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Master in Business Analytics for Business Analysts

Name: Master in Business Analytics for Business Analysts
Rating: 4.0 (224 reviews)

Become a Business & Data Analyst: Data Analytics, Statistics & Real-World Analysis Skills

Created byArun Singhal B-Tech, MBA (IIM-B),Unilever, J&J, Danone, IIMU, Cello

Last updated 6/2026

English

What you'll learn

Understand Business Analytics, Data Analytics & how businesses make data-driven decisions
Differentiate Business Analyst vs Data Analyst roles and choose the right career path
Explore high-demand careers in Business Analytics, Data Analytics & Business Analysis
Solve real-world case studies using Business & Data Analytics techniques
Build a step-by-step roadmap to become a job-ready Business/Data Analyst
Collect, clean, and preprocess data for accurate business analysis
Apply descriptive, predictive & prescriptive analytics to real business problems
Learn essential technical skills: Excel, basic programming & analytics tools
Understand databases and how to manage and use data effectively
Create impactful dashboards using data visualization tools
Apply statistics for data analysis and business decision-making
Connect analytics with business strategy for better decision-making
Communicate insights effectively using storytelling and presentation
Think like a Business Analyst to solve problems and drive business impact

Course content

19 sections • 95 lectures • 13h 31m total length

Introduction6:15
Introduction of the Instructor and the Course
Introduction Case Study13:30
At the end of this course, you will learn the following
•A Case Study of becoming an Excellent Business Analyst
Business Analytics in Action: Quick Exercise6:32
Business Analytics in Action: Quick Exercise

Overview I- What is Business Analytics?7:15
At the end of this lecture, you will learn the following
•What is Business Analytics?
Overview I
Overview II- How is Business Analytics different from Business Analysis?6:13
At the end of this lecture, you will learn the following
•How is Business Analytics different from Business Analysis?
Overview II
Overview III- How is Business Analytics different from Data Analytics?7:25
At the end of this lecture, you will learn the following
•How is Business Analytics different from Data Analytics?
Overview III
Overview IV- What roles can you get after doing Business Analytics9:27
At the end of this lecture, you will learn the following
•What roles can you get after doing Business Analytics
Overview IV

Case Studies of Excellence I- Overview5:30
At the end of this lecture, you will learn the following
Overview
Case Studies of Excellence I
Case Studies of Excellence II- Amazon Case Study8:03
At the end of this lecture, you will learn the following
Amazon Case Study
Case Studies of Excellence II
Case Studies of Excellence III- Netflix Case Study12:39
At the end of this lecture, you will learn the following
Netflix Case Study
Case Studies of Excellence III
Case Studies of Excellence IV- Walmart Case Study10:57
At the end of this lecture, you will learn the following
Walmart Case Study
Case Studies of Excellence IV

How to become excellent in Business Analytics I4:42
At the end of this lecture, you will learn the following
How to become excellent in Business Analytics?
How to become excellent in Business Analytics I
How to become excellent in business analytics II5:09
At the end of this lecture, you will learn the following
How to become excellent in Business Analytics?
How to become excellent in business analytics II
A case study of becoming excellent in business analytics11:32
A case study of becoming excellent in business analytics

