6. Course Outline
Course
Introduction
Evolution
of Data
Analytics
Dealing with Different
Types of Data
Data
Visualization
for Decision
Making
Data Analytics,
Data Science, and
Machine Learning
1
2
3
4
5
6
Data Science
Methodology
7
Data Analytics in
Different Sectors
8
Analytics Framework,
Case Study, and
Upcoming Trends
7. Learning Outcomes
By the end of this course, you will be able to:
● Analyze the triggers that led to the evolution of
analytics
● Develop an analytical approach to a business problem
● Compare data science, data analytics, and machine
learning and understand their business application
● Explain the significance of data visualization in
analytical modeling to drive meaningful business
decisions
● Identify business use cases that can leverage data
analytics
9. Course Features
Few case studies discussed in this course:
Amazon uses data analytics to improve efficiency and reduce cost.
LinkedIn employs data analytics to revamp its job listings, track
user profiles, and posts.
Netflix gathers data from its subscribers to decide on customer
preferences.
10. Course Features
Research Studies
According to McKinsey, companies that use customer analytics
outsmart their competitors in terms of profit.
According to a survey conducted by the Business Application
Research Center (BARC) on the BI trends, Master Data and Data
Quality Management are the most important trend in 2020.