1695 Computing courses in Frome delivered Live Online

Duration 5 Days 30 CPD hours This course is intended for Network Administrator Network Engineer Systems Engineer Consulting Systems Engineer Cisco Integrators/Partners Overview Upon completion of this course, you will be able to: Outline the troubleshooting process, and highlight which questions to ask Describe the troubleshooting tools and methodologies that are available from the CLI and are used to identify and resolve issues in a Cisco Data Center network architecture Identify and resolve issues related to VLANs and PVLANs Identify and resolve issues related to port channels and virtual port channels Identify and resolve issues related to Cisco FabricPath Identify and resolve issues related to OTV Identify and resolve issues related to VXLAN Identify and resolve issues related to LISP Describe troubleshooting of routing protocols, such as OSPF, ISIS, and PIM. Describe troubleshooting of the AAA and RBAC Identify and resolve issues related to a single device Identify and resolve issues related to Fibre Channel interface operation Identify and resolve issues related to Fibre Channel switching when the Cisco NX-OS software switch is used in switched mode (vs. NPV mode) Identify and resolve issues related to Fibre Channel switching when the NX-OS switch is used in N Port Virtualization (NPV) mode Identify and resolve issues related to FIP and FCoE, including FCoE performance Describe Cisco UCS architecture, initial setup, tools and service aids that are available for Cisco UCS troubleshooting and interpretation of the output Describe Cisco UCS configuration and troubleshoot related issues Describe Cisco UCS B-Series operation and troubleshoot related issues Describe LAN, SAN and Fibre Channel operations, including in depth troubleshooting procedures Describe Cisco IMC utilities to validate performance and facilitate data-gathering activities for Cisco UCS C-Series troubleshooting, as well troubleshooting approach to hardware and firmware failures Define proper procedures to configure LAN and SAN connectivity and avoid issues with the P81E virtual interface card (VIC) Troubleshoot integration of Cisco UCS C-Series servers with Cisco UCS Manager Identify tools, protocols and methods to effectively troubleshoot Cisco ACI The focus of this skills-building course is troubleshooting of LANs, SANs, Cisco Unified Fabric, Cisco Unified Computing System (UCS), and Cisco Application Centric Infrastructure (ACI). The course provides rich hands-on experience in resolving problems on Cisco MDS switches, Cisco Nexus switches, Cisco fabric extenders (FEXs), Cisco UCS, and Cisco ACI. Troubleshooting the Data Center LAN Network Overview of the Troubleshooting Process Understanding CLI Troubleshooting Tools Troubleshooting VLANs and Private VLANs Troubleshooting Port Channels and Virtual Port Channels Troubleshooting Cisco FabricPath Troubleshooting Cisco OTV Troubleshooting VXLAN Troubleshooting LISP Troubleshooting Routing Protocols Troubleshooting Data Center LAN Security Troubleshooting Platform-Specific Issues Troubleshooting Data Center SAN Troubleshooting Fibre Channel Interfaces Troubleshooting Fibre Channel Fabric Service Troubleshooting NPV Mode Troubleshooting FCoE Troubleshooting Data Center Unified Computing Troubleshooting Cisco UCS Architecture and Initialization Troubleshooting Cisco UCS Configuration Troubleshooting Cisco UCS B-Series Servers Troubleshooting Cisco UCS B-Series LAN and SAN Connectivity Troubleshooting Cisco UCS C-Series Servers Troubleshooting Cisco UCS C-Series LAN and SAN Connectivity Troubleshooting Cisco UCS C-Series and Cisco UCS Manager Integration Troubleshooting Data Center ACI Exploring the Tools and Methodology of Troubleshooting Cisco ACI Additional course details: Nexus Humans Cisco Troubleshooting Cisco Data Center Infrastructure v7.0 (DCIT) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Cisco Troubleshooting Cisco Data Center Infrastructure v7.0 (DCIT) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for The primary audience for this course are Application Consultants/Modelers and Project team members. Overview Get an overview of SAP HANA SPS09 and in-memory computing.Build an analytic data model with native HANA modeling tools.Understand the different approaches to provision data into SAP HANA.Learn how to connect to SAP HANA and consume HANA models. In this course, students get an overview of SAP HANA SPS09 and in-memory computing. Students will also gain an understanding of the different approaches to provision data into SAP HANA. Key concepts of SAP HANAWorking with SAP HANA StudioArchitecture of SAP in-memory computingModeling with SAP HANA Attribute Views Analytic Views Calculation Views Overview of data provisioning in SAP HANA with the tools Flat file upload SAP BusinessObjects Data Services SAP Landscape Transformation Replication Server SAP Replication Server SAP Direct Extractor Connection Smart Data Access Smart Data Integration / Smart Data Quality Smart Data Streaming SAP HANA Interfaces to BI client tools SAP BusinessObjects Analysis for Office SAP Design Studio SAP Lumira Additional course details: Nexus Humans HA100 SAP HANA - Introduction training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the HA100 SAP HANA - Introduction course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