Data Collection4:55
At the end of this lecture, you will learn the following
•Data Collection
Understand data collection, cleaning, and preprocessing I
Manual Data Collection10:06
At the end of this lecture, you will learn the following
•Manual Data Collection
Understand data collection, cleaning, and preprocessing II
How to collect data using Automated Tools?11:15
At the end of this lecture, you will learn the following
•How to collect data using Automated Tools?
Understand data collection, cleaning, and preprocessing III
How to use Data Acquisition Tools like Google Analytics?7:08
At the end of this lecture, you will learn the following
•How to use Data Acquisition Tools like Google Analytics?
Understand data collection, cleaning, and preprocessing IV
How to use Data Acquisition Tools Like Scrapy?3:13
At the end of this lecture, you will learn the following
•How to use Data Acquisition Tools Like Scrapy?
Understand data collection, cleaning, and preprocessing V
How to use Data Acquisition Tools like SQL?4:04
At the end of this lecture, you will learn the following
•How to use Data Acquisition Tools like SQL?
Understand data collection, cleaning, and preprocessing VI
Data Collection
Data Cleaning6:32
At the end of this lecture, you will learn the following
•Let us now look at Data Cleaning
Understand data collection, cleaning, and preprocessing VII
Data Cleaning
Data Preprocessing7:49
At the end of this lecture, you will learn the following
•Let us now look at Data Preprocessing
Understand data collection, cleaning, and preprocessing VIII
How to use StandardScaler for data standardization6:38
At the end of this lecture, you will learn the following
•How to use StandardScaler for data standardization
Understand data collection, cleaning, and preprocessing IX
Label Encoding6:10
At the end of this lecture, you will learn the following
Label Encoding
Understand data collection, cleaning, and preprocessing X
One Hot Encoding7:59
At the end of this lecture, you will learn the following
One Hot Encoding
Understand data collection, cleaning, and preprocessing XI
Correlation analysis9:05
At the end of this lecture, you will learn the following
•Correlation analysis
Understand data collection, cleaning, and preprocessing XII
How to determine Feature Importance Scores?9:48
At the end of this lecture, you will learn the following
•How to determine Feature Importance Scores?
Understand data collection, cleaning, and preprocessing XIII
PCA - Principal Component Analysis8:17
At the end of this lecture, you will learn the following
•PCA - Principal Component Analysis
How to use SMOTE to handle imbalanced data?
Understand data collection, cleaning, and preprocessing XV9:21
At the end of this lecture, you will learn the following
•How to use SMOTE to handle imbalanced data?
Understand data collection, cleaning, and preprocessing XV
Data Preprocessing Automation Tools, Practice and Resources and Real-World Appl5:21
At the end of this lecture, you will learn the following
•Data Preprocessing Automation Tools, Practice and Resources and Real-World Applications
Understand data collection, cleaning, and preprocessing XVI
Data Preprocessing

How to master descriptive analytics?5:23
At the end of this lecture, you will learn the following
•How to master descriptive analytics?
Master Descriptive Analytics I
How to compute and interpret descriptive statistics?12:23
At the end of this lecture, you will learn the following
•How to compute and interpret descriptive statistics?
Master Descriptive Analytics II
What are the data visualization principles?9:51
At the end of this lecture, you will learn the following
•What are the data visualization principles?
Master Descriptive Analytics III
How to master tools like Excel, Tableau, Power BI, and Python10:44
At the end of this lecture, you will learn the following
•How to master tools like Excel, Tableau, Power BI, and Python (Pandas, Matplotlib, Seaborn)
Master Descriptive Analytics IV
How to use Data Aggregation Techniques7:00
At the end of this lecture, you will learn the following
How to use Data Aggregation Techniques like Grouping, filtering, pivot tables, and summarizing large datasets
Master Descriptive Analytics V
Analyze sales trends, customer behavior, or operational metrics6:53
At the end of this lecture, you will learn the following
•Analyze sales trends, customer behavior, or operational metrics using real-world datasets
Master Descriptive Analytics VI
Create dashboards and reports to communicate insights10:18
At the end of this lecture, you will learn the following
•Create dashboards and reports to communicate insights
Master Descriptive Analytics VII
Descriptive Analytics

Purpose and steps to master predictive analytics3:33
At the end of this lecture, you will learn the following
•Purpose and steps to master predictive analytics
Master Predictive Analytics I
Regression Analysis10:46
At the end of this lecture, you will learn the following
•Regression Analysis
Master Predictive Analytics II
Classical Probability8:21
At the end of this lecture, you will learn the following
How to learn Probability
•Classical Probability
Master Predictive Analytics III
Conditional Probability8:49
At the end of this lecture, you will learn the following
•Conditional Probability
Master Predictive Analytics IV
Bayes' Theorem6:59
At the end of this lecture, you will learn the following
•Bayes' Theorem
Master Predictive Analytics V
Normal Distribution4:18
At the end of this lecture, you will learn the following
Normal Distribution
Master Predictive Analytics VI
Binominal Distribution4:40
At the end of this lecture, you will learn the following
•Binominal Distribution
Master Predictive Analytics VII
Poisson Distribution •Probability Distribution Comparisons and Examples in Pyth7:19
At the end of this lecture, you will learn the following
•Poisson Distribution
•Probability Distribution Comparisons and Examples in Python
Master Predictive Analytics VIII
Real-world applications in risk analysis, A/B testing, and predictive modeling12:25
At the end of this lecture, you will learn the following
•Real-world applications in risk analysis, A/B testing, and predictive modeling
Master Predictive Analytics IX
Hypothesis Testing12:05
At the end of this lecture, you will learn the following
•Hypothesis Testing
Master Predictive Analytics X
How to learn Time Series Forecasting?11:06
At the end of this lecture, you will learn the following
•How to learn Time Series Forecasting?
Master Predictive Analytics XI
Supervised Learning11:37
At the end of this lecture, you will learn the following
•Supervised Learning
Master Predictive Analytics XII
Unsupervised Learning6:09
At the end of this lecture, you will learn the following
•Unsupervised Learning
Master Predictive Analytics XIII
Python or R for implementing machine learning models9:22
At the end of this lecture, you will learn the following
•How to focus on Python (Scikit-learn, TensorFlow, PyTorch) or R for implementing machine learning models?
Master Predictive Analytics XIV
How to use datasets from Kaggle or UCI Machine Learning Repository7:00
At the end of this lecture, you will learn the following
•How to use datasets from Kaggle or UCI Machine Learning Repository to build and test predictive models like customer churn prediction, demand forecasting, or fraud detection
Master Predictive Analytics XV
Evaluation metrics such as accuracy, precision, recall, F1-score, and RMSE11:17
At the end of this lecture, you will learn the following
•How to learn evaluation metrics such as accuracy, precision, recall, F1-score, and RMSE?
Master Predictive Analytics XVI
Predictive Analytics

Purpose and steps to Master Prescriptive Analytics4:20
At the end of this lecture, you will learn the following
•Purpose and steps to Master Prescriptive Analytics
Master Prescriptive Analytics I
Linear programming, integer programming, and constraint optimization6:39
At the end of this lecture, you will learn the following
•How to learn linear programming, integer programming, and constraint optimization
Master Prescriptive Analytics II
Mathematical Foundation10:05
At the end of this lecture, you will learn the following
•Learn the Mathematical Foundation
Master Prescriptive Analytics III
How to Solve Optimization Problems14:25
At the end of this lecture, you will learn the following
•Learn How to Solve Optimization Problems
Master Prescriptive Analytics IV
How to study decision trees, Markov decision processes6:58
At the end of this lecture, you will learn the following
How to study decision trees, Markov decision processes
Master Prescriptive Analytics V
Markov Decision Processes (MDPs)5:16
At the end of this lecture, you will learn the following
•Markov Decision Processes (MDPs)
Master Prescriptive Analytics VI
Simulation Techniques7:14
At the end of this lecture, you will learn the following
•Simulation Techniques
Master Prescriptive Analytics VII
How to integrate predictive insights into optimization models10:31
At the end of this lecture, you will learn the following
•How to integrate predictive insights into optimization models
Master Prescriptive Analytics VIII
How to learn tools like IBM CPLEX, Gurobi, or SAS Optimization8:34
At the end of this lecture, you will learn the following
•How to learn tools like IBM CPLEX, Gurobi, or SAS Optimization
Master Prescriptive Analytics IX
Creating optimization models for resource allocation, scheduling, or pricing st9:21
At the end of this lecture, you will learn the following
•How to practice creating optimization models for resource allocation, scheduling, or pricing strategies
Master Prescriptive Analytics X
Apply prescriptive analytics10:18
At the end of this lecture, you will learn the following
•How to apply prescriptive analytics in industries like supply chain, finance, healthcare, or marketing
Master Prescriptive Analytics XI
Prescriptive Analytics

How to learn Python or R for data analysis and modeling10:36
At the end of this lecture, you will learn the following
•How to learn Python or R for data analysis and modeling
Developing Technical Skills I
How to gain proficiency in SQL to retrieve and manipulate data8:16
At the end of this lecture, you will learn the following
•How to gain proficiency in SQL to retrieve and manipulate data
Developing Technical Skills II
How to use Tableau, Power BI, or Excel to create meaningful visual reports12:45
At the end of this lecture, you will learn the following
•How to use Tableau, Power BI, or Excel to create meaningful visual reports
Developing Technical Skills III
How to learn frameworks like CRISP-DM11:17
At the end of this lecture, you will learn the following
•How to learn frameworks like CRISP-DM (Cross-Industry Standard Process for Data Mining) to structure your analytics project?
Developing Technical Skills IV

