1680 Guide courses delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for Experienced software developers who are already familiar with AWS services Overview In this course, you will learn how to: Analyze a monolithic application architecture to determine logical or programmatic break points where the application can be broken up across different AWS services Apply Twelve-Factor Application manifesto concepts and steps while migrating from a monolithic architecture Recommend the appropriate AWS services to develop a microservices based cloud-native application Use the AWS API, CLI, and SDKs to monitor and manage AWS services Migrate a monolithic application to a microservices application using the 6 Rs of migration Explain the SysOps and DevOps interdependencies necessary to deploy a microservices application in AWS The Advanced Developing on AWS course uses the real-world scenario of taking a legacy, on-premises monolithic application and refactoring it into a serverless microservices architecture. This three-day advanced course covers advanced development topics such as architecting for a cloud-native environment; deconstructing on-premises, legacy applications and repackaging them into cloud-based, cloud-native architectures; and applying the tenets of the Twelve-Factor Application methodology. Module 1: The cloud journey Common off-cloud architecture Introduction to Cloud Air Monolithic architecture Migration to the cloud Guardrails The six R?s of migration The Twelve-Factor Application Methodology Architectural styles and patterns Overview of AWS Services Interfacing with AWS Services Authentication Infrastructure as code and Elastic Beanstalk Demonstration: Walk through creating base infrastructure with AWS CloudFormation in the AWS console Hands-on lab 1: Deploy your monolith application using AWS Elastic Beanstalk Module 2: Gaining Agility DevOps CI/CD Application configuration Secrets management CI/CD Services in AWS Demonstration: Demo AWS Secrets Manager Module 3: Monolith to MicroServices Microservices Serverless A look at Cloud Air Microservices using Lambda and API Gateway SAM Strangling the Monolith Hands-on lab: Using AWS Lambda to develop microservices Module 4: Polyglot Persistence & Distributed Complexity Polyglot persistence DynamoDB best practices Distributed complexity Steps functions Module 5: Resilience and Scale Decentralized data stores Amazon SQS Amazon SNS Amazon Kinesis Streams AWS IoT Message Broker Serverless event bus Event sourcing and CQRS Designing for resilience in the cloud Hands-on lab: Exploring the AWS messaging options Module 6: Security and Observability Serverless Compute with AWS Lambda Authentication with Amazon Cognito Debugging and traceability Hands-on lab: Developing microservices on AWS Additional course details: Nexus Humans Advanced Developing on AWS 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 Advanced Developing on AWS 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 This in an introductory-level course geared for QA, Test team members and others who want to use the Python testing framework PyTest to implement code testing strategies. Attendees should have prior basic Python scripting experience. Students should have some familiarity with tools to be used in this course: PyCharm, Jupyter Notebook and basic GIT. Overview Working within in a hands-on learning environment students will learn to: Become proficient with pytest from day one by solving real-world testing problems Use pytest to write tests more efficiently Scale from simple to complex and functional testing Write and run simple and complex tests Organize tests in fles and directories Find out how to be more productive on the command line Markers and how to skip, xfail and parametrize tests Explore fxtures and techniques to use them effectively, such as tmpdir, pytestconfg, and monkeypatch Convert unittest suites to pytest using little-known techniques The pytest framework is simple to use but powerful enough to cover complex testing integration scenarios. PyTest is considered by many to be the true Pythonic approach to testing in Python. Geared for QA, Test team members and others who want to use the Python testing framework PyTest to implement code testing strategies, Test Automation with Python is a hands-on, two day Python testing course that provides students with the skills required to get started with PyTest right away. Participnats will learn how to get the most out of it in their daily workflow, exploring powerful mechanisms and plugins to facilitate many common testing tasks. Students will also learn how to use pytest in existing unittestbased test suites and will learn some tricks to make the jump to a pytest-style test suite