3467 Courses delivered Live Online

Our teams are increasingly built from colleagues from around the world, each of whom has their own unique culture and communication style. We can help you embrace, enjoy and harness the diversity in teams for incredible outcomes! This course includes: The impact on the team of language and cultural differences Communication techniques for an effective global team The importance of clarity and commitment The difference in planning and scheduling across cultures Different perceptions of power and leadership Leveraging the diversity in your team

Certified NLP Training and Business Coaching

Duration 0.5 Days 3 CPD hours This course is intended for This course is designed for business professionals in any functional role who need to participate in Zoom meetings and webinars, and who may also be called on to host Zoom events. Overview In this course, you will participate in and host Zoom meetings to collaborate with others. You will: Use Zoom as a meeting participant. Use Zoom to host meetings. Customize Zoom settings. Manage Zoom contacts. With people transitioning to working remotely, virtual meetings have become the norm and, as a result, the Zoom video conferencing tool is gaining attention and usage. If Zoom has become part of your professional or personal life, this course will help you be a more confident and productive Zoom user. In this course, you will participate in and host Zoom meetings, use Zoom productivity tools such as breakout rooms and contacts, and apply Zoom security and personalization. Using Zoom as a Meeting Participant Topic A: Join a Zoom Meeting Topic B: Participate in a Zoom Meeting Topic C: Collaborate in a Meeting Using Zoom to Host Meetings Topic A: Schedule a Meeting Topic B: Host a Meeting Topic C: Use Breakout Rooms Topic D: Compare Meetings and Webinars Customizing Zoom Topic A: Customize Settings in the Zoom Web Portal Topic B: Customize Zoom Desktop Client Settings Managing Zoom Contacts Topic A: Add Zoom Contacts Topic B: Chat with Zoom Contacts

Duration 5 Days 30 CPD hours This course is intended for This course benefits individuals responsible for implementing, monitoring, and troubleshooting Layer 3 components of a service provider's network. Overview Describe the various OSPF link-state advertisement (LSA) types. Explain the flooding of LSAs in an OSPF network. Describe the shortest-path-first (SPF) algorithm. List key differences between OSPFv2 and OSPFv3. Describe OSPF area types and operations. Configure various OSPF area types. Summarize and restrict routes. Identify some scenarios in a service provider network that can be solved using routing policy or specific configuration options. Use routing policy and specific configuration options to implement solutions for various scenarios. Describe how to troubleshoot OSPF. Explain the concepts and operation of IS-IS. Describe various IS-IS link-state protocol data unit (LSP) types. List IS-IS adjacency rules and troubleshoot common adjacency issues. Configure and monitor IS-IS. Display and interpret the link-state database (LSDB). Perform advanced IS-IS configuration options. Implement IS-IS routing policy. Explain the default operation in multiarea IS-IS. Describe IS-IS address summarization methods. Configure and monitor a multiarea IS-IS network. Describe how to troubleshoot IS-IS. Describe basic BGP operation. List common BGP attributes. Explain the route selection process for BGP. Describe how to alter the route selection process. Configure some advanced options for BGP peers. Describe various BGP attributes in detail and explain the operation of those attributes. Manipulate BGP attributes using routing policy. Explain the causes for route instability. Describe the effect of damping on BGP routing. Explain the default behavior of damping on links. Control damping using routing policy. View damped routes using command-line interface (CLI) commands. Describe the operation of BGP route reflection. Configure a route reflector. Describe the operation of a BGP confederation. Configure confederations. Describe peering relationships in a confederation. Describe how to troubleshoot BGP. Describe how to troubleshoot routing policy. This five-day course is designed to provide students with detailed coverage of OSPF, IS-IS, BGP, and routing policy. Course Outline Course Introduction OSPF OSPFv2 Review Link-State Advertisements Protocol Operations OSPF Authentication OSPF Areas Review of OSPF Areas Stub Area Operation Stub Area Configuration NSSA Operation NSSA Configuration Route Summarization OSPF Case Studies and Solutions Virtual Links OSPF Multiarea Adjacencies External Reachability Troubleshooting OSPF Troubleshooting OSPF IS-IS Overview of IS-IS IS-IS PDUs Neighbors and Adjacencies Configuring and Monitoring IS-IS Advanced IS-IS Operations and Configuration Options IS-IS Operations IS-IS Configuration Options IS-IS Routing Policy Multilevel IS-IS Networks Level 1 and Level 2 Operations Multilevel Configuration Troubleshooting IS-IS Troubleshooting IS-IS BGP Review of BGP BGP Operations BGP Path Selection Options Configuration Options BGP Attributes and Policy?Part 1 BGP Policy Next Hop Origin and MED AS Path BGP Attributes and Policy?Part 2 Local Preference Communities Route Reflection and Confederations Route Reflection Operation Configuration and Routing Knowledge BGP Confederations BGP Route Damping Route Flap and Damping Overview Route Damping Parameters Configuring and Monitoring Route Damping Troubleshooting BGP Troubleshooting BGP Troubleshooting Policy Troubleshooting Policy

Duration 5 Days 30 CPD hours This course is intended for This course is for support staff for AIX on POWER systems Overview After completing this course, you should be able to: - Distinguish Korn and bash shell specific features - Use utilities such as sed and awk to manipulate data - Understand system shell scripts such as /etc/shutdown - Write useful shell scripts to aid system administration This course will teach you how to use shell scripts and utilities for practical system administration of AIX (or other UNIX) operating systems. Basic shell conceptsFlow control in a shell ScriptFunctions and typesetShell features such as arithmetic and string handlingUsing regular expressionsUsing sed, awk and other AIX utilities

Duration 1 Days 6 CPD hours This course is intended for This seminar is intended for individuals who want to gain intermediate knowledge of Sales. Overview Upon successful completion of this seminar, guests will gain intermediate knowledge of Sales Leadership and learning resource availability. In this seminar, guests will obtain knowledge in Sales Leadership, leveraging New Horizons' Leadership and Professional Development Program. Sales Leadership Session Sales Leadership Topics

Duration 5 Days 30 CPD hours This course is intended for This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. Lesson 1: Setting Up R and Executing Simple Code Topic A: Set Up the R Development Environment Topic B: Write R Statements Lesson 2: Processing Atomic Data Types Topic A: Process Characters Topic B: Process Numbers Topic C: Process Logicals Lesson 3: Processing Data Structures Topic A: Process Vectors Topic B: Process Factors Topic C: Process Data Frames Topic D: Subset Data Structures Lesson 4: Writing Conditional Statements and Loops Topic A: Write Conditional Statements Topic B: Write Loops Lesson 5: Structuring Code for Reuse Topic A: Define and Call Functions Topic B: Apply Loop Functions Topic C: Manage R Packages Lesson 6: Managing Data in R Topic A: Load Data Topic B: Save Data Topic C: Manipulate Data Frames Using Base R Topic D: Manipulate Data Frames Using dplyr Topic E: Handle Dates and Times Lesson 7: Analyzing Data in R Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Identify Missing Values Lesson 8: Visualizing Data in R Topic A: Plot Data Using Base R Functions Topic B: Plot Data Using ggplot2 Topic C: Format Plots in ggplot2 Topic D: Create Combination Plots Lesson 9: Modeling Data in R Topic A: Create Statistical Models in R Topic B: Create Machine Learning Models in R

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.

