8520 Courses

Duration 2 Days 12 CPD hours This course is intended for This course is designed for the Business Analyst professional who is involved with testing the functionality of technology projects. Overview Develop an understanding about basic concepts associated with User Acceptance TestingSee how UAT applies to the Software Development Lifecycle (SDLC)Recognize benefits of improved quality of deployed software using User Acceptance TestingIdentify the key roles, activities and deliverables which make up User Acceptance Testing Use a Business Use Case to define scenarios for testingCreate a UAT test plan and write UAT test cases with associated test dataUnderstand the process for testing functional and non-functional requirementsIdentify the challenges of testing vendor-supplied applications This course looks at the issues which drive the need for a UAT process & describes the components of the process. It is designed to help Business Analysts to develop an understanding of their role, the process, and the deliverables associated with UAT. Day 1 Software Testing - the Basics Understanding the Tester?s Terminology The UAT Planning Process Day 2 UAT Test Coverage Creating & Executing the UAT Test Cases Verifying the Test Results Testing Vendor-Supplied Applications Additional course details: Nexus Humans BA29 - User Acceptance Testing for Business Analysts 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 BA29 - User Acceptance Testing for Business Analysts 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 intermediate course is designed for anyone who works on WebSphere related applications and projects, including administrators, IBM Business Partners, independent software vendors (ISVs), and consultants. Overview The objectives for this course are as follows:Use IBM Support Assistant to organize and analyze problem artifactsUse problem determination techniques to identify common problemsApply problem investigation approaches such as analysis and isolationGather diagnostic data problem artifacts by using administrative toolsTroubleshoot JVM-related problems such as hung threads, out of memory issues, and crashesUse IBM Support Assistant to run tools that analyze diagnostic dataIdentify and troubleshoot common problems with database connectionsConfigure and tune database connection poolsTroubleshoot WebSphere security problems associated with authentication, authorization, SSL, and Java 2 policiesIdentify and resolve Java EE application deployment problemsTroubleshoot HTTP request flow problems from web server to web containerIdentify and resolve application server startup failuresTroubleshoot problems associated with WebSphere default messaging and SI busTroubleshoot WebSphere installation problemsUse Intelligent Management features to configure health policies and tasksCommunicate effectively with IBM support teams This course teaches you how to manage WebSphere Application Server problems more skillfully within your organization by using problem determination tools and techniques. Outline Course introduction Overview of WebSphere Application Server systems and components Using the IBM Support Assistant Team Server 5.0 Exercise: Using the IBM Support Assistant Team Server 5.0 Problem determination methods Gathering diagnostic data Exercise: Gathering diagnostic data Introduction to JVM-related problems Exercise: Introduction to configuring garbage collection policies How to troubleshoot hangs Exercise: Troubleshooting hung threads How to troubleshoot crashes Exercise: Troubleshooting crashes Introduction to WebSphere out-of-memory problems Exercise: Troubleshooting an out-of-memory condition Introduction to database connection problems Exercise: Troubleshooting database connection problems Tuning and connection pool management problems Exercise: Troubleshooting a connection leak WebSphere security configuration problems Exercise: Troubleshooting security problems Application deployment problems Server start failures Exercise: Troubleshooting server start failures Request flow and web container problems Exercise: Troubleshooting request flow and web container problems Default messaging provider problem determination Exercise: Troubleshooting WebSphere default messaging WebSphere installation problems when using IBM Installation Manager Intelligent Management problem determination and problem determination tools Exercise: Configuring health management policies Course summary

