Data Science Fundamentals and Practical Approaches

This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems.
Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language.
Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic.

Tagline

Learn how to process and analysis data using Python

Key Features

* The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code.
* The book is not just dealing with the background mathematics alone or only the programs but beautifully correlates the background mathematics to the theory and then finally translating it into the programs.
* A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions.

What will you learn

* Perform processing on data for making it ready for visual plot and understand the pattern in data over time.
* Understand what machine learning is and how learning can be incorporated into a program.
* Know how tools can be used to perform analysis on big data using python and other standard tools.
* Perform social media analytics, business analytics, and data analytics on any data of a company or organization.

Who this book is for

The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems.

1. Fundamentals of Data Science1
2. Data Preprocessing
3. Data Plotting and Visualization
4. Statistical Data Analysis
5. Machine Learning for Data Science
6. Time-Series Analysis
7. Deep Learning for Data Science
8. Social Media Analytics
9. Business Analytics
10. Big Data Analytics

Dr. Gypsy Nandi is an Assistant Professor (Sr) in the Department of Computer Applications, Assam Don Bosco University, India. Her areas of interest include Data Science, Social Network Mining, and Machine Learning. She has completed her Ph.D. in the field of ‘Social Network Analysis and Mining’. Her research scholars are currently working mainly in the field of Data Science. She has several research publications in reputed journals and book series.

Dr. Rupam Kumar Sharma is an Assistant Professor in the Department of Computer Applications, Assam Don Bosco University, India. His area of interest includes Machine Learning, Data Analytics, Network, and Cyber Security. He has several research publications in reputed SCI and Scopus journals. He has also delivered lectures and trained hundreds of trainees and students across different institutes in the field of security and android app development.