How to learn about key business functions10:36
At the end of this lecture, you will learn the following
•How to learn about key business functions such as marketing, finance, operations, and supply chain
Understand Business Context and Strategy I
How to focus on a specific industry9:32
At the end of this lecture, you will learn the following
•How to focus on a specific industry to understand its unique challenges and data requirements
Understand Business Context and Strategy II
How to hone your ability to ask the right questions and define clear objectives12:42
At the end of this lecture, you will learn the following
How to hone your ability to ask the right questions and define clear objectives for analytics projects
Understand Business Context and Strategy III

Requirements

Willingness to invest 13 hours in doing this course

Description

Want to become a Business Analyst but not sure where to start?

This course helps you build job-ready skills in Business Analytics, Data Analytics, and Statistics—even if you are a beginner.

You will learn how to analyze data, solve business problems, and make data-driven decisions like a professional Business or Data Analyst.

What Makes This Course Different

This is not just theory.

You will learn how Business Analytics actually works in real-world business situations through:

Practical examples
Case studies
Step-by-step frameworks

What You Will Learn

You will gain hands-on understanding of:

Business Analytics vs Data Analytics vs Business Analysis
Data collection, cleaning, and preprocessing
Descriptive, predictive, and prescriptive analytics
Statistics for data analysis and decision-making
Data visualization and dashboard creation
Basics of databases and analytics tools
Connecting analytics with business strategy
Communicating insights using storytelling

Build Complete Analyst Skills

Beyond tools and techniques, you will also learn:

How to think like a Business Analyst
How to solve real business problems using data
How to present insights to stakeholders
How to move from data → insights → decisions

Career Outcomes

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

Understand Business & Data Analytics concepts clearly
Identify and prepare for roles like:
Business Analyst
Data Analyst
Reporting Analyst
Build a roadmap to become job-ready in analytics

Instructor Experience

My deep exposure to Business Analytics began while helping students at IIM Udaipur prepare for Data Analyst roles.

While I already had strong experience in business strategy, communication, and statistics from my corporate career at companies like Unilever and Johnson & Johnson, I further developed expertise in:

Data analytics techniques
Data processing and tools
Real-world business applications

Over the years, I have applied and taught these concepts across industries and learners.

This course brings together practical business understanding + analytics skills to help you succeed.

What Students Are Saying

“Comprehensive curriculum with real-world applications. Perfect for aspiring data professionals.”

“Helped me understand data analytics and apply it confidently in business situations.”

“Complex topics made simple with practical examples and tools like Excel, Python, and SQL.”

Why Enroll

Beginner-friendly and structured learning
Covers both business and technical aspects
Focus on real-world application
Helps you become job-ready

Preview Before You Enroll

You can preview multiple lectures for free to evaluate the course.

If it meets your expectations, enroll and start building your career in Business & Data Analytics.

Your Next Step

Start learning, apply concepts, and move towards becoming a confident Business Analyst or Data Analyst.

This Course is Part of a Structured Learning Path

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

This course is your BUILDER step.

Next Recommended Courses

After completing this course, continue your growth with:

Data Analytics (Starter)

Business Analysis (Builder)

Data Science (Builder)

AI Data Driven Management (Advanced)

Who this course is for:

Aspiring Business Analysts and Data Analysts starting their careers
Students and MBA learners exploring Business Analytics & Data Analytics
Working professionals looking to switch into Business/Data Analyst roles
Non-technical learners who want to understand data analytics and statistics
Managers and professionals who want to make data-driven decisions
Anyone who wants to become a job-ready Business or Data Analyst

Master in Business Analytics for Business Analysts

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 26min

Overview4 lectures • 30min

Case Studies of Excellence4 lectures • 37min

How to become excellent in Business Analytics3 lectures • 21min

Understand data collection, cleaning, and preprocessing16 lectures • 1hr 58min

Master Descriptive Analytics7 lectures • 1hr 3min

Master Predictive Analytics16 lectures • 2hr 16min

Master Prescriptive Analytics11 lectures • 1hr 34min

Developing Technical Skills4 lectures • 43min

Understand Business Context and Strategy3 lectures • 33min

Requirements

Description

Who this course is for: