Name: Data Science for Business: Analytics, ML & Decision Making
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Created byArun Singhal B-Tech, MBA (IIM-B),Unilever, J&J, Danone, IIMU, Cello

Last updated 5/2026

English

What you'll learn

Understand the real-world role of a Data Scientist and what companies actually expect
Identify the skills, tools, and qualifications needed to break into data science
Build a clear, step-by-step roadmap to become a successful Data Scientist—even if you’re starting from scratch
Learn how to analyze and explore data to uncover meaningful business insights
Master the fundamentals of feature engineering and model development to solve real problems
Create impactful data visualizations that drive decision-making
Apply experimentation techniques (A/B testing, validation) to improve outcomes
Learn how to deliver end-to-end data science projects with confidence
Develop the mindset and approach used by high-performing Data Scientists in industry

Course content

13 sections • 50 lectures • 6h 1m total length

Introduction6:05
Introduction to the course and instructor
Introduction Case Study11:08
A case study of becoming a successful data scientist

Overview I- What are the responsibilities of a Data Scientist?7:33
At the end of this lecture, you will learn the following
•What are the responsibilities of a Data Scientist?
Overview I
What qualifications are required to become a Data Scientist?4:40
At the end of this lecture, you will learn the following
•What qualifications are required to become a Data Scientist?
Overview II
How can you become a successful Data Scientist?8:25
At the end of this lecture, you will learn the following
•How can you become a successful Data Scientist?
Overview III
Data Scientist Interview13:07
At the end of this lecture, you will learn the following
•What to plan for the interview?
•What Topics to prepare for the Interview?
•What Type of Questions to prepare?
•How will you plan Practice Sessions?
•How to prepare for the interview?
•How to perform in the interview?

Data Analysis and Exploration I- Conduct exploratory data analysis (EDA)8:43
At the end of this lecture, you will learn the following
•Conduct exploratory data analysis (EDA) to understand patterns, trends, and anomalies in the data.
Data Analysis and Exploration I
Clean and preprocess data to ensure accuracy and completeness7:06
At the end of this lecture, you will learn the following
•Clean and preprocess data to ensure accuracy and completeness
Data Analysis and Exploration II
Data Analysis and Exploration
Data Analysis and Exploration Assignment Solution8:33
The solution to the assignment on Data Analysis and Exploration

Model Development I- Design, develop, and implement machine learning models8:51
At the end of this lecture, you will learn the following
Design, develop, and implement machine learning models to solve business problems
Model Development I
Design, develop, and implement machine learning models to solve business problem5:01
At the end of this lecture, you will learn the following
Design, develop, and implement machine learning models to solve business problems
Model Development II
Utilize statistical modeling techniques for predictive and prescriptive analytic7:08
At the end of this lecture, you will learn the following
Utilize statistical modeling techniques for predictive and prescriptive analytics
Model Development III
Utilize statistical modeling techniques for prescriptive analytics.5:24
At the end of this lecture, you will learn the following
•Utilize statistical modeling techniques for prescriptive analytics.
Model Development IV
Evaluate and select appropriate algorithms for specific tasks7:21
At the end of this lecture, you will learn the following
•Evaluate and select appropriate algorithms for specific tasks
Model Development V
Evaluate and select appropriate algorithms for specific tasks4:28
At the end of this lecture, you will learn the following
•Evaluate and select appropriate algorithms for specific tasks
Model Development VI
Model Development

Feature Engineering I- Identify relevant features and variables6:31
At the end of this lecture, you will learn the following
Identify relevant features and variables for model training and optimization
Feature Engineering I
Identify relevant features and variables for model training and optimization4:39
At the end of this lecture, you will learn the following
•Identify relevant features and variables for model training and optimization
Feature Engineering II
Collaborate with domain experts to incorporate industry-specific knowledge6:29
At the end of this lecture, you will learn the following
•Collaborate with domain experts to incorporate industry-specific knowledge into analyses
Feature Engineering III
Feature Engineering

Data Visualization I- How to create compelling data visualizations6:47
At the end of this lecture, you will learn the following
•How to create compelling data visualizations to communicate complex findings to non-technical stakeholders.
Data Visualization I
How to create compelling data visualizations to communicate complex findings4:53
At the end of this lecture, you will learn the following
•How to create compelling data visualizations to communicate complex findings to non-technical stakeholders
Data Visualization II
How to use tools such as Matplotlib, Seaborn, or Tableau to present insights4:56
At the end of this lecture, you will learn the following
•How to use tools such as Matplotlib, Seaborn, or Tableau to present insights effectively
Data Visualization III

Collaboration I- How to work closely with cross-functional teams5:40
At the end of this lecture, you will learn the following
•How to work closely with cross-functional teams, including business analysts, engineers, and decision-makers, to understand business needs and provide data-driven solutions
Collaboration I
Collaboration II- How to work closely with cross-functional teams5:14
At the end of this lecture, you will learn the following
•How to work closely with cross-functional teams, including business analysts, engineers, and decision-makers, to understand business needs and provide data-driven solutions
Collaboration II
Collaboration III- How to work closely with cross-functional teams5:53
At the end of this lecture, you will learn the following
•How to work closely with cross-functional teams, including business analysts, engineers, and decision-makers, to understand business needs and provide data-driven solutions
Collaboration III