SAP HANA Training | Online Courses | UK Provider Stay Ahead of the competition by gaining skills on SAP HANA with Osborne Training. SAP HANA training builds the foundation for seamless SAP applications, which helps deliver ground-breaking innovations without disruption. SAP HANA provides powerful features like significant processing speed, predictive capabilities, the ability to handle large amount of data, and text mining capabilities. SAP HANA course is designed to make you ready for SAP certification and Job market. Introduction In-Memory Computing Evolution of In-Memory computing at SAP History of SAP HANA HANA compare to BWA In-Memory Basics HANA Use cases Architecture Hana Engine Overview Different HANA Engine Types Row Store Column Store Persistency Layer Business Impact of new architecture Backup & Recovery Modeling Key Concepts in Data Modeling Components of HANA data model & Views Analytical ViewsAttribute viewsCalculation ViewsJoins Measures Filters Real Time Scenarios HANA SQL Intro Functions & Expressions Procedures Data Provisioning Overview Trigger Based Replication ETL Based Replication Log Based Replication Intro to BODS 4 Basic Data service Connection types Flat File upload in to HANA Reporting Connectivity options Business Objects BI 4 Security Creating Users Creating Roles Privileges User Administration

Duration 3 Days 18 CPD hours This course is intended for This course is designed for the following roles: System administrators Technical solutions architects Systems integrators Channel partners Value-added resellers Customer sales engineers DevOps engineers Sales engineers Systems engineers Technical solutions architects Overview This course will help you: Use SaaS or on-prem version of Cisco Intersight to enable IT organizations to analyze, simplify, and automate their environments in more advanced ways than the prior generations of tools Gain hands-on experience using Cisco Intersight Understand the X-Series, B-Series, and C-Series product line similarities and differences Describe Cisco Intersight and how it can be used to manage UCS and Cisco Hyperflex Understand the process for upgrading firmware with Cisco Intersight Administering server profiles, pools, and policies with Cisco Intersight Describe how to get started with Cisco Intersight programmability Cisco Intersight Overview (DCISO) v1.0 is a 3-day instructor led course that builds your experience with the administration of Cisco Unified Computing System (UCS) X-Series servers, including using Cisco Intersight for UCS management. This course covers architecture, configuration, and operation of Cisco Intersight©, and is designed to serve the needs of engineers seeking to understand the capabilities of Cisco Intersight for managing data centers from a single management platform. Course Outline Describing Cisco UCS Management in Cisco Intersight Describing the Cisco UCS Platform Describing Cisco Intersight Workloads Describing Automation Options Using Cisco Intersight

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 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

Duration 1 Days 6 CPD hours This course is intended for This course is intended for an individual who has experience developing and deploying basic business logic and user interfaces using the programmatic capabilities of the Force.com platform, including practical application of the skills and concepts noted in the exam objectives below. Overview When you complete this course, you will be able to:Describe the structure of the exam and the exam?s objectives.List the most heavily weighted exam objectives.Describe key features of the Force.com programming languages, including: Apex, SOQL, SOSL, and Visualforce.Explain important Force.com concepts, such as the testing framework, governor limits, and the save order of execution.Explain the ALM of a Salesforce development project. By covering the details around the exam objectives, this course will help hone your problem-solving skills and reinforce your knowledge of key topics. A mock exam at the end of the course will help you assess your readiness. Introduction to the Course and the Exam Introduction to AW computing Introduction to the Platform Developer I certification Salesforce FundamentalsLogic & Process Automation (Part 1) Working with Formulas and Rollup Summary Fields Working with Basic Constructs in Apex Working with SOQL Working with SOSL Working with DML Working with Exceptions and Governor Limits Logic & Process Automation (Part 2) Working with Apex classes Working with Apex triggers Describe the save order of execution User Interface Working with Visualforce Pages Working with Visualforce Controllers Working with the Lightning Component Framework Testing Describing the Testing Framework and Requirements Creating Test Data and Tests Executing a Test Testing Considerations Debug & Deployment Tools Debugging Deploying Certification Preparation Resources

