catalog
Home
>

Higher Education

shoe image

Data Analytics : Transforming Data into Insights

Author(s): J. Indumathi

ISBN: 9789366601779

1st Edition

Copyright: 2026

India Release: 2025

₹899

Binding: Paperback

Pages: 846

Trim Size: 241 × 181

Refer Book

Order Inspection Copy

A comprehensive guide that helps readers turn large volumes of data into actionable insights for informed decision-making. The book progresses from basic to advanced concepts, making it suitable for students, professionals, and academicians. It explains essential analytics concepts such as trend identification and data-driven decision-making in accessible language. Covering key topics such as clustering and anomaly identification, it emphasizes analytical thinking, clear communication, and the importance of data narratives. This resource empowers readers to navigate the data landscape confidently and effectively apply data analytics in various real-world contexts.

  • It features real-world case studies from various sectors such as healthcare, finance, marketing, and logistics, demonstrating how data analytics can solve real-world problems.
  • It covers techniques such as data visualization, predictive modeling, and exploratory data analysis and provides a practical presentation of popular tools such as Python.
  • It includes practical exercises using popular Python, SQL, and Power BI tools.
  • It emphasizes effective communication and storytelling with data, using data storytelling principles and visualization best practices.
  • The nontechnical language is accessible, with flowcharts, graphs, and illustrations to visually explain complex concepts.
  • This book applies to various fields such as business, finance, healthcare, marketing, logistics, and more.
  1. Introduction to Data Analytics
  2. Fundamentals of Data and Big Data
  3. Data Analytics Lifecycle (End-To-End Project Management)
  4. Probability and Statistical Foundations for Data Analytics
  5. Statistical Measures
  6. Statistical Inference for Business Decision-Making
  7. Descriptive Analytics Using Statistics
  8. Predictive Analytics and Risk Forecasting
  9. Exploratory Data Analysis (EDA)
  10. Mining Data Streams
  11. Real Time Analytics
  12. Frequent Item Sets and Clustering
  13. Big Data Frameworks
  14. Data Integration and Processing Pipelines
  15. Real-World Applications of Big Data Frameworks (Available Online)
  16. Big Data Architecture & Performance Optimization (Available Online)
  17. Predictive Modeling and Machine Learning
  18. Unsupervised Learning Models
  19. Supervised Learning Models
  20. Advanced Learning Models
  21. Real-World Applications of Machine Learning in Data Analytics (Available Online)
  22. Model Deployment and Monitoring (Available Online)
  23. Foundations of Data Visualization
  24. Data Visualization Tools and Ecosystems for Modern Analytics
  25. Foundations of Scientific Computing in Python
  26. Data Wrangling and Statistical Modeling with Pandas and Statsmodels
  27. Advanced Visualization Techniques (Available Online)
  28. Decision Science and Optimization for Business (Available Online)
  29. Industry-Specific Analytics (Available Online)
  30. Data Analytics for IoT (Available Online)
  31. Text and NLP Analytics (Available Online)
  32. Capstone Projects and Practical Applications (Available Online)

Dr. J. Indumathi is currently working as Professor in the Department of Information Science and Technology, College of Engineering, Sardar Patel Road, Guindy, Anna University, Chennai