3193 Courses

Master Go programming with an in-depth course covering advanced topics such as authentication, authorization, JWT tokens, and refresh tokens. Learn how to write reliable code with effective unit testing techniques, while exploring concepts such as logging, error handling, and modularization. Build secure and scalable web applications with Go to take your coding expertise to the next level.

This Microsoft Office 2007 Excel Intermediate will provide you with experience at creating more elaborate worksheet and workbooks n Microsoft Office Excel 2007 to display data in more effective ways. In this series you will work with multiple worksheets and workbooks, you will learn how to switch between workbooks and even copy worksheets, you will also get 3D formulas: 3D formulas will allow you to link the worksheets, and also add something call the Watch-Window. You will learn to create and manage all these linked workbooks as well as creating workspaces which will allow you to manage the workbook as well. Before getting this course you must have the basic skills of Microsoft Excel 2007. This level will give you an advanced knowledge you need to know about Microsoft Excel. Who is this Course for? Microsoft Office 2007 Excel Intermediate is perfect for anyone trying to learn potential professional skills. As there is no experience and qualification required for this course, it is available for all students from any academic background. Entry Requirement: This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Course Curriculum Using Multiple Worksheets and Workbooks Using Multiple Workbooks 00:04:00 Linking Worksheets with 3-D Formulas 00:06:00 Linking Workbooks 00:11:00 Managing Workbooks 00:04:00 Advanced Formatting Using Special Number Formats 00:17:00 Using Functions to Format Text 00:13:00 Working with Styles 00:07:00 Working with Themes 00:11:00 Other Advanced Formatting 00:13:00 Outlining and Subtotals Outlining and Consolidating Data 00:11:00 Creating Subtotals 00:06:00 Cell and Range Names Creating and Using Names 00:12:00 Managing Names 00:04:00 Lists and Tables Examining Lists 00:03:00 Sorting and Filtering Lists 00:07:00 Advanced Filtering 00:12:00 Working with Tables 00:22:00 Web and Internet Features Saving Workbooks as Web Pages 00:11:00 Using Hyperlinks 00:04:00 Distributing Workbooks 00:03:00 Advanced Charting Chart Formatting Options 00:08:00 Combination Charts 00:05:00 Graphic Elements 00:09:00 Documenting and Auditing Auditing Features 00:05:00 Creating A Body Of Work 00:03:00 Protection 00:05:00 Workgroup Collaboration 00:13:00 Templates and Settings Application Settings 00:05:00 Built-in Templates 00:07:00 Creating and Managing Templates 00:10:00 Certificate and Transcript Order Your Certificates or Transcripts 00:00:00

Learn to build a complete Arduino project using a keypad, LCD, ultrasonic sensor, LDR sensor, and a buzzer

Overview This comprehensive course on Data Science & Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with Python comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Data Science & Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Data Science & Machine Learning with Python is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 2 sections • 90 lectures • 10:24:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:08:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:07:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

Duration 5 Days 30 CPD hours This course is intended for Cloud architects, systems engineers, datacenter administrators, and cloud administrators with experience in managed services or managing a service provider environment. Overview By the end of the course, you should be able to meet the following objectives: Understanding NSX-T Data Center and VMware Cloud Director fundamentals List the various NSX-T Data Center components List the use cases and topologies of VMware Cloud Director networks Discuss the VMware Cloud Director provider networking configurations Discuss the VMware Cloud Director tenant networking configurations Discuss NSX Advanced Load Balancer and its components Discuss NSX Advanced Load Balancer configuration and integration with VMware Cloud Director Enable a layer 2 stretched network that spans across multiple VMware Cloud organization VDCs Showcase the importance of the VMware Cloud Director migration tool and its functionality Manage resources from the VMWare Cloud Director console and use VMware vRealize Operations Manager™ In this five-day course, you focus on designing, integrating, configuring, and managing VMware Cloud Director? 10.x with VMware NSX-T© Data Center and VMware NSX© Advanced Load Balancer?. You learn about creating Network Pools and Edge Cluster management. You also learn about creating and managing an external network and creating and managing the organization of VDC, vApp, and data center group networks. Course Introduction Introductions and course logistics Course objectives VMware Cloud Director Data Center Fundamentals Explain NSX-T Data Center and its architecture Discuss various NSX-T Data Center components Describe NSX-T Data Center objects and their creation methods List the various requirement and maximum configuration details Explain NSX-T Data Center and its architecture Discuss various NSX-T Data Center components Describe NSX-T Data Center objects and their creation methods Explain VMware Cloud Director and its architecture List the various requirement and maximum configuration details Discuss VMware Cloud Director pre-requisites and deployment Discuss the use cases and features of VMware Cloud Director List the various requirement and maximum configuration details Discuss the design best practices Provider Configuration Discuss the integration of VMware vCenter Server© with VMware Cloud Director Discuss the integration of NSX-T Data Center with VMware Cloud Director Explain the provider side networking concepts and features Tenant Configuration Discuss the various types of organization VDC networks that can be created using NSX-T Data Center Explain the edge services available under each type of Organization VDC networks Explain what vApp network is Discuss various types of vApp networks Cross VDC networking with NSX-T Explain data center groups Configure cross-VDC networking and L2 stretched networks Configure a distributed firewall for a Data Center Group NSX Advanced Load Balancer Describe the NSX Advanced Load Balancer components and main functions Explain the NSX Advanced Load Balancer key features and benefits Understand and apply a Global Server Load Balancing design framework VMware NSX Migration for VMware Cloud Director Understand the main usage and purpose of the NSX migration for VMware Cloud Director List the supported topology and compatibility matrix List the supported features Understand the environmental prerequisites and how to prepare the edge cluster for bridging Know the logs and error handling exceptions Monitoring VMware Cloud Director Networking Discuss the methods to manage and monitor networking objects from VMware Cloud Director portals Understand the vRealize Operations Manager and vRealize Operations Manager Tenant App overview Discuss how to monitor VMware Cloud Director networking objects using vRealize Operations Manager and vRealize Operations Manager Tenant App Create views and reports Describe the use of vCloud Usage Meter with VMware Cloud Director

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 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 5 Days 30 CPD hours This course is intended for Typical candidates for this course are IT Professionals who deploy small-to-medium scale enterprise network solutions based on Aruba products and technologies Overview After you successfully complete this course, expect to be able to: Explain how Aruba's wireless networking solutions meet customers' requirements Explain fundamental WLAN technologies, RF concepts, and 802.11 Standards Learn to configure the Mobility Master and Mobility Controller to control access to the Employee and Guest WLAN Control secure access to the WLAN using Aruba Firewall Policies and Roles Recognize and explain Radio Frequency Bands and channels, and the standards used to regulate them Describe the concept of radio frequency coverage and interference and successful implementation and diagnosis of WLAN systems Identify and differentiate antenna technology options to ensure optimal coverage in various deployment scenarios Describe RF power technology including, signal strength, how it is measured and why it is critical in designing wireless networks Learn to configure and optimize Aruba ARM and Client Match features Learn how to perform network monitoring functions and troubleshooting AR-AMF teaches knowledge, skills & practical exp. to set up & config a basic AR WLAN utilizing OS 8.X architecture & features.using lecture & labs,AR-AMF provides tech. & hands-on exp. of config. a single Mobility Master with 1 controller & AP WLAN WLAN Fundamentals Describes the fundamentals of 802.11, RF frequencies and channels Explain RF Patterns and coverage including SNR Roaming Standards and QOS requirements Mobile First Architecture An introduction to Aruba Products including controller types and modes OS 8.X Architecture and features License types and distribution Mobility Master Mobility Controller Configuration Understanding Groups and Subgroups Different methods to join MC with MM Understanding Hierarchical Configuration Secure WLAN configuration Identifying WLAN requirements such as SSID name, encryption, authentication Explain AP groups structure and profiles Configuration of WLAN using the Mobility Master GUI AP Provisioning Describes the communication between AP and Mobility controller Explain the AP booting sequence and requirements Explores the APs controller discovery mechanisms Explains how to secure AP to controller communication using CPSec Describes AP provisioning and operations WLAN Security Describes the 802.11 discovery, authentication and association Explores the various authentication methods, 802.1x with WPA/WPA2, Mac auth Describes the authentication server communication Explains symmetric vs asymmetric Keys, encryption methods WIPS is described along with rogue discovery and protection Firewall Roles and Policies An introduction into Firewall Roles and policies Explains Aruba?s Identity based Firewall Configuration of Policies and Rules including aliases Explains how to assign Roles to users Dynamic RF Management Explain how ARM calibrates the network selecting channels and power settings Explores the new OS 8.X Airmatch to calibrate the network How Client Match and Client Insight match steers clients to better Aps Dynamic RF Management Explain how ARM calibrates the network selecting channels and power settings Explores the new OS 8.X Airmatch to calibrate the network How Client Match and Client Insight match steers clients to better Aps Guest Access Introduces Aruba?s solutions for Guest Access and the Captive portal process Configuration of secure guest access using the internal Captive portal The configuration of Captive portal using Clearpass and its benefits Creating a guest provisioning account Troubleshooting guest access Network Monitoring and Troubleshooting Using the MM dashboard to monitor and diagnose client, WLAN and AP issues Traffic analysis using APPrf with filtering capabilities A view of Airwaves capabilities for monitoring and diagnosing client, WLAN and AP issues

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 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.