Duration 5 Days 30 CPD hours This course is intended for This class is designed for experienced BizTalk Server Developers who have at least one year of hands-on experience developing BizTalk Server applications. Overview In this 5-day course, you will learn how to apply best practices and design patterns to build smarter BizTalk Server applications. Furthermore, this course provides extensive coverage of BizTalk Server's extensibility, including such topics as: custom functoids, custom pipeline components, and invoking external .NET methods. This course is designed specifically for experienced BizTalk Server developers and focuses on best practices & pattern-based design while pulling back the curtain on some of BizTalk Server's eccentricities. Review of BizTalk Server Fundamentals The BizTalk Server Architecture Inner Workings of the Messaging Engine Messaging Engine Deep Dive Two-way Messaging Without Orchestrations Designing and Testing Schemas Schema Design Enabling Unit Testing for BizTalk Projects Data Translation and Transformation Custom Data Transformation Creating Custom Pipeline Components Working with Message Interchanges Debatching Message Interchanges Advanced Concepts of WCF Adapters Connecting to External Systems Using WCF LOB Adapters in BizTalk Server Publishing and Consuming WCF and RESTful Services Overview of Service Integration Using WCF Implementing WCF Services Preprocessing Messages with IIS Modules Consuming Services Advanced Orchestration Communication Patterns Orchestration Engine Deep Dive Splitting and Aggregating Messages using Orchestrations Orchestration Communication Bridging the Synchronous/Asynchronous Gap Across Multiple Channels Correlating Messages in Orchestration Instances Building Convoy Orchestrations Handling Orchestration Faults and Exceptions Exception Handling in Orchestrations Implementing Transactions and Compensation Creating Transactional Processes Designing Custom Tracking Models for BizTalk Applications Introduction to Business Activity Monitoring Enabling Business Activity Monitoring Extending BAM Beyond BizTalk Building Declarative Logic Using the Business Rules Engine Concepts of Declarative Logic Fundamentals of BizTalk BRE Integrating Policies with BizTalk Advanced Concepts of the Business Rules Engine Advanced Business Rule Concepts Working with Advanced Facts Integrating Across Business Boundaries Using Parties, Roles, and EDI Port Binding Option Review Role-Based Integration What is EDI? Enabling EDI-Based Messaging

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

Duration 1 Days 6 CPD hours This course is intended for This course is designed for business users, educators, students, and knowledge workers in a variety of roles and fields who want to be able to use the apps included in Google Workspace to create and manage various types of files and communicate and collaborate with colleagues. Overview In this course, you will use the various apps included in Google Workspace to work productively as part of a team. You will: Navigate the Google Workspace environment and use Gmail to send and manage email correspondence. Manage schedules using Google Calendar™. Communicate with colleagues over text, voice, and video using Google Chat and Google Meet. Store and share files using Google Drive. Collaborate on documents using Google Docs, Google Slides™, and Google Keep™. Collaborate on data using Google Sheets and Google Forms™. Collaborate on websites using Google Sites™. The core productivity apps that make up the Google Workspace? suite enable users to work together on a variety of projects and tasks across many different industries and job roles. This course will teach you how to work efficiently and effectively in apps like Gmail?, Google Drive?, Google Docs?, Google Sheets?, Google Meet?, Google Chat?, and more?all while participating in a collaborative team environment. Lesson 1:Getting Started with Google Workspace Topic A: Navigate Google Workspace Topic B: Send and Manage Email Using Gmail Lesson 2:Managing Schedules Using Google Calendar Topic A: Create and Manage Events Topic B: Customize Calendars Topic C: Create and Share Calendars Topic D: Create and Manage Tasks Lesson 3:Communicating Using Google Chat and Google Meet Topic A: Chat Using Google Chat Topic B: Participate in Meetings Using Google Meet Lesson 4:Storing and Sharing Files Using Google Drive Topic A: Add Files and Folders Topic B: Manage Files and Folders Lesson 5:Collaborating on Documents Using Docs, Slides, and Keep Topic A: Create and Edit Documents Using Google Docs Topic B: Design Presentations Using Google Slides Topic C: Take Notes Using Google Keep Lesson 6:Collaborating on Data Using Sheets and Forms Topic A: Create and Manage Spreadsheets Using Google Sheets Topic B: Design Surveys Using Google Forms Lesson 7:Collaborating on Websites Using Sites Topic A: Create and Edit Sites Topic B: Share and Publish Sites

