sample R code
x=15
y=3
x/y
[1] 5
Vijayakumar P
Jan 11, 2024
Business Analytics for Decision Making is a comprehensive guide designed to equip readers with the essential skills and knowledge for applying business analytics in decision-making processes. This book seamlessly integrates theoretical concepts with practical applications, using MS Excel and two of the most powerful programming languages in the data science field: R and Python.
It covers a broad spectrum of data analytics. With an emphasis on data structures, statistical tests, data visualization techniques, and utilization of key packages from Python and R, this book is a practical resource for those looking to master business analytics for real-world applications.
Whether you’re a student stepping into the world of data science or a professional seeking to enhance your decision-making skills through data-driven insights, this book provides the tools and insights needed to navigate the complex landscape of business analytics
Throughout the book, the R & Python codes used for the analysis are included in this document as shown below. And the output of each code is given below the code.
sample R code
x=15
y=3
x/y
[1] 5
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.
Vijayakumar’s work is marked by clarity, precision, and innovation—whether in research, classroom teaching, or applied analytics—making data more accessible, impactful, and meaningful for decision-making.
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.