quickly and easily. Python Refresher Python Overview Python Basics Python Lab Introducing PyTest Why Spend time writing test UnitTest Module Why PyTest? Introductory Lab Writing and Running Test Installing PyTest Writing and Running Tests Organizing files and packages Command Line options Configure pytest.ini Install and Config Lab Markers and Parameters Mark Basics Built-in marks Parameterization Markers and Parameters Lab Fixtures Introduction to Fixtures Sharing fixtures with conftest.py files Scopes Autouse Parameterizing fixtures Using marks from fixtures Built-in fixtures Best Practices Fixtures Lab Fixtures Lab 2 Plugins Finding and installing plugins Overview of plugins Plugin Lab From UnitTest to PyTest Use PyTest as a Test Runner Convert asserts with unitest2pytest Handling setup/teardown Managing test hierarchies Refactoring test utilities Migration strategies Additional course details: Nexus Humans Test Automation with Python (TTPS4832) 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 Test Automation with Python (TTPS4832) 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 This course assumes that you have successfully completed the Administering BIG-IP course, or equivalent, and have hands-on experience working in a production BIG-IP environment for several months. You should have a solid understanding of the environment in which the BIG-IP is deployed. This course is meant for BIG-IP administrators, network engineers, applications engineers, etc., who will be responsible for troubleshooting problems associated with their BIG-IP system. This course gives networking professionals hands-on knowledge of how to troubleshoot a BIG-IP system using a number of troubleshooting techniques as well as troubleshooting and system tools. This course includes lectures, labs, and discussions. Module 1: Setting Up the BIG-IP System Introducing the BIG-IP System Initially Setting Up the BIG-IP System Archiving the BIG-IP System Configuration Module 2: Reviewing Local Traffic Configuration Reviewing Nodes, Pools, and Virtual Servers Reviewing Address Translation Reviewing Routing Assumptions Reviewing Application Health Monitoring Reviewing Traffic Behavior Modification with Profiles Reviewing the TMOS Shell (TMSH) Reviewing Managing BIG-IP Configuration Data Reviewing High Availability (HA) Module 3: Troubleshooting Methodology Step-By-Step Process Step 1: State the Problem Step 2: Specify the Problem Step 3: Map the System Step 4: Develop Possible Causes Step 5: Test Theories Step 6: Iterate Until Root Cause Identified Documenting a Problem Putting Troubleshooting Steps to Use Module 4: Working with F5 Support Leveraging F5 Support Resources AskF5.com DevCentral iHealth Leveraging F5 Labs Working with F5 Technical Support Running End User Diagnostics (EUD) - Hardware Only New Platform Diagnostic Tools Always-On Management (AOM) Subsystem Requesting Return Materials Authorization F5?s Software Version Policy Managing the BIG-IP License for Upgrades Managing BIG-IP Disk Space Upgrading BIG-IP Software Module 5: Troubleshooting ? Bottom to Top Introducing Differences between BIG-IP and LINUX Tools Troubleshooting with Layer 1/Layer 2 Tools Troubleshooting with Layer 2/Layer 3 Tools Troubleshooting with Layer 3 Tools Troubleshooting with LINUX Tools Troubleshooting Memory and CPU Troubleshooting with watch Troubleshooting with Additional tmsh commands Module 6: Troubleshooting Tools tcpdump Wireshark ssldump Fiddler diff KDiff3 cURL Module 7: Using System Logs Configuring Logging Log Files Understanding BIG-IP Daemons Functions Triggering an iRule Deploying and Testing iRules Application Visibility and Reporting Module 8: Troubleshooting Lab Projects Network Configurations for Project Additional course details: Nexus Humans F5 Networks Troubleshooting BIG-IP 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 F5 Networks Troubleshooting BIG-IP 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 3 Days 18 CPD hours This course is intended for This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Introduction to Data Analysis Fundamentals of data analysis Statistical foundations Setting up a virtual environment Working with Pandas DataFrames Pandas data structures Bringing data into a pandas DataFrame Inspecting a DataFrame object Grabbing subsets of the data Adding and removing data Data Wrangling with Pandas What is data wrangling? Collecting temperature data Cleaning up the data Restructuring the data Handling duplicate, missing, or invalid data Aggregating Pandas DataFrames Database-style operations on DataFrames DataFrame operations Aggregations with pandas and numpy Time series Visualizing Data with Pandas and Matplotlib An introduction to matplotlib