Experimentation I- How to design and execute experiments7:49
At the end of this lecture, you will learn the following
How to design and execute experiments to test hypotheses and measure the impact of various interventions
Experimentation I
Experimentation II- How to design and execute experiments8:43
At the end of this lecture, you will learn the following
•How to design and execute experiments to test hypotheses and measure the impact of various interventions.
Experimentation II
Experimentation III- How to design and execute experiments7:56
At the end of this lecture, you will learn the following
•How to design and execute experiments to test hypotheses and measure the impact of various interventions.
Experimentation III
Experimentation IV- How to design and execute experiments6:18
At the end of this lecture, you will learn the following
How to design and execute experiments to test hypotheses and measure the impact of various interventions
Experimentation IV
Experimentation V- How to design and execute experiments5:09
At the end of this lecture, you will learn the following
How to design and execute experiments to test hypotheses and measure the impact of various interventions
Experimentation V
Experimentation VI- How to iterate on models and analyses7:06
At the end of this lecture, you will learn the following
•How to iterate on models and analyses based on feedback and changing business requirements
Experimentation VI
Experimentation VII- How to iterate on models and analyses6:43
At the end of this lecture, you will learn the following
•How to iterate on models and analyses based on feedback and changing business requirements
Experimentation VII

Continuous Learning I- Stay abreast of the latest developments7:13
At the end of this lecture, you will learn the following
•Stay abreast of the latest developments in data science, machine learning, and related fields
Continuous Learning I
Continuous Learning II- Stay abreast of the latest developments5:59
At the end of this lecture, you will learn the following
•Stay abreast of the latest developments in data science, machine learning, and related fields
Continuous Learning II

Documentation I- Maintain clear and concise documentation of methodologies4:44
At the end of this lecture, you will learn the following
Maintain clear and concise documentation of methodologies
Documentation I
Maintain clear and concise documentation of data sources4:14
At the end of this lecture, you will learn the following
Maintain clear and concise documentation of data sources
Documentation II
Maintain clear and concise documentation of code6:06
At the end of this lecture, you will learn the following
Maintain clear and concise documentation of code
Documentation III
Maintain clear and concise documentation of methodologies, data sources, & code6:17
At the end of this lecture, you will learn the following
•Maintain clear and concise documentation of methodologies, data sources, and code
Documentation IV

Requirements

Willing to devote 5 hours

Description

Most Data Science courses teach tools.
Very few teach how to actually use data to make decisions.

That’s why many learners:

Know Python or machine learning concepts
But struggle to apply them in real business situations
Can build models… but cannot explain insights
Feel unsure about what companies actually expect

This course fixes that.

Learn Data Science the Way Businesses Actually Use It

This is not just another technical course.

It is a business-focused Data Science course designed to help you:

Think like a Data Scientist
Solve real-world business problems
Make better decisions using data

Even if you are starting from scratch.

What You Will Be Able to Do

By the end of this course, you will be able to:

Understand what Data Scientists actually do in real companies
Analyze and interpret data to generate meaningful insights
Apply machine learning concepts to solve practical problems
Create clear and impactful data visualizations
Use experimentation (like A/B testing) to improve decisions
Connect data insights to real business outcomes
Approach problems with a structured, data-driven mindset

How This Course is Different

Most courses focus on:

Tools
Coding
Algorithms

This course focuses on:

Thinking + Application + Decision Making

You will learn:

How to approach problems like a Data Scientist
How to use data to support decisions
How to avoid common mistakes in analysis and modeling

This makes you far more valuable in real roles.

A Clear, Structured Learning Path

You will follow a practical journey:

Foundations → Data Analysis → Machine Learning → Business Application → Decision Making

So you don’t feel lost or overwhelmed.

Designed for Real Career Outcomes

This course helps you:

Build a strong foundation in Data Science
Understand what companies expect in interviews and jobs
Develop skills that go beyond theory
Prepare for roles in analytics, business intelligence, and data science

Learn from Real Industry Experience

This course is built on decades of experience applying data-driven thinking in real business environments, including work with global organizations and teaching at top management institutes.

You are not just learning concepts—
you are learning how data is actually used to drive decisions.

Who This Course Is For

This course is ideal for:

Students and professionals who want to enter Data Science
MBA students and business professionals who want to use data effectively
Analysts who want to move into Data Science roles
Beginners who feel overwhelmed and want a structured path
Anyone who wants to connect data with real business decisions

Who This Course Is NOT For

Those looking only for deep coding or advanced technical implementation
Learners who want shortcuts without understanding fundamentals

What You Will Gain

Clarity on what to learn and why
Confidence in working with data
Ability to think and communicate like a Data Scientist
A strong foundation to grow into advanced AI and ML roles

Start Building Your Data Advantage

Data is no longer optional.
But tools alone are not enough.

What matters is how you think with data.

Preview the course.
If it helps you think better and decide better—enroll and begin your journey.

This Course is Part of a Structured Learning Path

Learning Path: ANALYTICS PATH (Starter → Builder → Advanced)

This course is your ADVANCED step.

Next Recommended Courses

After completing this course, continue your growth with:

Data Analytics (Starter)

Business Analytics (Builder)

Business Analysis (Builder)

AI Data Driven Management (Advanced)

Who this course is for:

Professionals who want to transition into a Data Scientist role and need a clear, structured roadmap
Analysts, engineers, or MBA students looking to upgrade into data science with real-world skills
Beginners who feel overwhelmed and want a step-by-step, practical path to start their data science career
Working professionals who want to apply data science to solve real business problems
Anyone who wants to understand what Data Scientists actually do in the industry (beyond theory)

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 17min

Overview4 lectures • 34min

Data Analysis and Exploration3 lectures • 24min

Model Development6 lectures • 38min

Feature Engineering3 lectures • 18min

Data Visualization3 lectures • 17min

Collaboration3 lectures • 17min

Experimentation7 lectures • 50min

Continuous Learning2 lectures • 13min

Documentation4 lectures • 21min

Requirements

Description

Who this course is for: