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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

Introduction Making a great first impression can be a game-changer, and your self-introduction plays a crucial role in this. It’s not just about stating who you are but presenting yourself in a way that captures attention and fosters connections. Whether you're stepping into a job interview or mingling at a party, a well-structured self-introduction can make all the difference. Understanding Your Audience Identifying Your Audience Before you craft your introduction, it's essential to understand who you're speaking to. Are you addressing colleagues in a corporate setting, potential clients, or new acquaintances at a social event? Knowing your audience helps tailor your message to their interests and expectations. Tailoring Your Message to the Audience Different audiences require different approaches. For a professional audience, focus on your career achievements and skills. For a social setting, you might emphasize hobbies or personal interests. Adapting your message ensures relevance and keeps your audience engaged. Crafting Your Introduction Key Elements of a Self-Introduction A solid introduction typically includes: Name and Current Role: Start with who you are and what you do. This sets the context for your audience. Relevant Background Information: Share a bit about your background that adds value to the conversation. Unique Selling Points: Highlight what makes you unique or what you bring to the table. Structuring Your Introduction for Clarity Organize your introduction in a logical flow. Start with your name, move on to your current role, share relevant background information, and end with your unique selling points. This structure ensures your introduction is clear and impactful. The Power of a Strong Opening Creating a Memorable Hook Capture your audience's attention right from the start. Use a compelling hook, such as an intriguing fact about yourself, a brief anecdote, or a relevant quote. A strong opening piques curiosity and draws your audience in. Using Anecdotes or Quotes Incorporating a brief story or a quote can make your introduction more engaging and memorable. Choose anecdotes that reflect your personality or achievements, and quotes that resonate with your message. Building Your Narrative Crafting a Compelling Story People connect with stories. Weave a narrative around your experiences, challenges, and successes. This approach makes your introduction more relatable and interesting. Highlighting Achievements and Experiences Focus on significant achievements and experiences that align with the context of your introduction. This helps establish credibility and shows what you bring to the table. Keeping It Concise Importance of Brevity While it’s tempting to share every detail, brevity is key. Aim for a concise introduction that covers the essentials without overwhelming your audience. A brief, well-crafted introduction is often more effective than a lengthy one. Techniques for Staying on Point To stay on point, practice your introduction and focus on the most relevant information. Avoid tangents and ensure every part of your introduction adds value. Engaging with Your Audience Asking Questions Engage your audience by asking questions or inviting them to share their thoughts. This interaction not only makes your introduction more engaging but also fosters a two-way conversation. Inviting Interaction Encourage dialogue by showing interest in your audience’s responses or reactions. This approach helps build connections and makes your introduction more dynamic. Non-Verbal Communication Body Language Tips Non-verbal cues play a significant role in communication. Maintain good posture, use open gestures, and avoid crossing your arms. Your body language should complement your verbal introduction. The Role of Eye Contact Maintaining eye contact shows confidence and helps build trust with your audience. It demonstrates engagement and sincerity in your introduction. Tailoring Your Introduction for Different Settings Professional Settings In professional settings, focus on your career achievements, skills, and professional interests. Be formal and concise, and ensure your introduction aligns with the context of the meeting or event. Social Gatherings For social gatherings, highlight personal interests, hobbies, or interesting facts about yourself. This helps create a friendly atmosphere and fosters personal connections. Virtual Introductions In virtual settings, ensure your introduction is clear and engaging despite the lack of physical presence. Use visual aids if possible, and maintain a positive and professional tone. Practicing Your Delivery Rehearsing Your Introduction Practice your introduction to build confidence and ensure smooth delivery. Rehearse in front of a mirror or with friends to refine your approach and timing. Adapting to Feedback Be open to feedback and adjust your introduction based on what resonates best with your audience. Continuous improvement helps keep your introduction fresh and effective. Common Mistakes to Avoid Overloading with Information Avoid overwhelming your audience with too much information. Focus on key points and keep your introduction relevant to the context. Being Too Vague or Generic Generic introductions can be forgettable. Be specific and highlight unique aspects of your background or achievements to stand out. Using Visual Aids Enhancing Your Introduction with Slides or Props In some settings, visual aids can enhance your introduction. Use slides or props to highlight key points or add visual interest. Ensure they support your message without overshadowing it. When and How to Use Visual Aids Effectively Only use visual aids when they add value to your introduction. Keep them simple and relevant, and practice using them smoothly in your delivery. The Role of Confidence Building Self-Confidence Confidence can be built through practice and preparation. Know your material, rehearse regularly, and adopt a positive mindset to boost your confidence. Projecting Confidence Through Speech Your tone, pace, and clarity of speech reflect your confidence. Speak clearly, at a steady pace, and with enthusiasm to convey confidence effectively. Feedback and Improvement Seeking Constructive Feedback Request feedback from trusted friends or colleagues to gain insights into how your introduction is received. Use this feedback to make improvements. Continuously Refining Your Introduction Refine your introduction based on feedback and personal experiences. Regular updates ensure your introduction remains relevant and impactful. Conclusion Crafting a powerful self-introduction is both an art and a skill. By understanding your audience, structuring your message clearly, and practicing your delivery, you can make a memorable first impression. Remember to tailor your introduction for different settings, engage with your audience, and continually refine your approach. With these strategies, you'll be well-equipped to introduce yourself effectively in any situation. FAQs 1. What should be included in a professional self-introduction? Include your name, current role, key achievements, and relevant skills. Tailor it to the context of the professional setting. 2. How long should my self-introduction be? Aim for 30-60 seconds. It should be concise yet comprehensive enough to convey your key points. 3. How can I make my introduction more engaging? Use a memorable hook, incorporate personal anecdotes, and engage your audience with questions or interactive elements. 4. What are common mistakes to avoid in a self-introduction? Avoid being too vague, overloading with information, or appearing uninterested. Focus on relevance and clarity. 5. How can I improve my self-introduction over time? Seek feedback, practice regularly, and refine your introduction based on experiences and audience reactions.

