144 Courses in Cardiff delivered Live Online

Data dashboards provide key information to stakeholders so that they can make informed decisions. While there are plenty of software solutions for building these essential data products, there is much less guidance on how to design dashboards to meet the diverse needs of users.  This course is for anyone who is building or implementing dashboards, and wants to know more about design principles and best practice. You could be using business intelligence software (such as Power BI or Tableau), or implementing bespoke solutions.  The course will give your team the ability to evaluate user needs and levels of understanding, make informed decisions about chart selections, and make effective use of interactivity dynamic data.  We’ll work with you before the course to ensure that we understand your organisation and what you’re hoping to achieve.  Sample learning content  Session 1: Data with a purpose Understanding the different types of dashboard. Information overload and other common dashboard pitfalls. Assessing user needs and levels of data fluency. Session 2: Planning a dashboard Assessing diverse user needs and levels of data fluency. Taking a User Experience (UX) approach to design and navigation. Applying an interative and collaborative approach to onboarding. Session 3: Graphs, charts and dials  Understanding how graphical perception informs chart choices. Making intelligent design choices to help users explore. Design principles for layout and navigation. Session 4: Using interactivity  Making effective use of filters to slice and dice data sets. Using layers of information to enable drilldown data exploration. Complenting dashboards with automated alerts and queries. Delivery We deliver our courses over Zoom, to maximise flexibility. The training can be delivered in a single day, or across multiple sessions. All of our courses are live and interactive – every session includes a mix of formal tuition and hands-on exercises. To ensure this is possible, the number of attendees is capped at 16 people.  Tutor Alan Rutter is the founder of Fire Plus Algebra. He is a specialist in communicating complex subjects through data visualisation, writing and design. He teaches for General Assembly and runs in-house training for public sector clients including the Home Office, the Department of Transport, the Biotechnology and Biological Sciences Research Council, the Health Foundation, and numerous local government and emergency services teams. He previously worked with Guardian Masterclasses on curating and delivering new course strands, including developing and teaching their B2B data visualisation courses. He oversaw the iPad edition launches of Wired, GQ, Vanity Fair and Vogue in the UK, and has worked with Condé Nast International as product owner on a bespoke digital asset management system for their 11 global markets. Testimonial “Alan was great to work with, he took us through the concepts behind data visualisation which means our team is now equipped for the future. He has a wide range of experience across the topic that is delivered in a clear, concise and friendly manner. We look forward to working with Alan again in the future.” John Masterson | Chief Product Officer | ImproveWell

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for information workers and data science professionals who seek to use database reporting and analysis tools such as Microsoft SQL Server Reporting Services, Excel, Power BI, R, SAS and other business intelligence tools, and wish to use TSQL queries to efficiently retrieve data sets from Microsoft SQL Server relational databases for use with these tools. Overview After completing this course, students will be able to: - Identify independent and dependent variables and measurement levels in their own analytical work scenarios. - Identify variables of interest in relational database tables. - Choose a data aggregation level and data set design appropriate for the intended analysis and tool. - Use TSQL SELECT queries to produce ready-to-use data sets for analysis in tools such as PowerBI, SQL Server Reporting Services, Excel, R, SAS, SPSS, and others. - Create stored procedures, views, and functions to modularize data retrieval code. This course is about writing TSQL queries for the purpose of database reporting, analysis, and business intelligence. 