Statistics for Data Science & Business Analytics 2026

Learn key statistical concepts and techniques to analyze data with IIM Ahmedabad Alumnus

Master the Fundamentals of Statistics and Elevate Your Data Analysis Skills!

Are you looking to build a strong foundation in statistics for data science, analytics, and decision-making? This beginner-to-intermediate level course is designed to simplify complex statistical concepts with real-world applications, helping you gain confidence in data analysis, hypothesis testing, and regression modeling.

What You’ll Learn:

Module 1: Introduction to Statistics –

1. Understand the role of statistics in data science

2. types of data

3. key distinctions between descriptive & inferential statistics.

Module 2: Descriptive Statistics –

1. Learn about measures of central tendency (mean, median, mode)

2. Measure of dispersion (range, variance, standard deviation)

3. visualize data using histograms, box plots, and scatter plots.

Module 3: Probability Basics –

1. Master probability concepts

2. conditional probability

3. Bayes’ theorem

Module 4: Probability Distributions –

1. Understanding Discrete and Continuous Probability distributions

2. Binomial Distribution

3. Poisson Distribution

4. Normal Distribution

5. Exponential Distribution

6. Central Limit Theorem(CLT)

Module 5: Inferential Statistics –

1. Dive into sampling techniques

2. What is confidence intervals

3. hypothesis testing

4. Types of errors

5. t-tests & z-tests

6. Understanding chi-square test

Module 6: Regression & Correlation –

1. Understanding correlation and Causation

2. Linear Regression Analysis-hands on exercise over EXCEL

3. Linear Regression Analysis-hands on exercise over R studio

4. Interpreting the Linear Regression equation

5. Interpreting the key summary statistics of Regression

6. Key assumptions and limitations in Simple Linear Regression

Module 7: Multiple Linear Regression

1. Understanding Multi Linear Regression

2. Hands on exercise on R studio for Multi linear Regression

3. Interpreting the key Summary statistics of the output

4. Understanding the ANOVA table

5. Remodelling based on ANOVA and regression summary stats

Module 8: Logistic Regression

1. Introduction to Logistics Regression

2. Hands on exercise on R studio for Logistics Regression

3. Interpreting the Key Summary Statistics of Logistics regression model

4. Understanding AIC

5. Understanding the confusion matrix : Accuracy, Precision & Recall

6. Understanding the ROC curve and AUC

7. Fine tuning model for better results

8. Understanding the Cost Matrix and solving a real business problem

Who Is This Course For?

• Aspiring Data Scientists, Business Analysts, and Researchers

• MBA & Analytics Students looking to strengthen their statistical foundation

• Students & professionals in Finance, Marketing, and Business Strategy

• Anyone looking to interpret and analyze data effectively

Why Take This Course?

• Beginner-friendly explanations with real-world examples

• No prior advanced math required—concepts explained intuitively

• Hands-on learning with statistical tools and visualization techniques

• Essential for careers in data-driven decision-making

By the end of this course, you’ll be able to confidently apply statistical techniques to analyze data, test hypotheses, and build predictive models—a must-have skill set for any data professional!

Ready to turn data into actionable insights? Enroll now and master statistics for real-world decision-making!