Business Analytics for Decision Making
Welcome
What’s inside every chapter
Every idea is built from first principles in plain language, so you understand what the analysis is doing and why it answers the business question.
Editable R and Python code blocks run in the browser, so you can reproduce every result and experiment as you read — no install needed.
Central tendency, dispersion, skewness, kurtosis, and visualizing it all with scatter, bar, pie, box and line charts.
Nominal, parametric and non-parametric tests — t-tests, ANOVA, correlation, Mann-Whitney, Kruskal-Wallis, Friedman and more.
Simple and multiple linear regression, from the least-squares formula to diagnostics, R-squared, RMSE and MAE.
A decision guide that maps your data and question to the correct statistical test before you run a single line of code.
R and Python basics from the ground up — data structures, functions, NumPy, pandas and SciPy — no prior coding needed.
Browse the modules
Try it now
The same mean, computed in R and in Python, both runnable right here:
How to use this book
Read a chapter top to bottom the first time: each builds the concept from the ground up, shows it in a worked example on a small self-contained dataset, and lets you run the code live in R and Python to see the result. No prior programming experience is required — R and Python are introduced from scratch — while a basic familiarity with statistics helps you get the most from the analytical chapters. Type into the editable blocks and re-run them; that is where the learning sticks. When you need a map, the Syllabus page lays out every module and topic.
About the authors
Vijayakumar P is an Educator & Data Analytics Professional with 8+ years in Analytics, AI and HR. UGC-JRF-NET in Management.
Rani C is an Educator & HR Business Intelligence Professional with 8+ years in HRM and HR Analytics. UGC-NET in Management.
References
- R for Everyone: Advanced Analytics and Graphics. Lander, Jared P. (2017). Addison-Wesley.
- Statistics for Management. Levin, Richard I., & Rubin, David S. Pearson.
- Numerical Python: Scientific Computing and Data Science Applications with NumPy, SciPy and Matplotlib. Johansson, Robert. (2019). (2nd ed.). Apress.
- Python for Data Analysis. McKinney, Wes. (2013). O’Reilly.
- R in Action — Data Analysis and Graphics with R. Kabacoff, Robert. (2022). Manning Publications.
- Practical Business Analytics Using R and Python. Hodeghatta, Umesh R., & Nayak, Umesh. (2023). Apress.
- A Handbook of Statistical Analyses Using R. Everitt, Brian S., & Hothorn, Torsten. (2005). Chapman and Hall/CRC.
- Practical Statistics for Data Scientists. Bruce, Peter, Bruce, Andrew, & Gedeck, Peter. (2020). O’Reilly Media.