Essentials of Big Data Analytics
Applications in R and Python
- 1st Edition - February 1, 2026
- Imprint: Morgan Kaufmann
- Authors: Pallavi Chavan, Kalyani Pampattiwar, Ramchandra Mangrulkar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 4 5 2 0 6 - 2
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 4 5 2 0 7 - 9
Essentials of Big Data Analytics: Applications in R and Python is a comprehensive guide that demystifies the complex world of big data analytics, blending theoretical concep… Read more
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Essentials of Big Data Analytics: Applications in R and Python is a comprehensive guide that demystifies the complex world of big data analytics, blending theoretical concepts with hands-on practices using the Python and R programming languages and MapReduce framework. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of big data. Essentials of Big Data Analytics is designed to provide a comprehensive resource for readers looking to deepen their understanding of Big Data analytics, particularly within a computer science, engineering, and data science context. By bridging theoretical concepts with practical applications, the book emphasizes hands-on learning through exercises and tutorials, specifically utilizing R and Python. Given the growing role of Big Data in industry and scientific research, this book serves as a timely resource to equip professionals with the skills needed to thrive in data-driven environments.
- Includes hands-on Tutorials and Case Studies: Structured exercises and real-world examples reinforce learning and skill-building
- Focuses on Python and R for Big Data: Detailed lessons in Python and R programming cater to the increasing demand for data science expertise
- Balanced Theory and Practice: Comprehensive coverage ensures a strong theoretical foundation paired with actionable insights for real-world application
Computer Science researchers, data science researchers, and data analysis researchers in academia and industry. The primary audience also includes researchers and professionals in the fields of mathematics, AI, ML, deep learning and those who want to enhance their skills in data mining and analysis
