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Duration 5 Days 30 CPD hours This course is intended for This course is geared toward Windows system administrators, network administrators, and other system administrators who are interested in supplementing current skills or backstopping other team members, in addition to Linux system administrators who are responsible for these tasks: Configuring, installing, upgrading, and maintaining Linux systems using established standards and procedures, Providing operational support, Managing systems for monitoring system performance and availability, Writing and deploying scripts for task automation and system administration. Overview #NAME? Red Hat System Administration I (RH124) equips you with Linux© administration "survival skills" by focusing on foundational Linux concepts and core tasks. You will learn how to apply command-line concepts and enterprise-level tools, starting you on your journey toward becoming a full-time Linux system administrator. This path continues with the follow-on course, Red Hat System Administration II (RH134). 1 - Getting Started with the GNOME Graphical Desktop Get started with GNOME and edit text files with gedit 2 - Manage Files Graphically with Nautilus Manage files graphically and access remote systems with Nautilus 3 - Get Help in a Graphical Environment Access documentation, both locally and online 4 - Configure Local Services Configure the date and time and configure a printer 5 - Manage Physical Storage Understand basic disk concepts and manage system disks 6 - Manage Logical Volumes Understand logical volume concepts and manage logical volumes 7 - Monitor System Resources Manage memory and CPU utilization 8 - Manage System Software Manage system software locally and using Red Hat Network (RHN) 9 - Get Started with Bash Understand basic shell concepts, execute simple commands, and use basic job control techniques 10 - Get Help in a Textual Environment Use man and info pages and find documentation in /usr/share/doc 11 - Establish Network Connectivity Understand basic network concepts; configure, manage, and test network settings 12 - Administer Users and Groups Manage users and groups 13 - Manage Files from the Command Line Understand Linux filesystem hierarchy; manage files from the command line 14 - Secure Linux File Access Understand Linux file access mechanisms; manage file access from the GUI and the command line 15 - Administer Remote Systems Share and connect to a desktop; use SSH and rsync 16 - Configure General Services Manage services; configure SSH and remote desktops 17 - Manage Physical Storage II Manage filesystem attributes and swap space 18 - Install Linux Graphically Install Red Hat Enterprise Linux and configure the system with firstboot 19 - Manage Virtual Machines Understand basic virtualization concepts; install and manage virtual machines 20 - Control the Boot Process Understand runlevels and manage GRUB 21 - Deploy File Sharing Services Deploy an FTP server and a web server 22 - Secure Network Services Manage a firewall; understand SELinux concepts and manage SELinux 23 - Comprehensive Review Get a hands-on review of the concepts covered throughout the course

Duration 5 Days 30 CPD hours This course is intended for Red Hat Certified System Administrator (RHCSA) who wants to learn how to provision and configure IdM technologies across both Linux and Windows applications Identity management specialist or engineer Access management specialist or engineer Web application developer DevOps specialist Overview As a result of attending this course, you will gain an understanding of the architecture of an identity management realm and trusted relationships using both Red Hat Enterprise Linux Identity Management and Microsoft Active Directory. You will be able to create, manage, and troubleshoot user management structures, security policies, local and remote secure access methods, and implementation technologies such as Kerberos, PKI, and certificates. You should be able to demonstrate these skills: Create and manage a scalable, resilient Identity Management realm, including both Linux and Microsoft Windows clients and servers. Create and manage secure access configurations, including managing and troubleshooting Kerberos, certificate servers, and access control policies. Integrate IdM as the back end for other major enterprise tools in the Red Hat portfolio, including Satellite Server and Tower. This course will empower you with the skills to configure and manage IdM, the comprehensive Identity Management solution bundled with Red Hat© Enterprise Linux.You will master these skills on the most requested Red Hat Identity Management (IdM) capabilities, including Active Directory trusts, multi-product federation, configuration management with Ansible, integrated certificate management, single sign-on, one-time passwords, and cybersecurity policy conformance. This course covers the same material as RH362, but includes the Red Hat Certified Specialist in Identity Management exam (EX362). Install Red Hat Identity Management Describe and install Red Hat Identity Management (IdM). Centralize Identity Management Explain the IdM server services, explore IdM clients access methods, and install an IdM client. Authenticate identities with Kerberos Define the Kerberos protocol and configure services for Kerberos authentication. Integrate IdM with Active Directory Create a trust relationship with Active Directory. Control user access Configure users for authorized access to services and resources. Manage a public key infrastructure Manage certificate authorities, certificates, and storing secrets. Maintain IdM operations Troubleshoot and recover Identity Management. Integrate Red Hat products with IdM Configure major services to share the IdM authentication database. Install scalable IdM Construct a resilient and scalable Identity Management topology.