1 - INTRODUCTION TO TSQL FOR BUSINESS INTELLIGENCE Two Approaches to SQL Programming TSQL Data Retrieval in an Analytics / Business Intelligence Environment The Database Engine SQL Server Management Studio and the CarDeal Sample Database Identifying Variables in Tables SQL is a Declarative Language Introduction to the SELECT Query Lab 1: Introduction to TSQL for Business Intelligence 2 - TURNING TABLE COLUMNS INTO VARIABLES FOR ANALYSIS: SELECT LIST EXPRESSIONS, WHERE, AND ORDER BY Turning Columns into Variables for Analysis Column Expressions, Data Types, and Built-in Functions Column aliases Data type conversions Built-in Scalar Functions Table Aliases The WHERE clause ORDER BY Lab 1: Write queries 3 - COMBINING COLUMNS FROM MULTIPLE TABLES INTO A SINGLE DATASET: THE JOIN OPERATORS Primary Keys, Foreign Keys, and Joins Understanding Joins, Part 1: CROSS JOIN and the Full Cartesian Product Understanding Joins, Part 2: The INNER JOIN Understanding Joins, Part 3: The OUTER JOINS Understanding Joins, Part 4: Joining more than two tables Understanding Joins, Part 5: Combining INNER and OUTER JOINs Combining JOIN Operations with WHERE and ORDER BY Lab 1: Write SELECT queries 4 - CREATING AN APPROPRIATE AGGREGATION LEVEL USING GROUP BY Identifying required aggregation level and granularity Aggregate Functions GROUP BY HAVING Order of operations in SELECT queries Lab 1: Write queries 5 - SUBQUERIES, DERIVED TABLES AND COMMON TABLE EXPRESSIONS Non-correlated and correlated subqueries Derived tables Common table expressions Lab 1: Write queries 6 - ENCAPSULATING DATA RETRIEVAL LOGIC Views Table-valued functions Stored procedures Creating objects for read-access users Creating database accounts for analytical client tools Lab 1: Encapsulating Data Retrieval Logic 7 - GETTING YOUR DATASET TO THE CLIENT Connecting to SQL Server and Submitting Queries from Client Tools Connecting and running SELECT queries from: Excel PowerBI RStudio Exporting datasets to files using Results pane from SSMS The bcp utility The Import/Export Wizard Lab 1: Getting Your Dataset to the Client Additional course details: Nexus Humans 55232 Writing Analytical Queries for Business Intelligence 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 55232 Writing Analytical Queries for Business Intelligence 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 in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands on skills that you can apply right away. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll: Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights. Develop the ability to transform raw text into a structured format that machines can understand and analyze. Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects. Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends. Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction. Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback. The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots. Launch into the Universe of Natural Language Processing The journey begins: Unravel the layers of NLP Navigating through the history of NLP Merging paths: Text Analytics and NLP Decoding language: Word Sense Disambiguation and Sentence Boundary Detection First steps towards an NLP Project Unleashing the Power of Feature Extraction Dive into the vast ocean of Data Types Purification process: Cleaning Text Data Excavating knowledge: Extracting features from Texts Drawing connections: Finding Text Similarity through Feature Extraction Engineer Your Text Classifier The new era of Machine Learning and Supervised Learning Architecting a Text Classifier Constructing efficient workflows: Building Pipelines for NLP Projects Ensuring continuity: Saving and Loading Models Master the Art of Web Scraping and API Usage Stepping into the digital world: Introduction to Web Scraping and APIs The great heist: Collecting Data by Scraping Web Pages Navigating through the maze of Semi-Structured Data Unearth Hidden Themes with Topic Modeling Embark on the path of Topic Discovery Decoding algorithms: Understanding Topic-Modeling Algorithms Dialing the right numbers: Key Input Parameters for LSA Topic Modeling Tackling complexity with Hierarchical Dirichlet Process (HDP) Delving Deep into Vector Representations The Geometry of Language: Introduction to Vectors in NLP Text Manipulation: Generation and Summarization Playing the creator: Generating Text with Markov Chains Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank Peering into the future: Recent Developments in Text Generation and Summarization Solving real-world problems: Addressing Challenges in Extractive Summarization Riding the Wave of Sentiment Analysis Unveiling emotions: Introduction to Sentiment Analysis Tools Demystifying the Textblob library Preparing the canvas: Understanding Data for Sentiment Analysis Training your own emotion detectors: Building Sentiment Models Optional: Capstone Project Apply the skills learned throughout the course. Define the problem and gather the data. Conduct exploratory data analysis for text data. Carry out preprocessing and feature extraction. Select and train a model. ? Evaluate the model and interpret the results. Bonus Chapter: Generative AI and NLP Introduction to Generative AI and its role in NLP. Overview of Generative Pretrained Transformer (GPT) models. Using GPT models for text generation and completion. Applying GPT models for improving autocomplete features. Use cases of GPT in question answering systems and chatbots. Bonus Chapter: Advanced Applications of NLP with GPT Fine-tuning GPT models for specific NLP tasks. Using GPT for sentiment analysis and text classification. Role of GPT in Named Entity Recognition (NER). Application of GPT in developing advanced chatbots. Ethics and limitations of GPT and generative AI technologies.

