Welcome
From Fundamentals to Advanced Techniques
Why this book exists
Data Analytics is written to be the single reference that grows with the field itself, rather than a book scoped to one syllabus or one semester. It starts, deliberately, at the very beginning: what data is, how it is classified, and how raw observations become an organized frequency table, the same ground covered on the first page of any basic statistics textbook, then moves into measures of central tendency, dispersion, skewness, and kurtosis, the complete descriptive-statistics toolkit, and now into the foundations of probability, from sample spaces and the addition and multiplication rules through to Bayes’ Theorem. From there, it is designed to keep expanding, module by module, into probability distributions, inferential statistics, data visualization, and hands-on analysis in R and Python, as each part is written.
If you are new to data analytics, that means you can start on page one with confidence that nothing is assumed. If you are already comfortable with the basics, the syllabus page will let you jump straight to whatever is live. Either way, this book is never really finished, and neither is your learning.
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 matters.
Editable R and Python code blocks run in the browser, so you can reproduce every result and experiment as you read — no install needed.
Data types, classification, tabulation, class intervals, frequency distributions, and now central tendency, dispersion, skewness, and kurtosis, worked through with real examples before any shortcut is offered.
New modules are added over time as the book moves from fundamentals toward advanced statistical and analytical techniques.
Browse the modules
Try it now
A quick frequency count, computed in R and in Python, both runnable right here — exactly the kind of thing you’ll be building by the end of Module I:
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 as they come up, while a basic familiarity with numbers and everyday arithmetic is all you need to start. 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 added so far.
About the author
Vijayakumar P is an Educator & Data Analytics Professional with 8+ years in Analytics, AI and HR. UGC-JRF-NET in Management.
References
- Statistics for Management. Levin, Richard I., & Rubin, David S. Pearson.
- Fundamentals of Mathematical Statistics. Gupta, S. C., & Kapoor, V. K. Sultan Chand & Sons.
- Schaum’s Outline of Statistics. Spiegel, Murray R., & Stephens, Larry J. (4th ed.). McGraw-Hill.
- Statistics for Managers Using Microsoft Excel. Levine, David M., Szabat, Kathryn A., & Stephan, David F. Pearson.
- Practical Statistics for Data Scientists. Bruce, Peter, Bruce, Andrew, & Gedeck, Peter. (2020). O’Reilly Media.