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