Plotting with pandas The pandas.plotting subpackage Plotting with Seaborn and Customization Techniques Utilizing seaborn for advanced plotting Formatting Customizing visualizations Financial Analysis - Bitcoin and the Stock Market Building a Python package Data extraction with pandas Exploratory data analysis Technical analysis of financial instruments Modeling performance Rule-Based Anomaly Detection Simulating login attempts Exploratory data analysis Rule-based anomaly detection Getting Started with Machine Learning in Python Learning the lingo Exploratory data analysis Preprocessing data Clustering Regression Classification Making Better Predictions - Optimizing Models Hyperparameter tuning with grid search Feature engineering Ensemble methods Inspecting classification prediction confidence Addressing class imbalance Regularization Machine Learning Anomaly Detection Exploring the data Unsupervised methods Supervised methods Online learning The Road Ahead Data resources Practicing working with data Python practice

Duration 2 Days 12 CPD hours This course is intended for Anyone who works with IBM SPSS Statistics and wants to learn advanced statistical procedures to be able to better answer research questions. Overview Introduction to advanced statistical analysis Group variables: Factor Analysis and Principal Components Analysis Group similar cases: Cluster Analysis Predict categorical targets with Nearest Neighbor Analysis Predict categorical targets with Discriminant Analysis Predict categorical targets with Logistic Regression Predict categorical targets with Decision Trees Introduction to Survival Analysis Introduction to Generalized Linear Models Introduction to Linear Mixed Models This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases. Introduction to advanced statistical analysis Taxonomy of models Overview of supervised models Overview of models to create natural groupings Group variables: Factor Analysis and Principal Components Analysis Factor Analysis basics Principal Components basics Assumptions of Factor Analysis Key issues in Factor Analysis Improve the interpretability Use Factor and component scores Group similar cases: Cluster Analysis Cluster Analysis basics Key issues in Cluster Analysis K-Means Cluster Analysis Assumptions of K-Means Cluster Analysis TwoStep Cluster Analysis Assumptions of TwoStep Cluster Analysis Predict categorical targets with Nearest Neighbor Analysis Nearest Neighbor Analysis basics Key issues in Nearest Neighbor Analysis Assess model fit Predict categorical targets with Discriminant Analysis Discriminant Analysis basics The Discriminant Analysis model Core concepts of Discriminant Analysis Classification of cases Assumptions of Discriminant Analysis Validate the solution Predict categorical targets with Logistic Regression Binary Logistic Regression basics The Binary Logistic Regression model Multinomial Logistic Regression basics Assumptions of Logistic Regression procedures Testing hypotheses Predict categorical targets with Decision Trees Decision Trees basics Validate the solution Explore CHAID Explore CRT Comparing Decision Trees methods Introduction to Survival Analysis Survival Analysis basics Kaplan-Meier Analysis Assumptions of Kaplan-Meier Analysis Cox Regression Assumptions of Cox Regression Introduction to Generalized Linear Models Generalized Linear Models basics Available distributions Available link functions Introduction to Linear Mixed Models Linear Mixed Models basics Hierachical Linear Models Modeling strategy Assumptions of Linear Mixed Models Additional course details: Nexus Humans 0G09A IBM Advanced Statistical Analysis Using IBM SPSS Statistics (v25) 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 0G09A IBM Advanced Statistical Analysis Using IBM SPSS Statistics (v25) 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 3 Days 18 CPD hours This course is intended for This course is designed for existing Python programmers who have at least one year of Python experience and who want to expand their programming proficiency in Python 3. Overview In this course, you will expand your Python proficiencies. You will: Select an object-oriented programming approach for Python applications. Create object-oriented Python applications. Create a desktop application. Create a data-driven application. Create and secure web service-connected applications. Program Python for data science. Implement unit testing and exception handling. Package an application for distribution.   