3-day instructor-led training course
Hands-on labs included
Learning Tree Exam included
After-course coaching available
#1290
Introduction to Data Analytics
Course 1290
- Duration: 3 days
- Language: English
-
17 NASBA CPE Credits (live, in-class training only)
- Level: Foundation
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
In-Person
Online
Upskill your whole team by bringing Private Team Training to your facility.
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 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
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 Rosling - 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
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Data Analytics Introduction FAQs
40% of the course time is spent in hands-on exercises
Yes, this course is a perfect starting point for someone entering Data Analytics
No, this course is technology neutral
- This course applies to anyone looking for an introduction to data analytics, including data warehousing, data mining, and data visualization.
- Senior Managers, Business Analysts, Data Analysts, Data Scientists, Database Administrators
- They come from all types of industries, with data being the common element, i.e., Any industry that needs help leveraging the advantages of data analytics and the data analytics industry itself.
- Senior Managers, Business Analysts, Data Analysts, Data Scientists, and Database Administrators.
- Senior managers will find it helpful to understand data analytics's advantages for the business.
- Business analysts will find this course helpful in understanding the eventual output of requirements and modeling.
- Students wishing to pursue a course in Data Science or Data Analysis will find this course useful as it gives an overview of the Data Analytics field.