Duration 4 Days 24 CPD hours This course is intended for Students in this course are interested in designing and implementing DevOps processes or in passing the Microsoft Azure DevOps Solutions certification exam. This course provides the knowledge and skills to design and implement DevOps processes and practices. Students will learn how to plan for DevOps, use source control, scale Git for an enterprise, consolidate artifacts, design a dependency management strategy, manage secrets, implement continuous integration, implement a container build strategy, design a release strategy, set up a release management workflow, implement a deployment pattern, and optimize feedback mechanisms Prerequisites Successful learners will have prior knowledge and understanding of: Cloud computing concepts, including an understanding of PaaS, SaaS, and IaaS implementations. Both Azure administration and Azure development with proven expertise in at least one of these areas. Version control, Agile software development, and core software development principles. It would be helpful to have experience in an organization that delivers software. AZ-104T00 - Microsoft Azure Administrator AZ-204T00: Developing Solutions for Microsoft Azure 1 - Introduction to DevOps What is DevOps? Explore the DevOps journey Identify transformation teams Explore shared goals and define timelines 2 - Choose the right project Explore greenfield and brownfield projects Decide when to use greenfield and brownfield projects Decide when to use systems of record versus systems of engagement Identify groups to minimize initial resistance Identify project metrics and key performance indicators (KPIs) 3 - Describe team structures Explore agile development practices Explore principles of agile development Define organization structure for agile practices Explore ideal DevOps team members Enable in-team and cross-team collaboration Select tools and processes for agile practices 4 - Choose the DevOps tools What is Azure DevOps? What is GitHub? Explore an authorization and access strategy Migrate or integrate existing work management tools Migrate or integrate existing test management tools Design a license management strategy 5 - Plan Agile with GitHub Projects and Azure Boards Link GitHub to Azure Boards Configure GitHub Projects Manage work with GitHub Project boards Customize Project views Collaborate using team discussions Agile Plan and Portfolio Management with Azure Boards 6 - Introduction to source control Explore DevOps foundational practices What is source control? Explore benefits of source control Explore best practices for source control 7 - Describe types of source control systems Understand centralized source control Understand distributed source control Explore Git and Team Foundation Version Control Examine and choose Git Understand objections to using Git Describe working with Git locally 8 - Work with Azure Repos and GitHub Migrate from TFVC to Git Use GIT-TFS Develop online with GitHub Codespaces 9 - Structure your Git Repo Explore monorepo versus multiple repos Implement a change log 10 - Manage Git branches and workflows Explore branch workflow types Explore feature branch workflow Explore Git branch model for continuous delivery Explore GitHub flow Explore fork workflow Version Control with Git in Azure Repos 11 - Collaborate with pull requests in Azure Repos Collaborate with pull requests Examine GitHub mobile for pull request approvals 12 - Identify technical debt Examine code quality Examine complexity and quality metrics Measure and manage technical debt Integrate other code quality tools Plan effective code reviews 13 - Explore Git hooks Implement Git hooks 14 - Plan foster inner source Explore foster inner source Implement the fork workflow Describe inner source with forks 15 - Manage Git repositories Work with large repositories Purge repository data Manage releases with GitHub Repos Automate release notes with GitHub 16 - Explore Azure Pipelines Explore the concept of pipelines in DevOps Describe Azure Pipelines Understand Azure Pipelines key terms 17 - Manage Azure Pipeline agents and pools Choose between Microsoft-hosted versus self-hosted agents Explore job types Explore predefined agent pool Understand typical situations for agent pools Communicate with Azure Pipelines Communicate to deploy to target servers Examine other considerations Describe security of agent pools Configure agent pools and understanding pipeline styles 18 - Describe pipelines and concurrency Understand parallel jobs Estimate parallel jobs Describe Azure Pipelines and open-source projects Explore Azure Pipelines and Visual Designer Describe Azure Pipelines and YAML 19 - Explore continuous integration Learn the four pillars of continuous integration Explore benefits of continuous integration Describe build properties Enable Continuous Integration with Azure Pipelines 20 - Implement a pipeline strategy Configure agent demands Implement multi-agent builds Explore source control types supported