Business Analytics for Agriculture

Data Science & Machine Learning for Agribusiness

Business Analytics for Agriculture

A hands-on companion that turns agricultural data into decisions — blending data science, statistics and machine learning with live R and Python you can run right in the browser, all anchored in real agribusiness case studies.

Start reading Browse the syllabus
2
Blocks from data science to deep learning
23
Topic-wise chapters across the syllabus
R & Python
Live, runnable code in your browser
Agri
Case studies in precision farming & IoT

What’s inside every chapter

Concept, then code

Every technique is explained from first principles, then put to work in runnable R and Python — so you learn the idea and the implementation together.

Live in-browser labs

WebR and Pyodide cells let you edit and run code on the page — no install, no setup, no cloud account required.

Statistics that decide

From descriptive measures to t-tests, ANOVA and Chi-square — hypothesis testing applied to real agricultural questions.

Machine & deep learning

Regression, classification, clustering, decision trees and neural networks, built up step by step with worked examples.

Agribusiness context

Forecasting, precision farming and IoT-driven solutions ground every method in problems the agriculture sector actually faces.

Multi-tool fluency

Hands-on work across R, Python, Excel and SPSS builds the flexibility to translate data into productivity, profit and sustainability.

Browse the modules

The Two-Block Syllabus
1Block I — IntroductionData science foundations, analytics & ML concepts, R and R Studio, data preparation, visualization, the analytical cycle, descriptive and inferential statistics 2Block II — Machine and Deep LearningSupervised learning, regression & classification, advanced ML and unsupervised methods, clustering, forecasting, neural networks and agribusiness deep-learning applications
Tools
Full SyllabusThe complete block, unit and topic map of the course Live Analytics LabRun code, statistics and dashboards in your browser — no install

How to use this book

Read each topic top to bottom the first time: the concept comes first, then the runnable R and Python so you can change the inputs and watch the output update on the page. Block I builds your foundation — data science, R, data preparation, visualization and the statistical toolkit (descriptive measures through t-tests, ANOVA and Chi-square). Block II takes you into machine and deep learning, from regression and clustering to neural networks and IoT applications in agribusiness. Try every live cell as you go, and use the Syllabus page as your map to the whole book and the Live Analytics Lab when you want a free, install-free workspace.

Inclusion of R & Python codes in this book

About the authors

Photo of Vijayakumar P

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

Read full profile ↗

Photo of Rani C

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

Read full profile ↗

Reference books

  • R for Everyone — Advanced Analytics and Graphics. Lander, Jared P.
  • Statistics for Management. Levin, Richard I., & Rubin, David S.
  • 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.
  • Python for Data Analysis. McKinney, Wes. (2013). O’Reilly.
  • Numerical Python: Scientific Computing and Data Science Applications with NumPy, SciPy and Matplotlib. Johansson, Robert. (2019). (2nd ed.). Apress.

```