Data Analytics using Machine Learning Techniques on Cloud Platforms examines how machine learning (ML) and cloud computing combine to drive data-driven decision-making across industries. Covering ML techniques, loud-based analytics tools and security concerns, this book provides theoretical foundations and real-world applications in fields like healthcare, logistics and e-commerce. It also addresses security challenges, privacy concerns and compliance frameworks, ensuring a comprehensive understanding of cloud-based analytics.
This book:
Covers supervised and unsupervised learning, including regression, clustering, classification and neural networks
Discusses Hadoop, Spark, Tableau, Power BI and Splunk for analytics and visualization
Examines how cloud computing enhances scalability, efficiency and automation in data analytics
Showcases ML-driven solutions in e-commerce, supply chain logistics, healthcare and education
This book is an essential resource for students, researchers and professionals who seek to understand and apply ML-driven cloud analytics in real-world scenarios.
Dr. Seema Rawat | Professor | AI & Data Science | Innovation & Entrepreneurship
Dr. Seema Rawat, Professor in the Department of Information Technology at Amity School of Engineering and Technology, Amity University Uttar Pradesh Noida, is a distinguished academician and researcher. She has specialization in Deep Learning, Artificial Intelligence, Data Science, Machine Learning, Cloud Computing.
Dr. Seema holds a PhD and M. Tech in Computer Science and Engineering and has 20 years of teaching experience in leading engineering institutes across India and abroad. Dr. Seema has an impressive research portfolio, with high-impact SCIindexed journal papers and Scopus-indexed research papers/book chapters. She has published 70+ research papers, authored books with Elsevier and Springer, and holds more than 15 Indian patents. She serves as a reviewer for top-tier Scopus-indexed journals and editor of various books. She is supervising 5 PhD Scholar in India and 02 Foreign PhD research Scholars.
She is actively involved in professional organizations such as IEEE, ACM, and CSI. Beyond her academic and research accomplishments. Dr. Seema recognized with the Faculty Innovation Excellence Award 2019 by DST, Government of India, she actively contributes to AI research, innovation, and entrepreneurship. Dr. Seema dominates real-world impact as Vice President of UP's Entrepreneurship Council (WICCI). She is Senior Technical Advisor Technical Advisor to DeetyaSoft, Ennoble IP, and MyDigital360.
Dr. Neelu Jyothi Ahuja | Professor & Associate Dean (Academics), School of Computer Science, UPES, Dehradun, Uttarakhand, India
Dr. Neelu Jyothi Ahuja is a professor and associate dean (Academics) at the School of Computer Science, UPES, Dehradun. She earned her PhD in 2010, focusing on developing a rule-based expert system for seismic data interpretation. With 24+ years of experience in teaching, research and project development, she has led numerous AI and machine learning-driven projects addressing real-world challenges.
From 2010 to 2017, she headed the Computing Research Institute, fostering interdisciplinary research. She has successfully delivered R&D projects worth over ?1.5 crores, funded by the Department of Science and Technology (DST), GOI. Her current research focuses on AI-based tutoring tools for learning disabilities and an AI-driven snake trapper. She has supervised 10 PhD scholars and is currently guiding five more.
Dr. Ahuja has received prestigious recognitions, including the Himayan Nari Sakhti Award (2020), IGEN Women Achievers Award (2021), Leading Women Researcher Award (2022) and a research felicitation by UCOST (2022). She has been an invited speaker at national and international forums and serves on key committees such as WHO's Promotion of Assistive Products and DST's Expert Committee for CORE projects. She has also chaired conference sessions and various academic panels.
Her research interests include machine learning, intelligent tutoring systems, AI, expert systems, ICT and object-oriented development. She is an active member of IEEE, ACM and ACM-Women. Passionate about innovative teaching and student engagement, she emphasizes holistic learning beyond classroom boundaries.
Dr. Avita Katal | Associate Professor and Program Leader, School of Computer Science, UPES, Dehradun, Uttarakhand, India
Dr. Avita Katal is a highly regarded academic and researcher in the fields of cloud computing, Internet of Things (IoT) and artificial intelligence. She holds a PhD in the domain of cloud computing, has completed her MTech and BE in computer science engineering.
Dr. Avita Katal is currently Associate Professor in the School of Computer Science at the University of Petroleum and Energy Studies (UPES) in Dehradun, Uttarakhand, India. She serves as the program leader for the BTech program in Computer Science & Engineering with specialization in Cloud Computing and Virtualization Technologies. Dr. Avita Katal holds a Postgraduate Certificate in Academic Practice (PGCAP), enhancing her educational expertise. With over a decade of research experience, Dr. Katal has contributed significantly to the development of advanced algorithms and systems in cloud computing environments, with particular focus on optimization techniques, resource management and cloud security.
Dr. Katal has published extensively in reputed international journals and conferences, where her work has been recognized for its innovation and practical applications. She also serves as a reviewer for numerous prestigious journals and conferences in her field, demonstrating her leadership and expertise in cloud computing, IoT and AI. Her ongoing research aims to bridge the gap between theoretical advancements and their implementation in real-world cloud infrastructures, particularly in the context of scalability, reliability and efficiency.
Dr. Praveen Kumar | Director and Professor in Astana IT University, Astana, Kazakhstan
Dr. Praveen Kumar received his PhD and MTech in Computer Science and Engineering. Currently, he is working as a director and professor in Astana IT University, Astana, Kazakhstan. He has more than 18+ years of experience in teaching and research. He has been awarded the Best PhD thesis, Best Researcher award and Fellow Member of the Indian Institute of Machine Learning for outstanding contribution in AI and ML, recognized by the Government of West Bengal, India. His areas of interest include big data analytics, data mining, ML. etc.
He has to his credit 10 Patents/Copyright and has published more than 140+ research papers in International Journals and Conferences (Scopus Indexed) with Scopus H-Index 16. He is supervising five PhD research scholars in AI, big data analytics and data mining. He has delivered invited talk and guest lecture in Jamia Millia Islamia University, Maharaja Agrasen College of Delhi University, Duy Tan University Vietnam, ECE Paris, France, etc. He has been associated with many conferences throughout the world as a TPC member and session chair, etc.
He has visited various countries like Uzbekistan; Tokyo, Japan; London, UK; Paris, France; Da Nang, Vietnam; Dubai; Russia and Kazakhstan. He is a senior member of IEEE, lifetime member of IETE, member of ACM and member of IET (UK) and other renowned technical societies. He is associated with Corporates, and he is a technical adviser of DeetyaSoft Pvt. Ltd. Noida, IVRguru, MyDigital360, etc.
Prof. (Dr.) Shabana Urooj | Professor, College of Engineering, Princess Nourah Bint Abdulrahman University
Prof. (Dr.) Shabana Urooj (Senior Member, IEEE) presently working as a full professor at the College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Dr. Urooj is persistently contributing for the technical and professional development of society. She has received the bachelor's degree in electrical engineering and the master's degree in electrical engineering (Instrumentation and Control) from Aligarh Muslim University, India. She obtained her doctorate degree from Jamia Millia Islamia (A Central University), Delhi, India. She has served industry for three years and teaching organizations for over 20 years.
She has authored and co-authored more than 250 research articles, which are published in high-quality international journals, reputed conference proceedings and quality books. She has successfully completed several editorial responsibilities for reputable journals and several quality publishers and proceedings. She is presently contributing as an associate editor in Frontiers in Energy Research. She has served as an associate editor of a reputed journal viz. IEEE Sensors Journal in the past.
She was a recipient of the Springer's Excellence in Teaching and Research Award, the American Ceramic Society's Young Professional Award, the IEEE's Region 10 Award for outstanding contribution in Educational Activities, Leadership Excellence Women's Award in University Professor category (Middle East), Research Excellence Award for quality publishing/authorship from Princess Nourah University and several other best paper presentation awards.
Dr. Urooj is serving as an active volunteer of Institute of Electrical & Electronics Engineering-IEEE in various capacities. She is the Chairperson of Education Society Chapter of the IEEE Saudi Arabia Section. She has served IEEE Delhi Section, India, in various potential positions for about a decade.
Autor*in
Amity University Tashkent, Uzbekistan
University of Petroleum and Energy Studies, India
UPES
Amity University Tashkent Uzbekistan
Princess Nourah bint Abdulrahman University, Riyadh
Preface
Author biography
Introduction
1. Data Analytics: An Overview 2. Data Analytics: Tools and Technologies 3. Data Analytics: Statistical Approach 4. Supervised and Unsupervised Methods of Machine Learning used in Data Analytics 5. Opportunities and Challenges for Data Analytics Integrated with Machine Learning 6. Cloud Computing: A Change in the IT Infrastructure Landscape 7. Redefining Data Analytics with Machine Learning and Cloud 8. Data Analytics and Cloud together: A powerful combination for E-commerce and Supply Chain logistics 9. Data Analytics, Machine Learning, and Cloud Together: A Powerful Combination for Healthcare & Education 10. Security and Privacy issues for data analytics using machine learning in cloud computing 11. Future Trends for ML-Based Data Analytics in the Cloud