Duration 5 Days 30 CPD hours This course is intended for The audience includes implementers, developers, system administrators, project teams, database administrators and engine project technical teams. The audience also includes consultants that are looking to gain an understanding of Maximo Asset Management 7.6.0.x and the engine. Overview After completing this course, you should be able to perform the following tasks: List the components of Tivoli's process automation engine Understand Maximo modules and applications Understand Tivoli's Process Automation Engine Create the foundation data necessary for Maximo Asset Management Customize the engine database and applications Automate IBM Service Management applications using workflows Use the Maximo Work Centers Use the Integration Framework to import and export data This course is designed for anyone planning to use Maximo Asset Management and Tivoli?s process automation engine with one of the IBM System Management (ISM) products. It is a course that introduces you to the features and functions of both products. IBM Maximo Asset Management Overview This unit focuses on Maximo as an overall product and how Maximo assists companies with their asset management lifecycle. Tivoli Process Automation Engine This unit describes the functions of Tivoli?s process automation engine and introduces the products that are based on the engine. This unit also introduces Start Centers and basic navigation. Architecture and components This unit covers the architecture of Tivoli?s process automation engine. The various components that make up the system are described. The unit will address Java EE servers and the basic use of WebSphere© as it relates to the engine. The unit then covers the organization of the administrative workstation and system properties. The unit briefly describes the setup of the system for using attachments. Foundation Data This unit covers the creation of foundation data for Tivoli?s process automation engine. The foundation data is the software constructs that are necessary in the basic configuration of the product. These constructs include organizations, sites, locations, classifications, and various engine financial configurations. Security Security addresses the need to protect system resources from unauthorized access by unauthenticated users. Resources in the system are protected by Authentication and Authorization. Database architecture This unit illustrates the possible database configurations using the Database Configuration application. It also presents specific command lines that you can run to configure the changes made on the attributes of business objects using the Database Configuration application. Work Management Work Management is a collection of components and products that work together to form a powerful process and work management system. This unit provides a look at work management and focuses on using Work Management to generate, process, and complete work orders. Customizing an application This unit provides an overview of the Application Designer and Migration Manager. You will learn how to change, duplicate and create applications. You will learn the process to move from development, integration testing, user acceptance testing and moving to production. Automation This unit provides a high-level overview of key automation application programs and their functionality. It describes cron tasks, which are used to automate jobs in the system. The unit then discusses various communication tools in the system such as Communication Templates and the E mail Listener application. Finally, automated means of notification using escalations and actions are covered. Workflow This unit focuses on workflow. You learn about the Workflow Designer and its tools. You also learn how to modify an existing workflow and how to manage the included workflows. Reporting This unit provides an overview of the data analysis and reporting options that you can use in the system to analyze data. You create query by example (QBE) reports, result sets, key performance indicators (KPI), and query-based reports (QBRs). Students can optionally review Appendix A to learn how to create a simple enterprise report using Business Intelligence Reporting Tools (BIRT) designer. This report provides an example of how developers create more complex, widely used reports for users. Integration Framework In this unit, a high-level overview of the Integration Framework is provided. The Integration Framework architecture and components are described and basic configuration steps are described. The configuration and steps for loading and exporting data to and from the system are covered. You have the opportunity to practice them also. Budget Monitoring This unit provides information on a new feature introduced in Maximo 7.6.0.8, the Budget Monitoring application. In this application, you can create budget records to monitor transactions in a financial period. Inspection Tools and Tasks This unit introduces the new Inspection application. You can use the Inspections tools to create online inspection forms by using your desktop computer or laptop, and you can use the forms to complete an inspection by using your desktop computer, laptop, or tablet. Troubleshooting This unit focuses on troubleshooting as a systematic approach to solving a problem. The goal is to determine why something does not work as expected and to resolve the problem. It discusses the configuration of logging in the application. It also covers basic troubleshooting techniques, some important component logs, and information about obtaining help from Tivoli Support. Additional course details: Nexus Humans U5TR712 - IBM Maximo Asset Management - System Administration and Development v7.6x 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 U5TR712 - IBM Maximo Asset Management - System Administration and Development v7.6x 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.

ChatGPT for Marketing and Productivity with AI Tools Course Overview: This course provides an in-depth exploration of ChatGPT and other AI tools in the context of marketing and productivity. Designed for individuals keen on integrating AI into their business strategies, it covers essential techniques and applications to enhance marketing efforts and streamline work processes. Learners will gain insights into leveraging AI for targeted campaigns, content creation, and automation, while also learning how to increase personal and team productivity using AI tools. By the end of the course, learners will have a clear understanding of how to apply AI-driven solutions to achieve measurable results in marketing and productivity. Course Description: In this course, learners will explore the dynamic field of AI-powered marketing and productivity tools. Key topics include the AI Marketing Playbook, which introduces learners to the fundamentals of using AI in marketing, followed by strategies for utilising ChatGPT and other AI tools for content creation, social media campaigns, and customer engagement. Additionally, learners will discover various AI tools designed to optimise productivity, including project management, data analysis, and communication tools. This course provides a comprehensive approach, equipping learners with the knowledge to harness AI’s capabilities in improving both marketing efforts and workplace efficiency. ChatGPT for Marketing and Productivity with AI Tools Curriculum: Module 01: The AI Marketing Playbook Module 02: How to Use ChatGPT and AI for Marketing Module 03: Productivity with AI Tools (See full curriculum) Who is this course for? Individuals seeking to enhance their marketing efforts with AI. Professionals aiming to boost their productivity using AI-driven tools. Beginners with an interest in AI technologies and marketing. Business owners looking to streamline marketing and productivity. Career Path: Digital Marketing Specialist Marketing Automation Expert AI Solutions Specialist Productivity Consultant Marketing Manager

Developing the Business Case: In-House Training Business analysts must be able to create business case documents that highlight project benefits, costs, and risks. The business case is based on the real business need to be solved. These become parts of proposals, feasibility studies, and other decision support documents. This course teaches the purpose, structure, and content of a business case. It presents the basic techniques for determining financial ROI, non-tangible benefits, and the probability of meeting expectations. What you will Learn At the end of this program, you will be able to: Perform feasibility studies Justify the business investment to solve the business problem Prepare an effective business case document Plan and implement a business case approval process Foundation Concepts The role of the BA An introduction to the BABOK® Guide The business analyst and the product / project life cycle (PLC) The business case deliverable Introducing the Business Case Process The BA and strategy analysis The BA and the business case process (BCP) The BA during the business case process (BCP) The BA after the business case process (BCP) Importance of defining solution performance metrics Defining the Business Need Overview of defining the business need Business needs: problem / opportunity statement Product vision Objectives and constraints Exploring Business Case Solutions Overview of exploring solutions Solution identification for feasibility Solution definition for analysis Assessing project risks Justifying the Business Case Overview of justifying the business case Qualitative justification Quantitative justification Approving the Business Case Overview of business case approval Developing recommendations Preparing the decision package - documents Preparing the decision package - presentations

Duration 2 Days 12 CPD hours This course is intended for IBM SPSS Statistics users who want to familiarize themselves with the statistical capabilities of IBM SPSS StatisticsBase. Anyone who wants to refresh their knowledge and statistical experience. Overview Introduction to statistical analysis Describing individual variables Testing hypotheses Testing hypotheses on individual variables Testing on the relationship between categorical variables Testing on the difference between two group means Testing on differences between more than two group means Testing on the relationship between scale variables Predicting a scale variable: Regression Introduction to Bayesian statistics Overview of multivariate procedures This course provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. Students will review several statistical techniques and discuss situations in which they would use each technique, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing relationships. Students will gain an understanding of when and why to use these various techniques and how to apply them with confidence, interpret their output, and graphically display the results. Introduction to statistical analysis Identify the steps in the research process Identify measurement levels Describing individual variables Chart individual variables Summarize individual variables Identify the normal distributionIdentify standardized scores Testing hypotheses Principles of statistical testing One-sided versus two-sided testingType I, type II errors and power Testing hypotheses on individual variables Identify population parameters and sample statistics Examine the distribution of the sample mean Test a hypothesis on the population mean Construct confidence intervals Tests on a single variable Testing on the relationship between categorical variables Chart the relationship Describe the relationship Test the hypothesis of independence Assumptions Identify differences between the groups Measure the strength of the association Testing on the difference between two group meansChart the relationship Describe the relationship Test the hypothesis of two equal group means Assumptions Testing on differences between more than two group means Chart the relationship Describe the relationship Test the hypothesis of all group means being equal Assumptions Identify differences between the group means Testing on the relationship between scale variables Chart the relationship Describe the relationship Test the hypothesis of independence Assumptions Treatment of missing values Predicting a scale variable: Regression Explain linear regression Identify unstandardized and standardized coefficients Assess the fit Examine residuals Include 0-1 independent variables Include categorical independent variables Introduction to Bayesian statistics Bayesian statistics and classical test theory The Bayesian approach Evaluate a null hypothesis Overview of Bayesian procedures in IBM SPSS Statistics Overview of multivariate procedures Overview of supervised models Overview of models to create natural groupings