Duration 3 Days 18 CPD hours This course is intended for This is an introductory-level course designed to teach experienced systems administrators how to install, maintain, monitor, troubleshoot, optimize, and secure Hadoop. Previous Hadoop experience is not required. Overview Working within in an engaging, hands-on learning environment, guided by our expert team, attendees will learn to: Understand the benefits of distributed computing Understand the Hadoop architecture (including HDFS and MapReduce) Define administrator participation in Big Data projects Plan, implement, and maintain Hadoop clusters Deploy and maintain additional Big Data tools (Pig, Hive, Flume, etc.) Plan, deploy and maintain HBase on a Hadoop cluster Monitor and maintain hundreds of servers Pinpoint performance bottlenecks and fix them Apache Hadoop is an open source framework for creating reliable and distributable compute clusters. Hadoop provides an excellent platform (with other related frameworks) to process large unstructured or semi-structured data sets from multiple sources to dissect, classify, learn from and make suggestions for business analytics, decision support, and other advanced forms of machine intelligence. This is an introductory-level, hands-on lab-intensive course geared for the administrator (new to Hadoop) who is charged with maintaining a Hadoop cluster and its related components. You will learn how to install, maintain, monitor, troubleshoot, optimize, and secure Hadoop. Introduction Hadoop history and concepts Ecosystem Distributions High level architecture Hadoop myths Hadoop challenges (hardware / software) Planning and installation Selecting software and Hadoop distributions Sizing the cluster and planning for growth Selecting hardware and network Rack topology Installation Multi-tenancy Directory structure and logs Benchmarking HDFS operations Concepts (horizontal scaling, replication, data locality, rack awareness) Nodes and daemons (NameNode, Secondary NameNode, HA Standby NameNode, DataNode) Health monitoring Command-line and browser-based administration Adding storage and replacing defective drives MapReduce operations Parallel computing before MapReduce: compare HPC versus Hadoop administration MapReduce cluster loads Nodes and Daemons (JobTracker, TaskTracker) MapReduce UI walk through MapReduce configuration Job config Job schedulers Administrator view of MapReduce best practices Optimizing MapReduce Fool proofing MR: what to tell your programmers YARN: architecture and use Advanced topics Hardware monitoring System software monitoring Hadoop cluster monitoring Adding and removing servers and upgrading Hadoop Backup, recovery, and business continuity planning Cluster configuration tweaks Hardware maintenance schedule Oozie scheduling for administrators Securing your cluster with Kerberos The future of Hadoop

Duration 5 Days 30 CPD hours This course is intended for This class is targeted towards the professional developer new to HTML, self-taught HTML developers, graphics designers and those new to HTML development. Overview After completing this course, you will be able to: Create HTML5 compliant web pages. Test and validate HTML and CSS code. Create CSS for style pages. Work with experimental vendor prefixes. Work with fonts and CSS font effects. Work with color and color tools. Layout pages and content using DIVs, iFrames and Tables. Add and format images and CSS sprites. Create HTML5 forms. Embed and manage video and audio content. This course is an in-depth hands-on study of HTML5, CSS3 and modern web and mobile development. The course includes detailed hands-on labs and Q&A labs. The labs include multiple projects, including one beginning to end web site.This material updates and replaces course Microsoft course 20480 which was previously published under the title Programming in HTML5 with JavaScript and CSS3. Module 1: A Brief History of HTML and the Web Welcome! History Details, Details, Details? The Life of a Web Page HTTP Status Codes Definitions Most Important Things to Know as a Web Developer Module 2: Core HTML Elements HTML and CSS Editors Text, Spaces and Tabs Working with Tags Attributes Comments Non-Standard Tags Every Page Includes? File Extensions Core Page Elements Nesting Testing HTML HTML and Text Module 3: Cascading Style Sheets Before CSS With CSS Cascading Style Sheets (CSS) Adding CSS to a Page Order of CSS Processing Experimental Vendor Prefixes CSS Units The CSS Box Mode Module 4: Fonts and Text Fonts CSS for Text CSS Text Ticks! Working with Lists Upgrading and to Windows 7 Module 5: Colors and Backgrounds Specifying Colors Applying Colors Gradients Module 6: Anchors and Hyperlinks HTML and CSS Hyperlinks with Images and Other Objects Buttons Module 7: Page Layout Page Layout Options Tables for Data DIVs Float SPAN HTML 5 DIV-like Tags IFRAMES Module 8: Images Favicon Preparing Images Image Files The IMG Tag Background Images Image Best Practices CSS Sprites Module 9: HTML Forms A Basic Form POST vs. GET name vs. id Basic Form Elements Basic Form Attribute Select Uploading Files HTML 5 Form Enhancements DataList Module 10: Multimedia Video and Audio HTML 5 Video CSS JavaScript Audio Hosting Videos in the Cloud Working with Animated GIFs

