Business Analytics for Decision Making

Author

Vijayakumar P

Published

Apr 22, 2026

Welcome

Business Analytics for Decision Making is a practical guide to using data for better business decisions. It blends theory with hands-on application using MS Excel, R, and Python, so readers can move from concept to analysis with confidence.

The book covers the foundations of business analytics and R, descriptive and diagnostic analytics (measures of central tendency, dispersion, skewness, kurtosis, and nominal tests), inferential statistics with parametric and non-parametric tests, and data visualization along with Python essentials including NumPy, pandas, and SciPy. Whether you are a student entering data science or a professional sharpening your data-driven decision-making, it offers the tools and worked examples needed to apply business analytics in the real world.

No prior programming experience is required, since the book introduces R and Python from the ground up, while a basic familiarity with statistics will help readers get the most out of the analytical chapters. All worked examples use small, self-contained datasets so readers can reproduce every result on their own machine and adapt the code to their own data.

Inclusion of R & Python codes in this book

About the Authors

Vijay

Vijayakumar P is an accomplished educator and data professional with over eight years of teaching and research experience in business analytics, data science, and HR analytics. He is a UGC Junior Research Fellow (JRF) and has qualified the UGC NET and SET in Management multiple times, reflecting his strong academic foundation.

Proficient in Python, R, STATA, SPSS, AMOS, PLS-SEM, Tableau, Power BI, Google Looker Studio, GitHub, and Excel, he has guided students and professionals in transforming data into actionable insights. His teaching spans business analytics using R and Python, HR metrics and dashboards, business forecasting, and business research methods, and he has also developed interactive eBooks to enhance hands-on learning.

References

  • R for Everyone 🔗

  • Statistics for Management (Levin, Rubin) 🔗

  • 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.

Text Books

  • 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).
  • Practical Statistics for Data Scientists. Bruce, Peter, Bruce, Andrew, & Gedeck, Peter. (2020). O’Reilly Media.