption and authentication, Diffie Hellman, DES, 3DES, RSA, PKI, Ca server types, pre shared keys versus certificates, Enrolling with a CA, RADIUS in VPNs. PPP Encapsulation, operation, authentication. Hands on Setting up PPPoE and analysing PPP packets. PPTP Overview, Components, How it works, control and data connections, GRE. Hands on Building a PPTP VPN. L2TP Overview, components, how it works, security, packet authentication, L2TP/IPSec, L2TP/PPP, Layer 2 versus layer 3 tunnelling. Hands on Implementing a L2TP tunnel. IPSec AH, HMAC, ESP, transport and tunnel modes, Security Association, use of encryption and authentication algorithms, manual vs automated key exchange, NAT and other issues. Hands on Implementing an IPSec VPN. Intranet VPNs Headers, architecture, label switching, LDP, MPLS VPNs. VPN products and services PE and CPE, management, various VPN products. VPN issues and architectures VPN architectures: terminate VPN before/on/ after/in parallel with firewall, resilience issues, VRRP, performance issues, QoS and VPNs. documentation.

IPTV training course description A current hot topic in recent years has been the provision of multimedia services over IP networks aka triple or quadruple play. This course investigates the characteristics of video transmission and then studies the impact on IP networks. What will you learn Use Wireshark to analyse and troubleshoot TV streams. Describe techniques, which can be used in IP to provide low uniform delay. Evaluate IPTV technologies. Design data networks, which will support IPTV. IPTV training course details Who will benefit: Anyone working with IPTV. Prerequisites: TCP/IP Foundation for engineers Intro to data communications & networking. Duration 3 days IPTV training course contents What is IPTV? What is IP? What is TV? Pixels, frames, colour, digital modulation, digital video broadcasting. SDTV, HDTV, 4K. IPTV architectures, Contribution, distribution, delivery. IPTV standards. Hands on Base IP connectivity, VLC. IPTV protocol stacks IP, TCP, UDP, RTP. IPv6. HTTP. Bandwidth requirements. Hands on IPTV bandwidth calculations. Video codecs What is a CODEC, pictures and audio, digitisation, sampling, quantisation, encoding, compressing. MPEG, bit rates, resolution. I, B, P frames, GOP. MPEG 2, MPEG 4, H.264, H.265, VP9, AV1. Hands on Analysing MPEG frames. IP issues Quality vs. bandwidth. Bandwidth, delay, latency, jitter, signalling. Routers. Hands on Analysing jitter and other performance issues. IPTV performance and QoS IP DSCP field, queuing strategies; FIFO, WFQ, custom, priority, RED. Differentiated services, Diffserv. 802.1Q. Traffic shaping. QoE. Hands on best effort versus prioritisation. UDP versus TCP Reliable, unreliable, connection oriented, connectionless. Broadcasts, multicasts and unicasts. TCP flow control, TCP and performance. Hands on TCP window sizes. RTP RTP, ports, mixers, translators, RTCP, SMPTE, FEC. Hands on RTP analysis with Wireshark. Multicasting Multicasting compared to unicasting and broadcasting, when to use and when not to use multicasting. IGMP, PIM-SM, SSM. MLD. Hands on Analysing multicast streams. OTT TV HTTP, HTTPS, Chunked HTTP. Adaptive streaming. HTML5. DASH vs HLS. Hands on Analysing HTTP streams. Security Firewalls, TLS, DRM, watermarking. Encryption. Geolocation. VPNs. IPTV architecture and other protocols Content providers, Service providers, delivery networks, home networks. Caching, Service discovery. RTSP. SAP, SDP. DHCP, DNS, NTP Hands on Fixing the network.