Are your sales people too stressed, running on adrenalin, perhaps driving towards burnout, struggling to reach targets? Wouldn't you rather they delivered consistently good performance, sustainable over longer periods of time, with less stress (for both them and you)? This uniquely empowering workshop will help your team develop naturally high levels of focus, energy and motivation. They will attain a sense of grounded euphoria, giving them a very distinct and ethical edge in selling. A one-day programme, it gives salespeople an introduction to the 'Natural Superheroes' concepts and resources, tools and techniques, to help them improve their sales performance - realising greater sales potential, developing deeper and more profitable client relationships, winning more business. Through this experience, sales teams gain: Information about profiling clients to instantly understand more about their deepest drives and true needs so they can be genuinely met and, where possible, exceeded Insights into deep, honest and very 'real' reasons for sales procrastination - participants are given specific workable strategies they can easily apply to overcome sales resistance, by tapping their natural motivational styles An understanding of communicating at the very highest levels with different people so they truly understand your sales message and have a significantly more positive sales experience A realisation of their very specific natural sales talents as individuals and as a team A deeper level of experience and understanding of what specifically drives their own behaviour and the behaviour of their clients - these unique insights explain not only how but exactly why people behave the way they do An experience of what it takes to be unshakeable under pressure and manage the sales processes and relationships to a positive outcome for all Brand-new insights into working with and handling difficult people across all levels of authority An understanding of the pitfalls and the psychological traps we set ourselves which cause unnecessary stress, anxiety and frustration on a daily basis and, in turn, limit our sales performance Access to very simple and practical tools that massively increase self-awareness, engender accountability and responsibility and develop emotional sales mastery 1 Introducing 'Natural Superheroes' for sales What is a 'Natural Superhero'? Defining emotional intelligence in the context of sales and why it is so important Knowing yourself - why most people don't know themselves at all and how we can understand exactly what drives our behaviour for the purpose of improving sales performance specifically Knowing exactly why others, and specifically clients and team members, behave the way they do - understanding the real motives behind people's good and bad behaviour in a sales meeting Simple steps to freeing yourself of any anxiety, pressure and false sense of limitation when selling Being yourself in sales - why this is not as easy as it sounds but how you can make it effortless How to take control of achieving the sales performance you really need and want for yourself and others Strategies that raise your self-awareness, increase an authentic and sustainable sense of self-confidence, in difficult situations and in moments of crisis 2 Understanding yourself, your team members and your customers - using the Enneagram Introducing the Enneagram and why it is so valuable to sales people and their clients Exploring the 9 types of motivational drives and why people have different reasons for buying from you Core types and wings - understanding the influence of other motivations either side of the core Enneagram type How to confirm the profile of your client - using celebrities from the world of politics, cinema, sports - we explore how to identify each type - what are their core drives, why have they arisen and how can we use these insights to help you in improving your sales performance The 3 levels of behaviour within your personal profile and that of your clients Lookalike Enneagram types - mistaken identities - how to avoid these traps when profiling your clients and your team How to communicate effectively in a sales environment with each of the different Enneagram types - communication strategies for positive impact on morale, performance and, ultimately, sales results How to interpret and make use of the results of your online personal profile - participants complete an online profile before the event and have the opportunity to analyse their results with a view to improving their sales performance How sales teams sabotage their own performance and that of other people within the team - and how to stop it Uncovering your particular edge in a sales role - what unconsciously trips you up as a sales person? How the Enneagram helps us in sustaining a truly great sales performance over time 3 Why positive thinking alone doesn't work in sales Why 'PMA' does not stand for positive mental attitude when selling - learn its alternative meaning that can serve you even more effectively in a sales role 3 steps and exercises that naturally increase PMA The value of making unconscious thinking conscious and how to do this without any pain or discomfort in a sales meeting Why each Enneagram type has a different experience of PMA in terms of their outward behaviour and how to know when you are maximising your sales performance 4 Measuring success How to measure the development of your individual profile as a sales person Development planning and review Into the future - how to continue your Natural Superhero development