1. Introduction to Big Data Analytics
1.1 Understanding Big Data
1.1.1 Definition and characteristics of Big Data
1.1.2 Volume, Velocity, Variety, Veracity, and Value (5Vs)
1.1.3 Real-time processing challenges
1.2 Types of Big Data
1.2.1 Classifying data into structured, unstructured, and semi-structured types
1.2.2 Examples of each type in various industries
1.3 Significance and Applications of Big Data Analytics
1.3.1 Discussing the importance of deriving insights from Big Data
1.3.2 Applications in business, healthcare, finance, and more
1.3.3 Impact on decision-making and strategic planning
1.4 Basics of Data Science
1.4.1 Core principles and goals of data science
1.4.2 The data science lifecycle
1.4.3 Role of a data scientist
1.4.4 Big Data and Data Science: A Symbiotic Connection
2. Mathematical Foundations
2.1 Statistical Concepts for Big Data
2.1.1 Review of statistical fundamentals
2.1.2 Adaptations for handling large datasets
2.1.3 Significance testing and confidence intervals in Big Data
2.2 R and Python Fundamentals
2.2.1 Basic syntax, data types, structures
2.2.2 Data frames, lists, matrices, and arrays
2.3 Data Exploration and Visualization
2.3.1 Exploratory data analysis (EDA) with R and Python
2.3.2 Visualizing data using ggplot2, Matplotlib, Seaborn, and Plotly
2.3.3 Interpretation of visualizations, packages
3. Big Data Technologies and Programming
3.1 Overview of Big Data Technologies (Hadoop, Spark, etc.)
3.1.1 Introduction to Hadoop, Spark, and other Big Data frameworks
3.1.2 Use cases for each technology
3.2 Introduction to MapReduce
3.2.1 MapReduce programming model
3.2.2 Key concepts: Map phase, Shuffle and Sort, Reduce phase
3.2.3 MapReduce vs. traditional database processing
3.3 R and Python as Programming Languages for Big Data
3.3.1 Capabilities for handling large datasets
3.3.2 Integrating R and Python with Big Data tools
3.4 Integrating R and Python with Distributed Computing
3.4.1 Using R and Python on Hadoop and Spark clusters
3.4.2 Exploring distributed computing frameworks compatible with R and Python
3.4.3 Challenges of distributed R and Python computing
4. Data Ingestion and Preprocessing
4.1 Data Collection Strategies
4.1.1 Strategies for collecting diverse data sources
4.1.2 Challenges in data collection and solutions
4.2 Data Cleaning and Preprocessing
4.2.1 Techniques for cleaning noisy or inconsistent data
4.2.2 Handling missing data, outliers, and imputation (R and Python)
4.3 Feature Engineering and Transformation
5. Big Data Storage and Management
5.1 Storage Architectures for Big Data
5.1.1 Overview of storage solutions like HDFS and distributed databases
5.1.2 Choosing storage solutions based on use cases
5.2 Scalable Data Management
5.2.1 Scalability challenges and solutions
5.2.2 Horizontal and vertical scaling concepts
5.3 Data Warehousing and Data Lakes
5.3.1 Understanding data warehousing and data lakes
5.3.2 Integrating R and Python in analytics on data lakes
6. Advanced MapReduce for Big Data Processing
6.1 Understanding MapReduce Paradigm
6.1.1 Deep dive into the MapReduce framework
6.1.2 Practical use cases for MapReduce
6.2 Implementing MapReduce Jobs
6.2.1 Step-by-step guide on writing and executing a MapReduce job
6.2.2 Common patterns and anti-patterns in MapReduce development
6.3 MapReduce Optimization Techniques
6.3.1 Strategies for optimizing MapReduce jobs
6.3.2 Combiners, partitioning, and compression techniques
7. Machine Learning Techniques for Big Data Processing
7.1 Introduction to Machine Learning in Big Data Context
7.2 Supervised Learning for Big Data
7.3 Unsupervised Learning for Big Data
7.4 Ensemble Learning Techniques
7.5 Optimization Techniques in Big Data Processing
7.5.1 Linear Programming (LP)
7.5.2 Dynamic Programming (DP)
7.5.3 Goal Programming (GP)
7.6 Case Studies on Machine Learning in Big Data
8. Mining Data Streams
8.1 The Stream Data Model
8.1.1 A Data-Stream-Management System
8.1.2 Examples of Stream Sources, Stream Queries
8.2 Sampling and Filtering in Data Streams
8.3 Algorithms for Data Stream Mining
8.3.1 Stream clustering and classification algorithms
8.3.2 Bloom filters and their analysis
9. Case Studies and Practical Applications
9.1 Industry-specific Use Cases
9.1.1 Applications in healthcare, finance, e-commerce, etc.
9.2 Success Stories in Big Data Analytics
9.3 Practical Implementations and Challenges
9.3.1 Implementing solutions using R and Python
9.3.2 Addressing real-world challenges
10. Hands-on Exercises and Tutorials with R, MapReduce, and Data Streams
10.1 Coding Examples in R, Python, and MapReduce
10.2 End-to-End Tutorials for Implementing Big Data Solutions
10.3 Debugging and Optimization Strategies
11. Emerging Trends and Future Directions
11.1 AI, Edge Computing, and IoT Integration
11.2 Real-Time Analytics with Cloud Computing
11.3 Future Research Directions in Big Data (Quantum Computing, Ethics, etc.)
1.1 Understanding Big Data
1.1.1 Definition and characteristics of Big Data
1.1.2 Volume, Velocity, Variety, Veracity, and Value (5Vs)
1.1.3 Real-time processing challenges
1.2 Types of Big Data
1.2.1 Classifying data into structured, unstructured, and semi-structured types
1.2.2 Examples of each type in various industries
1.3 Significance and Applications of Big Data Analytics
1.3.1 Discussing the importance of deriving insights from Big Data
1.3.2 Applications in business, healthcare, finance, and more
1.3.3 Impact on decision-making and strategic planning
1.4 Basics of Data Science
1.4.1 Core principles and goals of data science
1.4.2 The data science lifecycle
1.4.3 Role of a data scientist
1.4.4 Big Data and Data Science: A Symbiotic Connection
2. Mathematical Foundations
2.1 Statistical Concepts for Big Data
2.1.1 Review of statistical fundamentals
2.1.2 Adaptations for handling large datasets
2.1.3 Significance testing and confidence intervals in Big Data
2.2 R and Python Fundamentals
2.2.1 Basic syntax, data types, structures
2.2.2 Data frames, lists, matrices, and arrays
2.3 Data Exploration and Visualization
2.3.1 Exploratory data analysis (EDA) with R and Python
2.3.2 Visualizing data using ggplot2, Matplotlib, Seaborn, and Plotly
2.3.3 Interpretation of visualizations, packages
3. Big Data Technologies and Programming
3.1 Overview of Big Data Technologies (Hadoop, Spark, etc.)