Duration 5 Days 30 CPD hours This course is intended for This course is designed for application developers. Overview Deploy microservice applications on Red Hat© OpenShift Container Platform. Build a microservice application with Quarkus. Implement unit and integration tests for microservices. Use the config specification to inject data into a microservice. Secure a microservice using OAuth. Build and deploy native Quarkus applications. Develop microservice-based applications with Quarkus and OpenShift. Many enterprises are looking for a way to take advantage of cloud-native architectures, but many do not know the best approach. Quarkus is an exciting new technology that brings the reliability, familiarity, and maturity of Java Enterprise with a container-ready lightning fast deployment time. Red Hat Cloud-native Microservices Development with Quarkus (DO378) emphasizes learning architectural principles and implementing microservices based on Quarkus and OpenShift. You will build on application development fundamentals and focus on how to develop, monitor, test, and deploy modern microservices applications.. This course is based on OpenShift 4.5 and Quarkus 1.7L 1 - Describe microservice architectures Describe components and patterns of microservice-based application architectures. 2 - Implement a microservice with Quarkus Describe the specifications in Quarkus, implement a microservice with some of the specifications, and deploy it to an OpenShift cluster. 3 - Build microservice applications with Quarkus Build a persistent and configurable distributed quarkus microservices application. 4 - Implement fault tolerance Implement fault tolerance in a microservice architecture. 5 - Build and deploy native Quarkus applications Describe Quarkus in native mode and describe its deployment on OpenShift Container Platform. 6 - Test microservices Implement unit and integration tests for microservices. 7 - Create application health checks Create a health check for a microservice. 8 - Secure microservices Secure microservice endpoints and communication. 9 - Monitor microservices Monitor the operation of a microservice using metrics and distributed tracing. Additional course details: Nexus Humans Red Hat Cloud-native Microservices Development with Quarkus (DO378) 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 Red Hat Cloud-native Microservices Development with Quarkus (DO378) 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 5 Days 30 CPD hours This course is intended for This course is designed for Java developers who want to learn more about the specifications that comprise the world of Java Enterprise Edition (Java EE). Overview As a result of attending this course, you should be able to describe most of the specifications in Java EE 7 and create a component with each specification. You will be able to convert a Java SE program into a multi-tiered Java EE application. You should be able to demonstrate these skills: Describe the architecture of multi-tiered Java EE applications. Package Java EE applications and deploy to Red Hat JBoss Enterprise Application Platform with various tools. Create an Enterprise Java Bean instance. Manage the persistence of data using Java Persistence API. Create a web service using JAX-RS. Properly apply context scopes to beans and inject resources into Java Beans. Store and retrieve messages using the Java Messaging Service. Secure a Java EE application. Red Hat Application Development I: Programming in Java EE with exam (AD184) exposes experienced Java Standard Edition (Java SE) developers to the world of Java Enterprise Edition (Java EE). This course is based on Red Hat© Enterprise Application Platform 7.0. This course is a combination of Red Hat Application Development I: Programming in Java EE (AD183) and Red Hat Certified Enterprise Application Developer Exam (EX183). In this course, you will learn about the various specifications that make up Java EE. Through hands-on labs, you will transform a simple Java SE command line application into a multi-tiered enterprise application using various Java EE specifications, including Enterprise Java Beans, Java Persistence API, Java Messaging Service, JAX-RS for REST services, Contexts and Dependency Injection (CDI), and JAAS for securing the application. Transition to multi-tiered applications Describe Java EE features and distinguish between Java EE and Java SE applications. Package and deploying applications to an application server Describe the architecture of a Java EE application server, package an application, and deploy the application to an EAP server. Create Enterprise Java Beans Develop Enterprise Java Beans, including message-driven beans. Manage persistence Create persistence entities with validations. Manage entity relationships Define and manage JPA entity relationships. Create REST services Create REST APIs using the JAX-RS specification. Implement Contexts and Dependency Injection Describe typical use cases for using CDI and successfully implement it in an application. Create messaging applications with JMS Create messaging clients that send and receive messages using the JMS API. Secure Java EE applications Use JAAS to secure a Java EE application. Comprehensive review of Red Hat JBoss Development I: Java EE Demonstrate proficiency of the knowledge and skills obtained during the course. Additional course details: Nexus Humans Red Hat Application Development I: Programming in Java EE with exam (AD184) 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 Red Hat Application Development I: Programming in Java EE with exam (AD184) 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 1 Days 6 CPD hours This course is intended for This introductory-level course is great for experienced technical professionals working in a wide range of industries, such as software development, data science, marketing and advertising, finance, healthcare, and more, who are looking to use the latest AI and machine learning techniques in their day to day. The hands-on labs in this course use Python, so you should have some familiarity with Python scripting basics. Overview Working in an interactive learning environment, led by our engaging OpenAI expert you'll: Understand the capabilities and products offered by OpenAI and how to access them through the OpenAI API. set up an OpenAI environment on Azure, including creating an Azure virtual machine and configuring the environment to connect to Azure resources. Gain hands-on experience building a GPT-3 based chatbot on Azure and implement advanced natural language processing capabilities. Use the OpenAI API to access GPT-3 and generate high-quality text Learn how to use Whisper to improve the quality of text generation. Understand the capabilities of DALL-E and use it to generate images for unique and engaging visuals. Geared for technical professionals, Quick Start to Azure AI Basics for Technical Users is a fun, fast paced course designed to quickly get you up to speed with OpenAI?s powerful tools and functionality, and to provide hands-on experience in setting up an OpenAI environment on Azure. Guided by our AI expert, you?ll explore the capabilities of OpenAI's GPT-3, Whisper and DALL-E, and build a chatbot on Azure. It will provide you with the knowledge and resources to continue your journey in AI and machine learning and have a good understanding of the potential of OpenAI and Azure for your projects. First, you?ll dive into the world of OpenAI, learning about its products and the capabilities they offer. You'll also discover how Azure's offerings for AI and machine learning can complement OpenAI's tools and resources, providing you with a powerful combination for your projects. And don't worry if you're new to Azure, we'll walk you through the process of setting up an account and creating a resource group. As you progress through the course, you'll get the chance to work with OpenAI's GPT-3, one of the most advanced large language models available today. You'll learn how to use the OpenAI API to access GPT-3 and discover how to use it to generate high-quality text quickly and easily. And that's not all, you'll also learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to implement advanced natural language processing capabilities in your chatbot projects. The course will also cover OpenAI Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. And you will learn about OpenAI DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. Introduction to OpenAI and Azure Explore OpenAI and its products, as well as Azure's offerings for AI and Machine Learning, allowing you to understand the tools and resources available to you for your AI projects. Explore OpenAI and its products Explore Azure and its offerings for AI and Machine Learning Get Hands-On: Setting up an OpenAI environment on Azure Walk through the process of setting up an OpenAI environment on Azure, giving you the hands-on experience needed to start building your own projects using OpenAI and Azure. Create an Azure virtual machine and installing the OpenAI SDK Configure the OpenAI environment and connecting to Azure resources Explore OpenAI GPT-3 Learn about GPT-3, one of OpenAI's most powerful language models, and how to use it to generate high quality text, giving you the ability to create natural language content quickly and easily. Review GPT-3 and its capabilities Use the OpenAI API to access GPT-3 Get Hands-on: Building a GPT-3 based chatbot on Azure Learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to learn how to implement advanced natural language processing capabilities in your chatbot projects. Setup an Azure Function and creating a chatbot Integrate GPT-3 with the chatbot OpenAI Whisper Explore Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. Explore Whisper and its capabilities Use Whisper to improve the quality of text generation OpenAI DALL-E Explore DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. Explore DALL-E and its capabilities Use the OpenAI API to access DALL-E What?s Next: Keep Going! Other ways OpenAI can impact your day to day Explore great places to check for expanded tools and add-ons for Azure OpenAI Where to go for help and support Quick Look at Generative AI and its Business Implications Understanding Generative AI Generative AI in Business Ethical considerations of Generative AI

Duration 1 Days 6 CPD hours This course is intended for This basic course is for: Business Analyst Systems Engineer Software Engineer Requirements Engineer Requirements Manager Requirements Team Leader Overview Build projects in DOORS, including defining data structure, linking schema, attributes, and access permissions Use DOORS external linking facilities Share DOORS information with 3rd parties Control the flow of changes through your DOORS database Apply configuration management and backup strategies to your DOORS data This course builds on the content learned in the IBM Engineering Requirements Management DOORS V9.6 Foundation course. It is designed for those who will be in the role of team lead or project manager, or who want to learn more about advanced DOORS end-user functionality. It discusses creating and structuring DOORS projects, defining linking relationships and attributes, setting access permissions, and managing change. It also discusses external linking, working with spreadsheets, and applying configuration management strategies to DOORS data. Course Outline Build projects in DOORS, including defining data structure, linking schema, attributes, and access permissions Use DOORS external linking facilities Share DOORS information with 3rd parties Control the flow of changes through your DOORS database Apply configuration management and backup strategies to your DOORS data

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 If you want to advance from being a front-end developer to a full-stack developer and learn how Node.js can be used for hosting full-stack applications, this course is for you. Knowledge of JavaScript's basic syntax and experience with popular front-end libraries such as jQuery is required. You should also have used JavaScript with HTML and CSS, but not necessarily Node.js. Overview By the end of this course, you'll have the skills you need to tackle any real-world JavaScript development problem using a modern JavaScript approach, both for client and server sides.After completing this course, you will be able to: Apply the core concepts of functional programming Build a Node.js project that uses the Express.js library to host an API Create unit tests for a Node.js project to validate it Use the Cheerio library with Node.js to create a basic web scraper Develop a React interface to build processing flows Use callbacks as a basic way to bring control back This is your one-stop solution to mastering modern JavaScript. This course covers the latest features of JavaScript and advanced concepts, such as modularity, testing, and asynchronous programming. By the end of the course, you?ll know how to create a full-stack JavaScript application using NodeJS and how to use JavaScript in functional programming. JavaScript, HTML, and the DOM HTML and the DOM Developer Tools Node.js and npm What is Node.js? Node Version Manager (nvm) Node Package Manager (npm) Node.js APIs and Web Scraping Globals FileSystem APIs HTTP APIs What is Scraping? RESTful APIs with Node.js What is an API? What is REST? Useful Defaults and Easy Inputs Middleware The Contents of a JWT MongoDB Modular JavaScript ES6 Modules Object-Oriented Programming (OOP) npm Package? Code Quality Clear Naming Unit Tests Integration Tests End-to-End Testing Puppeteer Advanced JavaScript Language Features Supported in ES5, ES6, ES7, ES8, and ES9 OOP in JavaScript Sorting Maps and Sets Math, Date, and String Symbols, Iterators, Generators, and Proxies Asynchronous Programming Callback Hell Async and Await Event-Driven Programming and Built-In Modules Eventing Node.js Built-In Modules Handling Large Files in Node.js Functional Programming with JavaScript Functions ? First-Class Citizens Pure Functions Higher-Order Functions Function Composition Immutability and Side Effects Introduction to GraphQL Language Schemas and Queries

Duration 5 Days 30 CPD hours This course is intended for Security-operations (SecOps), or security, orchestration, automation, and response (SOAR) engineers, managed security service providers (MSSPs), service delivery partners, system integrators, and professional services engineers Overview This training is designed to enable a SOC, CERT, CSIRT, or SOAR engineer to start working with Cortex XSOAR integrations, playbooks, incident-page layouts, and other system features to facilitate resource orchestration, process automation, case management, and analyst workflow.The course includes coverage of a complete playbook-development process for automating a typical analyst workflow to address phishing incidents. This end-to-end view of the development process provides a framework for more focused discussions of individual topics that are covered in the course. The Cortex? XSOAR 6.2: Automation and Orchestration (EDU-380) course is four days of instructor-led training that will help you: Configure integrations, create tasks, and develop playbooks.Build incident layouts that enable analysts to triage and investigate incidents efficientlyIdentify how to categorize event information and map that information to display fields.Develop automations, manage content, indicator data, and artifact stores, schedule jobs, organize users and user roles, oversee case management, and foster collaboration Course Outline 1 - Core functionality and Feature Sets 2 - Enabling and Configuring Integrations 3 - Playbook Development 4 - Classification and Mapping 5 - Layout Builder 6 - Solution Architecture - Docker 8 - Automation Development & Debugging 9 - Content Management 10 - Indicators 11 - Jobs and Job Scheduling 12 - Users and Role Management 13 - Integration Development Additional course details: Nexus Humans Palo Alto Networks : Cortex XSOAR 6.8: Automation and Orchestration (EDU-380) 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 Palo Alto Networks : Cortex XSOAR 6.8: Automation and Orchestration (EDU-380) 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.