Business Analysis Fundamentals: In-House Training This course is part of IIL's Business Analysis Certificate Program (BACP), a program designed to help prepare individuals to pass the IIBA® Certification exam to become a Certified Business Analysis Professional (CBAP™). This course teaches participants the overall process of business analysis and where it fits in the bigger picture of the project life cycle and the business context. The course is interactive and combines discussion, active workshops, and demonstrations of techniques. The goal is bottom-line results that cut through the real-world problems facing people seeking to improve the way they operate to develop new and improved systems and products or otherwise deliver results through project performance. What you will Learn At the end of this program, you will be able to: Define the solution scope Work with the development team in the systems testing stage Ensure the solution is usable in the business environment Foundation Concepts Defining the business analyst (BA) function The role of the BA as change agent An introduction to the BABOK® Guide BA roles and relationships through the project life cycle (PLC) Business Analysis Planning and Monitoring Overview of business analysis planning and monitoring (BAP&M) Business analysis planning and monitoring - process and tools Business analysis planning and monitoring - roles and responsibilities Business analysis planning and monitoring - governance, information management, and performance improvement Elicitation and Collaboration Overview of elicitation and collaboration Elicitation and collaboration techniques Requirements Life Cycle Management Overview of requirements life cycle management Requirements life cycle management task details Strategy Analysis Overview of strategy analysis Analyze current state Define future state Assess risks Define change strategy Requirements Analysis and Design Definition Overview of requirements analysis and design definition (RA&DD) The anatomy of requirements RA&DD task descriptions RA&DD techniques Solution Evaluation Overview of solution evaluation Solution evaluation tasks Solution evaluation in development stages Underlying Competencies Overview of underlying competencies (UC) Underlying competencies

Use Cases for Business Analysis: In-House Training The use case is a method for documenting the interactions between the user of a system and the system itself. Use cases have been in the software development lexicon for over twenty years, ever since it was introduced by Ivar Jacobson in the late 1980s. They were originally intended as aids to software design in object-oriented approaches. However, the method is now used throughout the Solution Development Life Cycle from elicitation through to specifying test cases, and is even applied to software development that is not object oriented. This course identifies how business analysts can apply use cases to the processes of defining the problem domain through elicitation, analyzing the problem, defining the solution, and confirming the validity and usability of the solution. What you will Learn You'll learn how to: Apply the use case method to define the problem domain and discover the conditions that need improvement in a business process Employ use cases in the analysis of requirements and information to create a solution to the business problem Translate use cases into requirements Getting Started Introductions Course structure Course goals and objectives Foundation Concepts Overview of use case modeling What is a use case model? The 'how and why' of use cases When to perform use case modeling Where use cases fit into the solution life cycle Use cases in the problem domain Use cases in the solution domain Use case strengths and weaknesses Use case variations Use case driven development Use case lexicon Use cases Actors and roles Associations Goals Boundaries Use cases though the life cycle Use cases in the life cycle Managing requirements with use cases The life cycle is use case driven Elicitation with Use Cases Overview of the basic mechanics and vocabulary of use cases Apply methods of use case elicitation to define the problem domain, or 'as is' process Use case diagrams Why diagram? Partitioning the domain Use case diagramming guidelines How to employ use case diagrams in elicitation Guidelines for use case elicitation sessions Eliciting the problem domain Use case descriptions Use case generic description template Alternative templates Elements Pre and post conditions Main Success Scenario The conversation Alternate paths Exception paths Writing good use case descriptions Eliciting the detailed workflow with use case descriptions Additional information about use cases Analyzing Requirements with Use Cases Use case analysis on existing requirements Confirming and validating requirements with use cases Confirming and validating information with use cases Defining the actors and use cases in a set of requirements Creating the scenarios Essential (requirements) use case Use case level of detail Use Case Analysis Techniques Generalization and Specialization When to use generalization or specialization Generalization and specialization of actors Generalization and specialization of use cases Examples Associating generalizations Subtleties and guidelines Use Case Extensions The <> association The <> association Applying the extensions Incorporating extension points into use case descriptions Why use these extensions? Extensions or separate use cases Guidelines for extensions Applying use case extensions Patterns and anomalies o Redundant actors Linking hierarchies Granularity issues Non-user interface use cases Quality considerations Use case modeling errors to avoid Evaluating use case descriptions Use case quality checklist Relationship between Use Cases and Business Requirements Creating a Requirements Specification from Use Cases Flowing the conversation into requirements Mapping to functional specifications Adding non-functional requirements Relating use cases to other artifacts Wire diagrams and user interface specifications Tying use cases to test cases and scenarios Project plans and project schedules Relationship between Use Cases and Functional Specifications System use cases Reviewing business use cases Balancing use cases Use case realizations Expanding and explaining complexity Activity diagrams State Machine diagrams Sequence diagrams Activity Diagrams Applying what we know Extension points Use case chaining Identifying decision points Use Case Good Practices The documentation trail for use cases Use case re-use Use case checklist Summary What did we learn, and how can we implement this in our work environment?