Statistics for Data Science & Business Analytics 2026

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!