Duration 2 Days 12 CPD hours This course is intended for Those who need to understand the financial implications of their day-to-day decisions to increase the profitability and performance of their business. This course is suitable for managers with little or no financial knowledge. Overview Understanding of financial accounts and reports The ability to use and understanding of financial concepts Analytical skills to interpret financial results using ratios Ability to manage budgets more effectively This course shows how to interpret key financial statements highlighting the questions and areas that matter. It identifies warning signals that managers need to be aware of and shows how to understand key performance indicators to drive profitability. The course will also cover the essentials of budgeting and forecasting as well as addressing key financial terms such as goodwill and accruals & prepayments. Delegates will learn how to appraise capital projects with confidence, allowing them to make the best decisions for their business. Course Outline The basics of finance: How companies are financially structured Accrual v cash accounting The Business Cycle: understand how money flows in a business How businesses are financed: debt and equity Business objectives: using financial data to achieve business targets Key Financial Statements: Income Statement Balance Sheet Cash Flow Statement Key Financial Analysis Ratios Profitability: profit margins, EBIT & EBITDA, operational gearing Return on Investment: ROCE, ROA Leverage: financial gearing & interest cover Liquidity: current & quick ratios Cash Flow: working capital requirement Working capital management Cash flow management Key financial terms ? goodwill, accruals & prepayments, depreciation and amortization Cost analysis, control and reduction Capex v Opex Effective Budgeting and Forecasting to control the business Management Accounts and undertaking variance analysis Improving margins and sales in your business Break even analysis Capital Investment techniques ? NPV, IRR and discounted cash flows Asset Valuation Additional course details: Nexus Humans Finance for Non-Finance Managers (2 Day) 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 Finance for Non-Finance Managers (2 Day) 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 Individuals planning to deploy applications and create application environments on Google Cloud. Developers, systems operations professionals, and solution architects getting started with Google Cloud. Executives and business decision makers evaluating the potential of Google Cloud to address their business needs. Overview Identify the purpose and value of Google Cloud products and services. Interact with Google Cloud services. Describe ways in which customers have used Google Cloud. Choose among and use application deployment environments on Google Cloud: App Engine, Google Kubernetes Engine, and Compute Engine. Choose among and use Google Cloud storage options: Cloud Storage, Cloud SQL, Cloud Bigtable, and Firestore. Make basic use of BigQuery, Google's managed data warehouse for analytics. This course uses lectures, demos, and hands-on labs to give you an overview of Google Cloud products and services so that you can learn the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies. Introducing Google Cloud Platform Explain the advantages of Google Cloud Platform. Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones. Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS). Getting Started with Google Cloud Platform Identify the purpose of projects on Google Cloud Platform. Understand the purpose of and use cases for Identity and Access Management. List the methods of interacting with Google Cloud Platform. Lab: Getting Started with Google Cloud Platform. Google Compute Engine and Networking Identify the purpose of and use cases for Google Compute Engine. Understand the basics of networking in Google Cloud Platform. Lab: Deploying Applications Using Google Compute Engine. Google Cloud Platform Storage Options Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, and Google Cloud Bigtable. Learn how to choose between the various storage options on Google Cloud Platform. Lab: Integrating Applications with Google Cloud Storage. Google Container Engine Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Introduction to Hybrid and Multi-Cloud computing (Anthos). Lab: Deploying Applications Using Google Container Engine. Google App Engine and Google Cloud Datastore Understand the purpose of and use cases for Google App Engine and Google Cloud Datastore. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand the purpose of and use cases for Google Cloud Endpoints. Lab: Deploying Applications Using App Engine and Cloud Datastore. Deployment and Monitoring Understand the purpose of template-based creation and management of resources. Understand the purpose of integrated monitoring, alerting, and debugging. Lab: Getting Started with Stackdriver and Deployment Manager. Big Data and Machine Learning Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Lab: Getting Started with BigQuery. Summary and Review Summary and Review. What's Next?.

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Architects and operators who build and manage data analytics pipelines Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a batch data analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Introduction to Amazon EMR Using Amazon EMR in analytics solutions Amazon EMR cluster architecture Interactive Demo 1: Launching an Amazon EMR cluster Cost management strategies Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage Storage optimization with Amazon EMR Data ingestion techniques Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR Apache Spark on Amazon EMR use cases Why Apache Spark on Amazon EMR Spark concepts Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell Transformation, processing, and analytics Using notebooks with Amazon EMR Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive Using Amazon EMR with Hive to process batch data Transformation, processing, and analytics Practice Lab 2: Batch data processing using Amazon EMR with Hive Introduction to Apache HBase on Amazon EMR Module 5: Serverless Data Processing Serverless data processing, transformation, and analytics Using AWS Glue with Amazon EMR workloads Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions Module 6: Security and Monitoring of Amazon EMR Clusters Securing EMR clusters Interactive Demo 3: Client-side encryption with EMRFS Monitoring and troubleshooting Amazon EMR clusters Demo: Reviewing Apache Spark cluster history Module 7: Designing Batch Data Analytics Solutions Batch data analytics use cases Activity: Designing a batch data analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures