1st Edition

Data Science and Data Analytics Opportunities and Challenges

Edited By Amit Kumar Tyagi Copyright 2022
    482 Pages 232 B/W Illustrations
    by Chapman & Hall

    482 Pages 232 B/W Illustrations
    by Chapman & Hall

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    Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues.

    Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy.


    • Gives the concept of data science, tools, and algorithms that exist for many useful applications
    • Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems
    • Identifies many areas and uses of data science in the smart era
    • Applies data science to agriculture, healthcare, graph mining, education, security, etc.

    Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm’s productivity.

    Section I: Introduction about Data Science and Data Analytics

    1. Data Science and Data Analytics: Artificial Intelligence and Machine Learning Integrated Based Approach

    Sumika Chauhan, Manmohan Singh, and Ashwani Kumar Aggarwal

    2. IoT Analytics/Data Science for IoT

    T. Perarasi, R. Gayathri, M. Leeban Moses, and B. Vinoth

    3. A Model to Identify Agriculture Production Using Data Science Techniques

    D. Anantha Reddy, Sanjay Kumar, and Rakesh Tripathi

    4. Identification and Classification of Paddy Crop Diseases Using Big Data Machine Learning Techniques

    Anisha P. Rodrigues, Joyston Menezes, Roshan Fernandes, Aishwarya, Niranjan N. Chiplunkar, and Vijaya Padmanabha

    Section II Algorithms, Methods, and Tools for Data Science and Data Analytics

    5. Crop Models and Decision Support Systems Using Machine Learning

    B. Vignesh and G. Suganya

    6. An Ameliorated Methodology to Predict Diabetes Mellitus Using Random Forest

    Arunakumari B. N., Aman Rai, and Shashidhar R.

    7. High Dimensionality Dataset Reduction Methodologies in Applied Machine Learning

    Farhan Hai Khan and Tannistha Pal

    8. Hybrid Cellular Automata Models for Discrete Dynamical Systems

    Sreeya Ghosh and Sumita Basu

    9. An Efficient Imputation Strategy Based on Adaptive Filter for Large Missing Value Datasets

    S. Radhika, A. Chandrasekar, and Felix Albu

    10. An Analysis of Derivative-Based Optimizers on Deep Neural Network Models

    Aruna Pavate and Rajesh Bansode

    Section III: Applications of Data Science and Data Analytics

    11. Wheat Rust Disease Detection Using Deep Learning

    Sudhir Kumar Mohapatra, Srinivas Prasad, and Sarat Chandra Nayak

    12. A Novel Data Analytics and Machine Learning Model towards Prediction and Classification of Chronic Obstructive Pulmonary Disease

    Sridevi U. K., Sophia S., Boselin Prabhu S.R., Zubair Baig, and P. Thamaraiselvi

    13. A Novel Multimodal Risk Disease Prediction of Coronavirus by Using Hierarchical LSTM Methods

    V. Kakulapati, BasavaRaju Kachapuram, Appiah Prince, and P. Shiva Kalyan

    14. A Tier-based Educational Analytics Framework

    Javed Nazura and Paul Anand

    15. Breast Invasive Ductal Carcinoma Classification Based on Deep Transfer Learning Models with Histopathology Images

    Saikat Islam Khan, Pulak Kanti Bhowmicka, Nazrul Islama, Mostofa Kamal Nasira, and Jia Uddin

    16. Prediction of Acoustic Performance Using Machine Learning Techniques

    Ratnavel Rajalakshmi, S. Jeyanthi, Yuvaraj L., Pradeep M., Jeyakrishna S., and Abhishek Krishnaswami

    Section IV: Issue and Challenges in Data Science and Data Analytics

    17. Feedforward Multi-Layer Perceptron Training by Hybridized Method between Genetic Algorithm and Artificial Bee Colony

    Aleksa Cuk, Timea Bezdan, Nebojsa Bacanin, Miodrag Zivkovic, K. Venkatachalam, Tarik A. Rashid, and V. Kanchana Devi

    18. Algorithmic Trading Using Trend Following Strategy: Evidence from Indian Information Technology Stocks

    Molla Ramizur Rahman

    19. A Novel Data Science Approach for Business and Decision Making for Prediction of Stock Market Movement Using Twitter Data and News Sentiments

    S. Kumar Chandar, Hitesh Punjabi, Mahesh Kumar Sharda, and Jehan Murugadhas

    20. Churn Prediction in Banking the Sector

    Shreyas Hingmire, Jawwad Khan, Ashutosh Pandey, and Aruna Pavate

    21. Machine and Deep Learning Techniques for Internet of Things Based Cloud Systems

    Raswitha Bandi and K. Tejaswini

    Section V: Future Research Opportunities towards Data Science and Data Analytics

    22. Dialect Identification of the Bengali Language

    Elizabeth Behrman, Arijit Santra, Siladitya Sarkar, Prantik Roy, Ritika Yadav, Soumi Dutta, and Arijit Ghosal

    23. Real-Time Security Using Computer Vision

    Bijoy Kumar Mandal and Niloy Sarkar

    24. Data Analytics for Detecting DDoS Attacks in Network Traffic

    Ciza Thomas and Rejimol Robinson R.R.

    25. Detection of Patterns in Attributed Graph Using Graph Mining

    Bapuji Rao

    26. Analysis and Prediction of the Update of Mobile Android Version

    Aparna Mohan and R. Maheswari


    Amit Kumar Tyagi is Assistant Professor (Senior Grade), and Senior Researcher at Vellore Institute of Technology (VIT), Chennai Campus, India.

    He earned his PhoD. in 2018 from Pondicherry Central University, India. He joined the Lord Krishna College of Engineering, Ghaziabad (LKCE) from 2009-2010, and 2012-2013. He was an Assistant Professor and Head -  Research, Lingaya’s Vidyapeeth (formerly known as Lingaya’s University), Faridabad, Haryana, India in 2018-2019. His current research focuses on Machine Learning with Big data, Blockchain Technology, Data Science, Cyber Physical Systems, Smart and Secure Computing and Privacy. He has contributed to several projects such as "AARIN" and "P3- Block" to address some of the open issues related to the privacy breaches in Vehicular Applications (such as Parking) and Medical Cyber Physical Systems (MCPS). He has published more than 8 patents in the area of Deep Learning, Internet of Things, Cyber Physical Systems and Computer Vision. He was recently awarded best paper award for paper titled "A Novel Feature Extractor Based on the Modified Approach of Histogram of oriented Gradient", ICCSA 2020, Italy (Europe). He is a regular member of the ACM, IEEE, MIRLabs, Ramanujan Mathematical Society, Cryptology Research Society, and Universal Scientific Education and Research Network, CSI and ISTE.