VoIP training course description Convergence of voice and data is now a common place mainstream technology. Our Voice Over IP course investigates the characteristics of voice transmission and then studies the impact on IP networks. Practical sessions with soft phones, hard phones and gateways allow the students to see all aspects of VoIP. Network analysers are used to study packets on the wire. What will you learn Describe the issues of voice and data convergence. Describe techniques, which can be used in IP to provide low uniform delay. Evaluate VoIP technologies. Design data networks, which will support voice. VoIP training course details Who will benefit: Anyone working in the field of networking or telecommunications. Prerequisites: TCP/IP foundation for engineers Intro to data communications & networking Duration 3 days VoIP training course contents What is VoIP Voice over IP, brief review of IP, brief review of telephones and voice. Configuring IP softphones What are softphones? Downloading, installing. Hands on Building the base IP network, a simple VoIP call with softphones, Internet telephony. Addressing E164, FQDN, IP addresses, URIs, DNS, SIP addressing, H.323 addressing. VoIP issues Bandwidth, Delay, Jitter, digitising voice, digitisation steps, coding, quality issues, MOS, voice compression, silence suppression, packetising voice, prioritising voice, jitter buffers. Hands on Simple packet analysis. Architectures Desktop, backbone, gateway, hard phones, PoE, integrating phones and PCs, carriers, Softswitches. Hands on Integrating Softphones, hard phones and analog phones. IP performance and QoS ITU delay recommendations, IP DSCP field, DiffServ, IP precedence, queuing strategies; FIFO, WFQ, custom, priority, RED, LLQ. VoIP protocol stack RTP, RTCP, mixers and translators, RSVP. Bandwidth, Erlang models, link layer overhead. Hands on Calculating VoIP bandwidth, analysing RTP packets. ITU Recommendation H.323 Architecture, protocols, terminals, Call setup, Gatekeepers, gateway discovery, H.323 registration with a gatekeeper. Hands on PC to PC using H.323. IETF - Session Initiation Protocol What is SIP? SIP protocol stack, SDP, Sip architecture, SIP messages, Initial SIP phone startup, SIP servers, proxy server, redirect server. Hands on PC to PC using SIP. Carrier networks Signalling systems, SS7, media gateways, Media gateway controllers, signalling gateways, MGCP, Megaco, SIGTRAN. Hands on PSTN interworking. Video over IP Video components, digital video, pictures and audio, video codecs, issues and solutions, video conferencing, multipoint video conferencing, video protocol stack. Appendix 1: Multicasting. Appendix 2: Voice/data integration without IP.

SNMP training course description A hands-on generic look at the technical operation of SNMP. The course starts with an overview of all the components, which make up SNMP. Hands on starts early with configuration of a managed network. The major versions of SNMP are then put into perspective followed by a look at the SNMP protocol. MIBs are then studied both from the perspective of reading MIBs and writing MIBs. The course finishes with a look at the security implications of SNMP. What will you learn Describe the SNMP architecture. Analyse SNMP packets. Recognise the MIB structure. Describe the SMI. Recognise the strengths and weaknesses of SNMPv2 and SNMPv3. SNMP training course details Who will benefit: Network administrators. Network operators. Programmers writing MIBs and agents. Prerequisites: TCP/IP Foundation for engineers Hands on experience of an SNMP management station would also be beneficial. Duration 3 days SNMP training course contents Network management What is network management? Benefits, issues. What is SNMP? SNMP architecture, SNMP MIBs, SMI, the SNMP protocol, polling security, alternatives to SNMP: CMIP, web based management. Configuring SNMP Auto discovery for management stations, NMS configuration, agent configuration, traps. Hands on Configuring agents and an NMS. SNMP background SNMP history, RFCs, standards, SNMP protocol versions, SNMPv1, SNMPv2, SNMPv3, SNMP SMI versions, which version should you use? Futures. SNMPv1 packets SNMP in the 7 layer model, port numbers, general packet format, BER, GET, GET-NEXT, tables, SET, TRAP, bandwidth issues, in band versus out of band management. Hands on Analysing SNMPv1 packets. SNMPv2 packets SNMPv2 improvements, error handling, GETBULK, v2traps, INFORM. Hands on Analysing SNMPv2 packets. SNMPv3 packets SNMPv3 packet format, use of SNMPv2 messages, REPORT PDU. MIB structure The internet MIB branch, standard mib-2, extra parts of mib-2, private enterprise MIBs, loading extra MIBs. Hands on MIB browsing. mib-2 The mib-2 groups, system group, interfaces group, IP group, ICMP group, TCP group, UDP group, transmission group, SNMP group, RMON. Hands on mib-2 browsing in detail. SMI The MIB layout, obtaining a private enterprise number, MIB definitions, IMPORT, Module identity, Textual conventions, object definitions, notifications, compliance statements, object groups, base SMI data types, application data types, scalars, instances, tables, table definition, writing agents, SMIng. SNMP security Community strings, SNMPv1 and SNMPv2c security practices, SNMPv3 security, SNMPv3 architecture, SNMP applications, the SNMP engine, the EngineID, security fields in SNMPv3 packets, USM, authentication, encryption, timeliness, VBAC, SNMPv3 configuration.

Welcome to the exciting world of C# (C-Sharp)! This course is your gateway to mastering one of the most versatile and in-demand programming languages in the world. As technology continues to advance, C# remains at the forefront, offering endless opportunities for those who can harness its power. In this course, we will take you on a journey that begins with the fundamentals and leads you to become a proficient C# developer. In the first section, 'Introduction,' you'll gain insight into the world of C# and understand its significance in the modern software development landscape. We'll guide you through the process of setting up your development environment, ensuring you're ready to dive into coding with confidence. The course progresses to cover essential topics such as C# console programming, Winforms applications, database operations, user management, and source control. By the end, you'll have a deep understanding of C# and the practical skills needed to develop robust software applications. Join us on this exhilarating journey, and let's unlock the incredible potential of web development together. Learning Outcomes of C# (C-Sharp): Develop a solid foundation in C# programming, including mastery of basic control structures and more advanced concepts. Create Windows forms applications and understand the basics of Multi-Document Interface (MDI) for efficient user interface development. Manage databases effectively, covering CRUD (Create, Read, Update, Delete) operations and database changes. Implement user management and login functionality to enhance the security and usability of your applications. Learn the importance of source control and acquire the skills to add your projects to source control repositories. Why buy this C# (C-Sharp) course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the C# (C-Sharp) Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this C# (C-Sharp) course for? Aspiring software developers looking to build a strong foundation in programming. Computer science students seeking practical knowledge and skills for real-world application development. IT professionals aiming to expand their expertise and career opportunities in software development. Individuals interested in creating Windows applications with user-friendly interfaces. Anyone eager to understand source control and its role in collaborative software development. Career path Junior Developer: £25,000 - £40,000 Per Annum Software Engineer: £40,000 - £60,000 Per Annum Database Developer with C#: £35,000 - £55,000 Per Annum Senior C# Developer: £55,000 - £80,000 Per Annum Software Development Team Lead (C#): £70,000 - £100,000 Per Annum C# Solutions Architect: £90,000 - £130,000 Per Annum Prerequisites This C# (C-Sharp) does not require you to have any prior qualifications or experience. You can just enrol and start learning.This C# (C-Sharp) was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Introduction Introduction 00:03:00 Development Environment Setup Install Visual Studio 2019 Community Edition 00:04:00 Install .Net Core SDK 00:02:00 Tour Of Visual Studio 00:11:00 C# Console Programming - Basic Control Structures Write a Simple Hello World Program 00:15:00 Input and Output Programs 00:18:00 C# Data Types and Conversion 00:22:00 Operators in C# 00:17:00 Condition Statements 00:32:00 Repetition Statements 00:28:00 Methods and Return Types 00:27:00 C# Console Programming - More Advanced Concepts String Manipulation Functions 00:21:00 Handling Exceptions 00:13:00 Classes and Objects 00:37:00 Arrays, Lists and Other Collections 00:35:00 Winforms Applications - The Basics Create a Simple Winforms Application (Car Rental Application) 00:26:00 Understand Winforms Controls, Properties and Events 00:21:00 Form Validation and Exception Handling 00:34:00 Create Database in SQL Server 00:10:00 Create Database Model with ADO.NET 00:13:00 Select Data from the Database Using LINQ 00:15:00 Submit to Database from Form 00:17:00 Multi Document Interface (MDI) and More Database Operations Creating Additional Forms and Windows 00:14:00 Create an MDI Application 00:17:00 Managing Database Changes and CRUD Operations View Data In A Grid 00:29:00 Managing Database Changes 00:18:00 Create and Edit Records (Using one form) 00:48:00 Vehicle Data Validations and Exception Handling 00:07:00 Manage Car Rental Records 00:45:00 Enhancing Application Flow and User Experience 00:28:00 User Management and Login Functionality Simple Login Form 00:39:00 Control Features Based on User Roles 00:35:00 User Password and Active Status Reset 00:21:00 Add New Users and Password Reset 00:45:00 Add To Source Control Add Project to GitHub 00:19:00 Assignment Assignment - C# (C-Sharp) 00:00:00