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.
Data Analytics Introduction Delivery Methods
Data Analytics Introduction Training Information
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
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
Introduction
- Example: Schools and Academies
- BI in Education
BI for Better Decisions
Decision types
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
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
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
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
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
