Data Analytics Introduction Certification - eLearning

Data Analytics Introduction Certification - eLearning

This data analytics course provides fundamental concepts of data analytics through real-world case studies and examples and gives insights into how to apply data and analytics principles in your business. You’ll learn about project lifecycles, the difference between data analytics, data science, and machine learning; building an analytics framework, and using analytics tools to draw business insights.

Prerequisites

This Introduction to Data Analytics course has been designed for all levels, regardless of prior knowledge of analytics, statistics, or coding. Familiarity with mathematics is helpful for this course.

Target Audience

This course is ideal for anyone who wishes to learn the fundamentals of data analytics and pursue a career in this growing field. The course also caters to CxO-level and middle-management professionals who want to improve their ability to derive business value and ROI from analytics.

Learning Objectives

When you complete this Introduction to Data Analytics course, you will be able to accomplish the following:

• Understand how to solve analytical problems in real-world scenarios
• Define effective objectives for analytics projects
• Work with different types of data
• Understand the importance of data visualization to drive more effective business decisions and ROI
• Understand charts, graphs, and tools used for analytics and use them to gain valuable insights
• Create an analytics adoption framework Identify upcoming trends in data analytics

Course Outline

Lesson 1: Data Analytics Overview

• Introduction
• Data Analytics: Importance
• Digital Analytics: Impact on Accounting
• Data Analytics Overview
• Types of Data Analytics
• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics
• Data Analytics: Amazon Example
• Data Analytics Benefits: Decision-making
• Data Analytics Benefits: Cost Reduction
• Data Analytics Benefits: Amazon Example
• Data Analytics: Other Benefits
• Key Takeaways

Lesson 2: Dealing with Different Types of Data

• Introduction
• Terminologies in Data Analytics - Part One
• Terminologies in Data Analytics - Part Two
• Types of Data
• Qualitative and Quantitative Data
• Data Levels of Measurement
• Normal Distribution of Data
• Statistical Parameters
• Key Takeaways

Lesson 3: Data Visualization for Decision making

• Introduction
• Data Visualization
• Understanding Data Visualization
• Commonly Used Visualizations
• Frequency Distribution Plot
• Swarm Plot
• Importance of Data Visualization
• Data Visualization Tools - Part One
• Data Visualization Tools - Part Two
• Languages and Libraries in Data Visualization
• Dashboard Based Visualization
• BI and Visualization Trends
• BI Software Challenges

Lesson 4: Data Science Data Analytics and Machine Learning

• Introduction
• The Data Science Domain
• Data Science, Data Analytics, and Machine Learning - Overlaps
• Data Science Demystified
• Data Science and Business Strategy
• Successful Companies Using Data Science
• Travel Industry
• Retail
• E-commerce and Crime Agencies
• Analytical Platforms Across Industries
• Key Takeaways

Lesson 5: Data Science Methodology

• Introduction
• Data Science Methodology
• From Business Understanding to Analytic Approach
• From Requirements to Collection
• From Understanding to Preparation
• From Modeling to Evaluation
• From Deployment
• Key Takeaways

Lesson 6: Data Analytics in Different Sectors

• Introduction
• Analytics for Products or Services
• How Google Uses Analytics
• How Linkedin Uses Analytics
• How Amazon Uses Analytics
• Netflix: Using Analytics to Drive Engagement
• Netflix: Using Analytics to Drive Success
• Media and Entertainment Industry
• Education Industry
• Healthcare Industry
• Government
• Weather Forecasting
• Key Takeaways

Lesson 7: Analytics Framework and Latest trends

• Introduction
• Case Study: EY
• Customer Analytics Framework
• Data Understanding
• Data Preparation
• Modeling
• Model Monitoring
• Latest Trends in Data Analytics
• Graph Analytics
• Automated Machine Learning
• Open Source AI
• Key Takeaways