Introduction to Data Analytics

As data evolves for organizations, employees must understand the value of the data they hold. This Data Analytics Introduction provides a clear understanding of data analytics's purpose, tools, and techniques. In addition, it will help attendees to plan the data and digital strategy for their organizations. Introduction to Data Analytics Benefits Back at work, attendees will be able to: Define what Data Analytics is and how it helps with business-focused decision-making Understand the fundamentals of pattern recognition Differentiate between data roles such as Data Analyst, Data Scientist, Data Engineer, Business Analyst, and Business Intelligence Analyst. Recognize the value, terminology, and challenges of Business Intelligence Understand how Data Mining builds knowledge, insights, patterns, & data advantages Appreciate the usefulness of data visualization, visual patterns, and Infographics for stakeholder communication Improve awareness of the value of the data your organization holds and how to manipulate it Have excellent fundamental knowledge of data, how it is captured, and how it is visualized for us in the business Position Data Warehouses as data management facilities that help to: Create reports and analysis Support managerial decision making Engineered for efficient reporting and querying Training Prerequisites A basic understanding of what data is and the function of data analysis Certification Information Learning Tree Exam included Data Analytics Introduction Training Outline Chapter 1: Data Analytics Introduction Business Intelligence Example: MoneyBall: Data Mining in Sports Pattern Recognition Types of Patterns Finding a Pattern Uses of Patterns The Data Processing Chain Data Database Data Warehouse Data Mining Data Visualization Data Analytics Terminology and Careers Review Wheel Chapter 2: BI Concepts & Applications Introduction Example: Schools and Academies BI in Education BI for Better Decisions Decision types BI Tools BI Skills BI Applications Customer Relationship Management Healthcare and Wellness Education Retail Banking Financial Services Insurance Manufacturing Supply Chain Management Telecom Public Sector Conclusion Review Wheel Case Study Exercise Chapter 3: Data Warehousing Introduction Example: University Health System – BI in Healthcare Design Considerations for DW DW Development Approaches DW Architecture Data Sources Data Loading Processes Data Warehouse Design DW Access DW Best Practices Data Lakes Conclusion Review Wheel Case Study Exercise: Step 2 Chapter 4: Data Mining Introduction Introduction Example: Target Corp – Data Mining in Retail Gathering and selecting data Data cleansing and preparation Outputs of Data Mining Evaluating Data Mining Results Data Mining Techniques Tools and Platforms for Data Mining Data Mining Best Practices Myths about data mining Data Mining Mistakes Conclusion Review Wheel Case Study Exercise: Step 3 Chapter 5: Data Visualization Introduction Example: Dr. Hans Gosling - Visualizing Global Public Health Excellence in Visualization Types of Charts Visualization Example Tips for Data Visualization Conclusion Review Wheel Case Study Exercise: Step 4 Chapter 6 Popular Data Mining Techniques Decision Trees Introduction Example: Predicting Heart Attacks using Decision Trees Decision Tree problem Decision Tree Construction Regression and Time Series Analysis Correlations and Relationships A visual look at relationships Regression Non-linear regression Logistic Regression Advantages and Disadvantages of Regression Time Series Analysis Artificial Neural Networks Introduction Example: IBM Watson - Analytics in Medicine Principles of an Artificial Neural Network Business Applications of ANN Design Representation of a Neural Network Architecting a Neural Network Developing an ANN Advantages and Disadvantages of using ANNs Conclusion