Data Science Fundamentals and Practical Approaches

Sharma Dr. Gypsy Nandi Dr. Rupam Kumar

Description

Learn how to process and analysis data using Python Key Features a- 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. a- The book is quite well balanced with programs and illustrative real-case problems. a- The book not only deals with the background mathematics alone or only the programs but also beautifully correlates the background mathematics to the theory and then finally translating it into the programs. a- A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. Description 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. What will you learn a- Understand what machine learning is and how learning can be incorporated into a program. a- Perform data processing to make it ready for visual plot to understand the pattern in data over time. a- Know how tools can be used to perform analysis on big data using python a- 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. Table of Contents 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 About the Authors 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.

Sujets

• Informatique
• Artificial intelligence

Informations

• Publié par: BPB Publications
• Date de parution: 03 septembre 2020
• Nombre de lectures: 0
• EAN13: 9789389845679
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Data Science Fundamentals and Practical Approaches

Understand Why Data Science is the Next

by
Dr. Gypsy Nandi
Dr. Rupam Kumar Sharma
FIRST EDITION 2020
Copyright © BPB Publications, India
ISBN: 978-93-89845-662
All Rights Reserved. No part of this publication may be reproduced or distributed in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication.
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The information contained in this book is true to correct and the best of author’s & publisher’s knowledge. The author has made every effort to ensure the accuracy of these publications, but cannot be held responsible for any loss or damage arising from any information in this book.
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Dedicated to
All the Enthusiastic Learners Who Thrive on Exploring and Learning for Knowledge Building
About the Authors
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. She is also actively involved in mentoring students for consultancy-based competitions and startup funds. She has won the national-level Smart India Hackathon and the state-level Ideathon many times and has successfully carried out two sanctioned consultancy-based government projects funded by AICTE and UNDP. She has also co-authored a book on “Soft Computing – Fundamentals and Practical Approaches,” published by Studium Press. With her teaching experience of more than 15 years, she is actively involved in various research work related to Data Science and Social Media Analytics.
Dr. Rupam Kumar Sharma is an Assistant Professor (Sr.) 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. He has also co-authored a book on “Soft Computing – Fundamentals and Practical Approaches,” published by Studium Press. He is a good mentor to students, and many of his students are working in a reputed research and corporate institutes abroad. He also has added to his list research grants from DSIR(Department of Scientific and Industrial Research). With more than ten years of teaching experience, he is a passionate teacher and welcomes community and group work with young minds.
Acknowledgement
We thank the Almighty, whose blessings are always endowed on us and have enabled us to remain determined and focused to overcome with courage every hurdle that came in our way. Deep from the heart, we revere every moral support that is extended to us by our family members at all times said and continue to do so from time immemorial. We also acknowledge the respect and learning endurance of students that have motivated us to write this book for a global reach of knowledge to every aspiring student. Lastly, we thank BPB Publications for all the support and cooperation extended throughout for actualizing the project in time.
Preface
This book introduces the fundamental concepts of different tools and techniques related to Data Science that are widely used in a variety of applications such as statistical analytics, business data analytics, social media analytics, and big data analytics. 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 each Chapter describes the fundamentals together with how various data analysis techniques can be implemented using different tools and libraries of Python programming language.
Readers with previous knowledge of python programming will find it easy to understand the program examples presented in the chapters. Each chapter contains numerous examples and illustrative output that explains the important 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.Over the ten chapters in this book, you will learn the following:
Chapter 1 discusses the various aspects of Data science, such as the importance and need of Data Science in today’s marketing trend. The main discussions covered in this Chapter are the complete life cycle of data analytics, the various types of data analytics, and the major tools required for data analysis. Coverage of the role of SQL in Data Science has been provided, as well as the various pros and cons of studying Data Science are explained to understand the current demand of this area of study. All the topics discussed in this Chapter are crucial to be learned by a reader for exploring Data science and for building a career in Data Science, which is and will remain as one of the most demanding careers till the next few decades.
Chapter 2 gives a brief introduction to data preprocessing and its need for data analytics. The various data types and the possible error types that occur on data are also discussed in detail. Various standard data preprocessing operations are elaborately explained, starting from data cleaning, data integration, data transformation, data reduction, and data discretization. For each data preprocessing method, simple examples are provided, and the corresponding Python code is given to demonstrate how the preprocessing operation works.
Chapter 3 gives a detailed explanation of the importance of data plotting and data visualization in data analytics. The importance and use of various data visualization graphs have been discussed. Several basic, specialized, and advanced visualization tools and the corresponding libraries used for each tool have been covered in detail. For each visualization tool, illustrative examples with well-explained Python code and corresponding output are also provided. This chapter covers an important concept of Data Science as the findings in a visualization graph may be subtle, yet it can create a profound impact on a data analyst to interpret the information easily.
Chapter 4 introduces the importance of statistics in data analysis. The chapter starts with the role of statistics in data analysis, and then it elaborately discusses the two main kinds of statistics commonly used for data analysis, namely the descriptive statistics and the inferential statistics. The chapter also introduces probability theory and explains the various concepts related to probability theory. The later part of the chapter discusses Bayesian probability and provides illustrations and Python code for the same.
Chapter 5 discusses in detail the concepts of machine learning used for data analysis. The chapter initially introduces the concept of machine learning and its primary role in data analysis. The various machine learning techniques that have evolved and established their presence in data analytics, such as supervised learning, unsupervised learning, and reinforcement learning, have been discussed in detail in the chapter.
Chapter 6 starts with an overview of time-series analysis and also discusses the important characteristics of time-series data. The latest standard time-series models are discussed along with the mathematical foundations and corresponding related illustrative examples. Each of the time-series modeling techniques is also explained using appropriate Python programs to correlate the applicability of the theory to applications.
Chapter 7 gives an overview of Deep learning relating to Data Science. The prerequisite concepts of deep learning are elaborately discussed here. The chapter also broadly explains the two main deep learning techniques that are widely used for data analysis, namely the Convolutional Neural Network and the AutoEncoder. Each of the deep learning techniques is explained using appropriate Python programs to correlate the applicability of the theory to applications.
Chapter 8 gives a detailed overview of social media analytics and emphasizes the impact of data and analytics on social media. Social media data is huge and highly dynamic and unstructured. The chapter discusses how such data on social media can be extracted for further analysis. It also explains the various important analyses carried out for social media data such as social network analysis, text analysis, and trend analysis.
Chapter 9 introduces to readers an overview of business analytics and its role in making decisions for businesses. The m