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 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

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.

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 1 Days 6 CPD hours This course is intended for This basic course is for those who will be administering Information Server and its product components. Overview List Information Server functional categories and the Information Server products and components that support themList and describe the Information Server architectural tiersAccess Information Server clients, including thin clients using the Information Server Launch Pad, the Information Server Engine clients, and the Information Server Console clients including Information Analyzer and Information Services DirectorCreate and configure Information Server users and groupsManage Information Server active sessionsManage Information Server reportingWork with Information Server command-line tools including tools for session administration, user and group management, and encryptionUse the istool functionality to query, export, and import Information Server Repository assets This course gets those charged with administering Information Server v11.5 and its suite of many products and components started with the basic administrative tasks necessary to support Information Server users and developers. Information Server Technical Overview List the Information Server functional categories List the Information Server products and components that support these functional categories List the Information Server architectural tiers Working with Information Server Clients Use the Information Server Launch Pad to access Information Server thin clients including the Administrative Console, Information Governance Catalog, and Metadata Asset Manager Access Information Server Engine Clients including DataStage, QualityStage, FastTrack, and Information Server Manager Access Information Server Console Clients including Information Analyzer and Information Services Director Authentication and Suite Security Configure Suite users and groups Configure DataStage credentials for Engine users Session Management View a list of active sessions View session properties Disconnect sessions Configure global session properties Managing Reports Create and manage report folders Create a report Run a report View report results Administrative Tools Session Admin tool Directory Command tool Encrypt tool Managing Information Server Repository Assets Use istool to export and import common metadata assets Use istool to query information assets Use istool to export and import security assets Use istool to export and import reporting assets