Duration 5 Days 30 CPD hours Overview By the end of the course, you should be able to meet the following objectives: Describe the architecture and main components of NSX Explain the features and benefits of NSX Deploy the NSX Management cluster and VMware NSX Edge™ nodes Prepare VMware ESXi™ hosts to participate in NSX networking Create and configure segments for layer 2 forwarding Create and configure Tier-0 and Tier-1 gateways for logical routing Use distributed and gateway firewall policies to filter east-west and north-south traffic in NSX Configure Advanced Threat Prevention features Configure network services on NSX Edge nodes Use VMware Identity Manager™ and LDAP to manage users and access Explain the use cases, importance, and architecture of Federation This five-day, fast-paced course provides comprehensive training to install, configure, and manage a VMware NSX© environment. This course covers key features and functionality offered in the NSX 4.0.0.1 and NSX 4.0.1 releases, including the overall infrastructure, logical switching, logical routing, networking and security services, firewalls and advanced threat prevention, and more. Course Introduction Introductions and course logistics Course objectives VMware Virtual Cloud Network and VMware NSX Introduce the VMware Virtual Cloud Network vision Describe the NSX product portfolio Discuss NSX features, use cases, and benefits Explain NSX architecture and components Explain the management, control, data, and consumption planes and their functions. Preparing the NSX Infrastructure Deploy VMware NSX© ManagerTM nodes on ESXi hypervisors Navigate through the NSX UI Explain data plane components such as N-VDS/VDS, transport nodes, transport zones, profiles, and more Perform transport node preparation and configure the data plane infrastructure Verify transport node status and connectivity Explain DPU-based acceleration in NSX Install NSX using DPUs NSX Logical Switching Introduce key components and terminology in logical switching Describe the function and types of L2 segments Explain tunneling and the Geneve encapsulation Configure logical segments and attach hosts using NSX UI Describe the function and types of segment profiles Create segment profiles and apply them to segments and ports Explain the function of MAC, ARP, and TEP tables used in packet forwarding Demonstrate L2 unicast packet flow Explain ARP suppression and BUM traffic handling NSX Logical Routing Describe the logical routing function and use cases Introduce the two-tier routing architecture, topologies, and components Explain the Tier-0 and Tier-1 gateway functions Describe the logical router components: Service Router and Distributed Router Discuss the architecture and function of NSX Edge nodes Discuss deployment options of NSX Edge nodes Configure NSX Edge nodes and create NSX Edge clusters Configure Tier-0 and Tier-1 gateways Examine single-tier and multitier packet flows Configure static routing and dynamic routing, including BGP and OSPF Enable ECMP on a Tier-0 gateway Describe NSX Edge HA, failure detection, and failback modes Configure VRF Lite NSX Bridging Describe the function of logical bridging Discuss the logical bridging use cases Compare routing and bridging solutions Explain the components of logical bridging Create bridge clusters and bridge profiles NSX Firewalls Describe NSX segmentation Identify the steps to enforce Zero-Trust with NSX segmentation Describe the Distributed Firewall architecture, components, and function Configure Distributed Firewall sections and rules Configure the Distributed Firewall on VDS Describe the Gateway Firewall architecture, components, and function Configure Gateway Firewall sections and rules NSX Advanced Threat Prevention Explain NSX IDS/IPS and its use cases Configure NSX IDS/IPS Deploy NSX Application Platform Identify the components and architecture of NSX Malware Prevention Configure NSX Malware Prevention for east-west and north-south traffic Describe the use cases and architecture of VMware NSX© Intelligence? Identify the components and architecture of VMware NSX© Network Detection and Response? Use NSX Network Detection and Response to analyze network traffic events. NSX Services Explain and configure Network Address Translation (NAT) Explain and configure DNS and DHCP services Describe VMware NSX© Advanced Load Balancer? architecture, components, topologies, and use cases. Configure NSX Advanced Load Balancer Discuss the IPSec VPN and L2 VPN function and use cases Configure IPSec VPN and L2 VPN using the NSX UI NSX User and Role Management Describe the function and benefits of VMware Identity Manager? in NSX Integrate VMware Identity Manager with NSX Integrate LDAP with NSX Identify the various types of users, authentication policies, and permissions Use role-based access control to restrict user access Explain object-based access control in NSX NSX Federation Introduce the NSX Federation key concepts, terminology, and use cases. Explain the onboarding process of NSX Federation Describe the NSX Federation switching and routing functions. Describe the NSX Federation security concepts.