Introduction to Data Analytics

Course Outline

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

Introduction to Data Analytics

Course Outline