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Yuvijen · Descriptive → Predictive → Prescriptive

Advanced Business Analytics

Turn raw data into evidence-based decisions. Master the full analytics spectrum — from preparing data, through exploratory and confirmatory analysis, to predictive modelling and prescriptive optimization — with every method implemented hands-on in R.

Start reading Browse the syllabus

4

Modules across the analytics spectrum

26

Topic-wise chapters

R + Py

Run code live in the browser

Applied

Real business decision problems

What’s inside every chapter

Concepts, the why

Each method is built up from first principles in plain language, so you understand what the analysis does and when to reach for it.

Run it live in R & Python

Editable R and Python code blocks run right in the browser — experiment with regressions, clusters and optimizations as you read, no install needed.

Descriptive to predictive

From univariate, bivariate and multivariate exploration to multiple, logistic, mediation, moderation, factor and cluster analysis.

Prescriptive optimization

Linear and integer programming, network optimization and simulation modelling — the methods that recommend the best decision, not just describe the data.

Worked, business-grounded

Every technique is anchored in a real managerial problem, with tables, diagrams and step-by-step implementation.

From insight to decision

The book's through-line: translating analytical output into choices that move organizations forward.

Browse the modules

The Four-Module Analytics Spectrum

1Data Fundamentals & PreparationData concepts, structured vs unstructured data, big data, levels of measurement, decision-making and data preparation 2Exploratory & Confirmatory Data AnalysisDescriptive, exploratory and confirmatory analysis, with univariate, bivariate and multivariate tools in R 3Predictive Analytics & Modeling TechniquesMultiple & logistic regression, mediation, moderation, factor and cluster analysis, implemented in R 4Prescriptive Analytics & OptimizationLinear and integer programming, network optimization and simulation modelling for decision recommendations

Tools

Full SyllabusThe complete module-and-topic map of the book Live Analytics LabRun and edit R & Python analyses right in the page

Try it now

The same mean, computed in R and in Python, both runnable right here:

Python

How to use this book

Read a chapter top to bottom the first time: each builds a method from the ground up, shows it on a real business problem, and lets you run the analysis live in R or Python to see the result. Type into the editable blocks and re-run them — that is where the learning sticks. Work through the modules in order, since each builds on the last: prepare your data, explore and confirm what it says, predict with regression and multivariate models, then prescribe the best decision with optimization. The Syllabus page is your map to every module and topic, and the Live Analytics Lab lets you experiment freely.

About the authors

Vijayakumar P is an Educator & Data Analytics Professional with 8+ years in Analytics, AI and HR. UGC-JRF-NET in Management.

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Rani C is an Educator & HR Business Intelligence Professional with 8+ years in HRM and HR Analytics. UGC-NET in Management.

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References

Text Books

• R in Action: Data Analysis and Graphics with R. Kabacoff, R. I. (2022). Manning Publications.
• Business Analytics. Evans, J. R. (2nd ed.). Pearson Education.
• Business Analytics: The Science of Data-Driven Decision Making. Kumar, U. D. (2017). Wiley.

Reference Books

• Machine Learning Using R. Ramasubramanian, K., & Singh, A. Apress.
• Business Intelligence: A Managerial Approach. Turban, E., Sharda, R., Aronson, J., & King, D. (2008). Pearson Prentice Hall.
• Linear Programming with R. Donovan, T. (2020). Handout.
• Integer Programming with R. Donovan, T. (2020). Handout.
• Network Optimization. Letkowski, J. (2021). Handout.