Python continues to be a popular programming language, perhaps owing to its easy learning curve, small code footprint, and versatility for business, web, and scientific uses. Python is useful for developing custom software tools, applications, web services, and cloud applications. In this course, you'll build upon your basic Python skills, learning more advanced topics such as object-oriented programming patterns, development of graphical user interfaces, data management, creating web service-connected apps, performing data science tasks, unit testing, and creating and installing packages and executable applications. Selecting an Object-Oriented Programming Approach for Python Applications Topic A: Implement Object-Oriented Design Topic B: Leverage the Benefits of Object-Oriented Programming Creating Object-Oriented Python Applications Topic A: Create a Class Topic B: Use Built-in Methods Topic C: Implement the Factory Design Pattern Creating a Desktop Application Topic A: Design a Graphical User Interface (GUI) Topic B: Create Interactive Applications Creating Data-Driven Applications Topic A: Connect to Data Topic B: Store, Update, and Delete Data in a Database Creating and Securing a Web Service-Connected App Topic A: Select a Network Application Protocol Topic B: Create a RESTful Web Service Topic C: Create a Web Service Client Topic D: Secure Connected Applications Programming Python for Data Science Topic A: Clean Data with Python Topic B: Visualize Data with Python Topic C: Perform Linear Regression with Machine Learning Implementing Unit Testing and Exception Handling Topic A: Handle Exceptions Topic B: Write a Unit Test Topic C: Execute a Unit Test Packaging an Application for Distribution Topic A: Create and Install a Package Topic B: Generate Alternative Distribution Files Additional course details: Nexus Humans Advanced Programming Techniques with Python 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 Advanced Programming Techniques with Python 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 Data Protection Officers Data Protection Managers Auditors Legal Compliance Officers Security Manager Information Managers Anyone involved with data protection processes and programmes Overview Principles of Privacy Program Management is the how-to training on implementing a privacy program framework, managing the privacy program operational lifecycle and structuring a knowledgeable, high-performing privacy team. Those taking this course will learn the skills to manage privacy in an organization through process and technology?regardless of jurisdiction or industry. The Principles of Privacy Program Management training is based on the body of knowledge for the IAPP?s ANSI accredited Certified Information Privacy Manager (CIPM) certification program. Founded in 2000, the IAPP is the world?s largest and most comprehensive privacy resource with a mission to define, support and improve the Privacy profession globally. Every organization has data protection needs. Every day, we access, share and manage data across companies, continents and the globe. Knowing how to implement a privacy program is an invaluable skill that will help you protect your organization?s data?and take your career to the next level. Our Principles of Privacy Program Management training is the premier course on implementing a privacy program framework, managing the privacy program operational lifecycle and structuring a privacy team. Introduction to privacy program management Privacy program management responsibilities Accountability in privacy program management Privacy governance Considerations for developing and implementing a privacy program Position of the privacy function within an organization Role of the DPO Program scope and charter Privacy strategy Support and ongoing involvement of key functions and privacy frameworks Applicable laws and regulations The regulatory environment Common elements across jurisdictions Strategies for aligning compliance with organizational strategy Data assessments Practical processes for creating and using data inventories/maps Generating and applying gap analyses Privacy assessments Privacy impact assessments/data protection impact assessments Vendor assessments Policies Common types of privacy-related policies Policy components Strategies for implementation Data subject rights Operational considerations for communicating and ensuring data subject rights Privacy notice Choice and consent Access and rectification Data portability Erasure Training and awareness Developing privacy training and awareness programs Implementing privacy training and awareness programs Protecting personal information Holistic approach to protecting personal information Privacy by design Data breach incident plans Planning for a data security incident or breach Responding to a data security incident or breach Monitoring and auditing program performance Common practices for monitoring privacy program performance Measuring, analyzing and auditing privacy programs Additional course details: Nexus Humans Certified Information Privacy Manager (CIPM) 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 Certified Information Privacy Manager (CIPM) 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 Data Modelers Participants will learn the full scope of the metadata modeling process, from initial project creation, to publishing a dynamic cube, and enabling end users to easily author reports and analyze data. Introduction to IBM Cognos Dynamic Cubes Define and differentiate Dynamic Cubes Dynamic Cubes characteristics Examine Dynamic Cube requirements Examine Dynamic Cube components Examine high level architecture IBM Cognos Dynamic Query Review Dimensional Data Structures Dynamic Cubes caching Create & Design a Dynamic Cube Explore the IBM Cognos Cube Designer Review the cube development process Examine the Automatic Cube Generation Manual development overview Create dimensions Model the cube Best practice for effective modeling Deploy & Configure a Dynamic Cube Deploy a cube Explore the Estimate Hardware Requirements Identify cube management tasks Examine Query Service administration Explore Dynamic Cube properties Schedule cube actions Use the DCAdmin comment line tool Advanced Dynamic Cube Modelling Examine advanced modeling concepts Explore modeling caveats Calculated measures and members Model Relative Time Explore the Current Period property Define period aggregation rules for measures Advanced Features of Cube Designer Examine multilingual support Examine ragged hierarchies and padding members Define Parent-Child Dimensions Refresh Metadata Import Framework Manager packages Filter measures and dimensions Optimize Performance with Aggregates Identify aggregates and aggregate tables In-memory aggregates Use Aggregate Advisor to identify aggregates User defined in-memory aggregates Optimize In-Memory Aggregates automatically Aggregate Advisor recommendations Monitor Dynamic Cube performance Model aggregates (automatically vs manually) Use Slicers to define aggregation partitions Define Security Overview of Dynamic Cube security Identify security filters The Security process - Three steps Examine security scope Identify scope rules Identify roles Capabilities and access permissions Cube security deep dive Model a Virtual Cube Explore virtual cubes Create the virtual cube Explore virtual cube objects Examine virtual measures and calculated members Currency conversion using virtual cubes Security on virtual cubes Introduction to IBM Cognos Analytics Define IBM Cognos Analytics Redefined Business Intelligence Self-service Navigate to content in IBM Cognos Analytics Interact with the user interface Model data with IBM Cognos Analytics IBM Cognos Analytics components Create reports Perform self-service with analysis and Dashboards IBM Cognos Analytics architecture (high level) IBM Cognos Analytics security Package / data source relationship Create Data modules Upload files Additional course details: Nexus Humans B6063 IBM Cognos Cube Designer - Design Dynamic Cubes (v11.0) 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 B6063 IBM Cognos Cube Designer - Design Dynamic Cubes (v11.0) 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 introductory-level Python course is geared for experienced web developers new to Python who want to use Python and Django for full stack web development projects. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Develop full-stack web sites based on content stored in an RDMS Use python data types appropriately Define data models Understand the architecture of a Django-based web site Create Django templates for easy-to-modify views Map views to URLs Take advantage of the built-in Admin interface Provide HTML form processing Geared for experienced web developers new to Python, Introduction to Full Stack Web Development with Python and Django is a five-day hands-on course that teaches students how to develop Web applications using the Django framework. Students will explore the basics of creating basic applications using the MVC (model-view-controller) design pattern, as well as more advanced topics such as administration, session management, authentication, and automated testing. This comprehensive, practical course provides an in-depth exploration of working with the programming language, not an academic overview of syntax and grammar. Students will immediately be able to use Python to complete tasks in the real world. The Python Environment Starting Python Using the interpreter Running a Python script Getting help Editors and IDEs Getting Started Using variables Built in functions Strings Numbers Converting among types Writing to the screen Command line parameters Flow Control About flow control Conditional expressions Relational and Boolean operators while loops Lists and Tuples About sequences Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions Working with Files File overview The with statement Opening a file Reading/writing files Dictionaries and Sets About dictionaries Creating and using dictionaries About sets Creating and using sets Functions Returning values Function parameters Variable Scope Sorting with functions Errors and Exception Handling Exception overview Using try/catch/else/finally Handling multiple exceptions Ignoring exceptions Modules and Packages Creating Modules The import statement Module search path Creating packages Classes About OO programming Defining classes Constructors Properties Instance methods and data Class/static methods and data Inheritance Django Architecture Django overview Sites and apps Shared configuration Minimal Django layout Built in flexibility Configuring a Project Executing manage.py Starting the project Generating app files App configuration Database setup The development server Using cookiecutter Creating models Defining models Related objects SQL Migration Simplel model access Login for Nothing and Admin for Free Setting up the admin user Using the admin interface Views What is a view HttpResponse URL route configuration Shortcut: get_object_or_404() Class-based views Templates About templates Variable lookups The url tag Shortcut: render() Querying Models QuerySets Field lookups Chaining filters Slicing QuerySets Related fields Q objects Advanced Templates Use Comments Inheritance Filters Escaping HTML Custom filters Forms Forms overview GET and POST The Form class Processing the form Widgets Validation Forms in templates Automated Testing Why create tests? When to create tests Using Django's test framework Using the test client Running tests Checking code coverage

Duration 3 Days 18 CPD hours Overview In this course you?ll learn how to: Containerize and deploy a new Python script Configure the deployment with ConfigMaps, Secrets and SecurityContexts Understand multi-container pod design Configure probes for pod health Update and roll back an application Implement services and NetworkPolicies Use PersistentVolumeClaims for state persistence And more In this vendor agnostic course, you will use Python to build, monitor and troubleshoot scalable applications in Kubernetes. Introduction Objectives Who You Are The Linux Foundation Linux Foundation Training Preparing Your System Course Registration Labs Kubernetes Architecture What Is Kubernetes? Components of Kubernetes Challenges The Borg Heritage Kubernetes Architecture Terminology Master Node Minion (Worker) Nodes Pods Services Controllers Single IP per Pod Networking Setup CNI Network Configuration File Pod-to-Pod Communication Cloud Native Computing Foundation Resource Recommendations Labs Build Container Options Containerizing an Application Hosting a Local Repository Creating a Deployment Running Commands in a Container Multi-Container Pod readinessProbe livenessProbe Testing Labs Design Traditional Applications: Considerations Decoupled Resources Transience Flexible Framework Managing Resource Usage Multi-Container Pods Sidecar Container Adapter Container Ambassador Points to Ponder Labs Deployment Configuration Volumes Overview Introducing Volumes Volume Spec Volume Types Shared Volume Example Persistent Volumes and Claims Persistent Volume Persistent Volume Claim Dynamic Provisioning Secrets Using Secrets via Environment Variables Mounting Secrets as Volumes Portable Data with ConfigMaps Using ConfigMaps Deployment Configuration Status Scaling and Rolling Updates Deployment Rollbacks Jobs Labs Security Security Overview Accessing the API Authentication Authorization ABAC RBAC RBAC Process Overview Admission Controller Security Contexts Pod Security Policies Network Security Policies Network Security Policy Example Default Policy Example Labs Exposing Applications Service Types Services Diagram Service Update Pattern Accessing an Application with a Service Service without a Selector ClusterIP NodePort LoadBalancer ExternalName Ingress Resource Ingress Controller Labs Troubleshooting Troubleshotting Overview Basic Troubleshooting Steps Ongoing (Constant) Change Basic Troubleshooting Flow: Pods Basic Troubleshooting Flow: Node and Security Basic Troubleshooting Flow: Agents Monitoring Logging Tools Monitoring Applications System and Agent Logs Conformance Testing More Resource Labs Additional course details: Nexus Humans Kubernetes for App Developers 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 Kubernetes for App Developers 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.