3.1.1 Introduction to Hadoop, Spark, and other Big Data frameworks
3.1.2 Use cases for each technology
3.2 Introduction to MapReduce
3.2.1 MapReduce programming model
3.2.2 Key concepts: Map phase, Shuffle and Sort, Reduce phase
3.2.3 MapReduce vs. traditional database processing
3.3 R and Python as Programming Languages for Big Data
3.3.1 Capabilities for handling large datasets
3.3.2 Integrating R and Python with Big Data tools
3.4 Integrating R and Python with Distributed Computing
3.4.1 Using R and Python on Hadoop and Spark clusters
3.4.2 Exploring distributed computing frameworks compatible with R and Python
3.4.3 Challenges of distributed R and Python computing
4. Data Ingestion and Preprocessing
4.1 Data Collection Strategies
4.1.1 Strategies for collecting diverse data sources
4.1.2 Challenges in data collection and solutions
4.2 Data Cleaning and Preprocessing
4.2.1 Techniques for cleaning noisy or inconsistent data
4.2.2 Handling missing data, outliers, and imputation (R and Python)
4.3 Feature Engineering and Transformation
5. Big Data Storage and Management
5.1 Storage Architectures for Big Data
5.1.1 Overview of storage solutions like HDFS and distributed databases
5.1.2 Choosing storage solutions based on use cases
5.2 Scalable Data Management
5.2.1 Scalability challenges and solutions
5.2.2 Horizontal and vertical scaling concepts
5.3 Data Warehousing and Data Lakes
5.3.1 Understanding data warehousing and data lakes
5.3.2 Integrating R and Python in analytics on data lakes
6. Advanced MapReduce for Big Data Processing
6.1 Understanding MapReduce Paradigm
6.1.1 Deep dive into the MapReduce framework
6.1.2 Practical use cases for MapReduce
6.2 Implementing MapReduce Jobs
6.2.1 Step-by-step guide on writing and executing a MapReduce job
6.2.2 Common patterns and anti-patterns in MapReduce development
6.3 MapReduce Optimization Techniques
6.3.1 Strategies for optimizing MapReduce jobs
6.3.2 Combiners, partitioning, and compression techniques
7. Machine Learning Techniques for Big Data Processing
7.1 Introduction to Machine Learning in Big Data Context
7.2 Supervised Learning for Big Data
7.3 Unsupervised Learning for Big Data
7.4 Ensemble Learning Techniques
7.5 Optimization Techniques in Big Data Processing
7.5.1 Linear Programming (LP)
7.5.2 Dynamic Programming (DP)
7.5.3 Goal Programming (GP)
7.6 Case Studies on Machine Learning in Big Data
8. Mining Data Streams
8.1 The Stream Data Model
8.1.1 A Data-Stream-Management System
8.1.2 Examples of Stream Sources, Stream Queries
8.2 Sampling and Filtering in Data Streams
8.3 Algorithms for Data Stream Mining
8.3.1 Stream clustering and classification algorithms
8.3.2 Bloom filters and their analysis
9. Case Studies and Practical Applications
9.1 Industry-specific Use Cases
9.1.1 Applications in healthcare, finance, e-commerce, etc.
9.2 Success Stories in Big Data Analytics
9.3 Practical Implementations and Challenges
9.3.1 Implementing solutions using R and Python
9.3.2 Addressing real-world challenges
10. Hands-on Exercises and Tutorials with R, MapReduce, and Data Streams
10.1 Coding Examples in R, Python, and MapReduce
10.2 End-to-End Tutorials for Implementing Big Data Solutions
10.3 Debugging and Optimization Strategies
11. Emerging Trends and Future Directions
11.1 AI, Edge Computing, and IoT Integration
11.2 Real-Time Analytics with Cloud Computing
11.3 Future Research Directions in Big Data (Quantum Computing, Ethics, etc.)
- Edition: 1
- Published: February 1, 2026
- Imprint: Morgan Kaufmann
- Language: English
PC
Pallavi Chavan
Dr. Pallavi Vijay Chavan is Professor and head-IT at Ramrao Adik Institute of Technology, D Y Patil Deemed to be University, Navi Mumbai, MH, India. She has been in academics since the past 20 years and working in the area of computing theory, data science and network security. In her academic journey, she published research work in the data science and security domain with reputed publishers including Springer, Elsevier, CRC press and Inderscience. She has published 5 books, 17+ book chapters, 10+ international journal papers and 30+ international conference papers. Two research scholars have successfully completed their PhD under her guidance and presently she is guiding 5 Ph.D. research scholars in the similar domain. She completed her Ph.D. from Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, MH, India in 2017. She secured the first merit position in Nagpur University for the degree of B.E. in Computer Engineering in 2003. She is recipient of research grants from UGC, CSIR and University of Mumbai. She is acting as a reviewer for Elsevier , Inderscience journals. Her firm belief is “Teaching is a mission”.
Affiliations and expertise
Ramrao Adik Institute of Technology, IndiaKP
Kalyani Pampattiwar
Dr. Kalyani Pampattiwar is an Assistant Professor at SIES Graduate School of Technology, Navi Mumbai, MH, India, with 21 years of experience in academia, specializing in blockchain, information security and network security. She earned her doctoral degree in 2023 from D. Y. Patil Deemed to be University, Navi Mumbai, MH, India. She holds the designation of Certified Software Tester and has received research funds from the University of Mumbai. Her research contributions include publications in prestigious international journals, conferences by Inderscience, Springer, and IEEE, as well as book chapters with reputed publishers such as Springer, Elsevier etc. She has received Swayam's "NPTEL Discipline Star" award.
Affiliations and expertise
Department of Computer Engineering, SIES Graduate School of Technology, IndiaRM
Ramchandra Mangrulkar
Dr. Mangrulkar is a Professor of Information Technology at Dwarkadas Sanghvi College of Engineering and has 20 years of teaching experience in the field of intelligent systems and security. He completed his M.Sc. in Computer Science and Engineering from NIT Rourkela. He completed his Ph.D. in Information Security from SGBAU, Amravati. Dr. Mangrulkar is the recipient of grants from UGC as well as AICTE.
Affiliations and expertise
Professor, Department of Information Technology, Dwarkadas Sanghvi College of Engineering, India.
Affiliations and expertise
Assistant Professor – Computer Engineering Dwarkadas Sanghvi College of Engineering, India