by Azure Pipelines 21 - Integrate with Azure Pipelines Describe the anatomy of a pipeline Understand the pipeline structure Detail templates Explore YAML resources Use multiple repositories in your pipeline 22 - Introduction to GitHub Actions What are Actions? Explore Actions flow Understand workflows Describe standard workflow syntax elements Explore events Explore jobs Explore runners Examine release and test an action 23 - Learn continuous integration with GitHub Actions Describe continuous integration with actions Examine environment variables Share artifacts between jobs Examine Workflow badges Describe best practices for creating actions Mark releases with Git tags Create encrypted secrets Use secrets in a workflow Implement GitHub Actions for CI/CD 24 - Design a container build strategy Examine structure of containers Work with Docker containers Understand Dockerfile core concepts Examine multi-stage dockerfiles Examine considerations for multiple stage builds Explore Azure container-related services Deploy Docker containers to Azure App Service web apps 25 - Introduction to continuous delivery Explore traditional IT development cycle What is continuous delivery? Move to continuous delivery Understand releases and deployments Understand release process versus release 26 - Create a release pipeline Describe Azure DevOps release pipeline capabilities Explore release pipelines Explore artifact sources Choose the appropriate artifact source Examine considerations for deployment to stages Explore build and release tasks Explore custom build and release tasks Explore release jobs Configure Pipelines as Code with YAML 27 - Explore release recommendations Understand the delivery cadence and three types of triggers Explore release approvals Explore release gates Use release gates to protect quality Control Deployments using Release Gates 28 - Provision and test environments Provision and configure target environments Configure automated integration and functional test automation Understand Shift-left Set up and run availability tests Explore Azure Load Testing Set up and run functional tests 29 - Manage and modularize tasks and templates Examine task groups Explore variables in release pipelines Understand variable groups 30 - Automate inspection of health Automate inspection of health Explore events and notifications Explore service hooks Configure Azure DevOps notifications Configure GitHub notifications Explore how to measure quality of your release process Examine release notes and documentation Examine considerations for choosing release management tools Explore common release management tools 31 - Introduction to deployment patterns Explore microservices architecture Examine classical deployment patterns Understand modern deployment patterns 32 - Implement blue-green deployment and feature toggles What is blue-green deployment? Explore deployment slots Describe feature toggle maintenance 33 - Implement canary releases and dark launching Explore canary releases Examine Traffic Manager Understand dark launching 34 - Implement A/B testing and progressive exposure deployment What is A/B testing? Explore CI-CD with deployment rings 35 - Integrate with identity management systems Integrate GitHub with single sign-on (SSO) Explore service principals Explore Managed Identity 36 - Manage application configuration data Rethink application configuration data Explore separation of concerns Understand external configuration store patterns Examine Key-value pairs Examine App configuration feature management Integrate Azure Key Vault with Azure Pipelines Manage secrets, tokens and certificates Examine DevOps inner and outer loop Integrate Azure Key Vault with Azure DevOps Enable Dynamic Configuration and Feature Flags 37 - Explore infrastructure as code and configuration management Explore environment deployment Examine environment configuration Understand imperative versus declarative configuration Understand idempotent configuration 38 - Create Azure resources using Azure Resource Manager templates Why use Azure Resource Manager templates? Explore template components Manage dependencies Modularize templates Manage secrets in templates Deployments using Azure Bicep templates 39 - Create Azure resources by using Azure CLI What is Azure CLI? Work with Azure CLI 40 - Explore Azure Automation with DevOps Create automation accounts What is a runbook? Understand automation shared resources Explore runbook gallery Examine webhooks Explore source control integration Explore PowerShell workflows Create a workflow Examine checkpoint and parallel processing 41 - Implement Desired State Configuration (DSC) Understand configuration drift Explore Desired State Configuration (DSC) Explore Azure Automation State configuration (DSC) Examine DSC configuration file Explore hybrid management Implement DSC and Linux Automation on Azure 42 - Implement Bicep What is Bicep? Install Bicep Understand Bicep file structure and syntax 43 - Introduction to Secure DevOps Describe SQL injection attack Understand DevSecOps Explore Secure DevOps Pipeline Explore key validation points Explore continuous security validation Understand threat modeling 44 - Implement open-source software Explore how software is built What is open-source software Explore corporate concerns with open-source software components Explore common open-source licenses Examine license implications and ratings 45 - Software Composition Analysis Inspect and validate code bases for compliance Explore software composition analysis (SCA) Integrate Mend with Azure Pipelines Implement GitHub Dependabot alerts and security updates Integrate software composition analysis checks into pipelines Examine tools for assess package security and license rate Interpret alerts from scanner tools Implement security and compliance in an Azure Pipeline 46 - Static analyzers Explore SonarCloud Explore CodeQL in GitHub Manage technical debt with SonarCloud and Azure DevOps 47 - OWASP and Dynamic Analyzers Plan Implement OWASP Secure Coding Practices Explore OWASP ZAP penetration test Explore OWASP ZAP results and bugs 48 - Security Monitoring and Governance Implement pipeline security Explore Microsoft Defender for Cloud Examine Microsoft Defender for Cloud usage scenarios Explore Azure Policy Understand policies Explore initiatives Explore resource locks Explore Azure Blueprints Understand Microsoft Defender for Identity 49 - Explore package dependencies What is dependency management? Describe elements of a dependency management strategy Identify dependencies Understand source and package componentization Decompose your system Scan your codebase for dependencies 50 - Understand package management Explore packages Understand package feeds Explore package feed managers Explore common public package sources Explore self-hosted and SaaS based package sources Consume packages Publish packages Package management with Azure Artifacts 51 - Migrate consolidating and secure artifacts Identify existing artifact repositories Migrate and integrating artifact repositories Secure access to package feeds Examine roles Examine permissions Examine authentication 52 - Implement a versioning strategy Understand versioning of artifacts Explore semantic versioning Examine release views Promote packages Explore best practices for versioning 53 - Introduction to GitHub Packages Publish packages Install a package Delete and restore a package Explore package access control and visibility 54 - Implement tools to track usage and flow Understand the inner loop Explore Azure Monitor and Log Analytics Examine Kusto Query Language (KQL) Explore Application Insights Implement Application Insights Monitor application performance with Application Insights 55 - Develop monitor and status dashboards Explore Azure Dashboards Examine view designer in Azure Monitor Explore Azure Monitor workbooks Explore Power BI Build your own custom application 56 - Share knowledge within teams Share acquired knowledge within development teams Integrate with Azure Boards Share team knowledge using Azure Project Wiki 57 - Design processes to automate application analytics Explore rapid responses and augmented search Integrate telemetry Examine monitoring tools and technologies 58 - Manage alerts, blameless retrospectives and a just culture Examine when get a notification Explore how to fix it Explore smart detection notifications Improve performance Understand server response time degradation Reduce meaningless and non-actionable alerts Examine blameless retrospective Develop a just culture

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

Duration 3 Days 18 CPD hours This course is intended for Anyone starting to write SAS programs Overview Use SAS Studio and SAS Enterprise Guide to write and submit SAS programs. Access SAS, Microsoft Excel, and text data. Explore and validate data. Prepare data by subsetting rows and computing new columns. Analyze and report on data. Export data and results to Excel, PDF, and other formats. Use SQL in SAS to query and join tables. This course is for users who want to learn how to write SAS programs to access, explore, prepare, and analyze data. It is the entry point to learning SAS programming for data science, machine learning, and artificial intelligence. Essentials The SAS programming process. Using SAS programming tools. Understanding SAS syntax. Accessing Data Understanding SAS data. Accessing data through libraries. Importing data into SAS. Exploring and Validating Data Exploring data. Filtering rows. Formatting columns. Sorting data and removing duplicates. Preparing Data Reading and filtering data. Computing new columns. Conditional processing. Analyzing and Reporting on Data Enhancing reports with titles, footnotes, and labels. Creating frequency reports. Creating summary statistics reports. Exporting Results Exporting data. Exporting reports. Using SQL in SAS Using Structured Query Language in SAS. Joining tables using SQL in SAS. Additional course details: Nexus Humans SAS Programming 1 - Essentials training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the SAS Programming 1 - Essentials course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.