The twenty-first century has witnessed an unprecedented explosion in the volume, variety, and velocity of data generated through digital technologies, social media, mobile devices, cloud computing, the Internet of Things (IoT), and business transactions. Organizations across industries are increasingly recognizing data as one of their most valuable strategic assets. The ability to collect, process, analyze, and interpret large volumes of data has become essential for making informed decisions, improving operational efficiency, enhancing customer experiences, managing risks, and achieving sustainable competitive advantage. Consequently, Data Analytics has emerged as one of the most important disciplines in business education, equipping students with the knowledge and analytical skills required to thrive in a data-driven economy.
Recognizing the growing significance of data analytics in modern business and industry, the State Education Policy (SEP) of Bangalore University has introduced Data Analytics as a core course for V Semester B.Com. students. The course has been designed to provide learners with a comprehensive understanding of Big Data concepts, analytics tools and techniques, business applications, governance, security, and emerging trends. This textbook has been meticulously prepared in accordance with the prescribed syllabus and aims to present the subject in a simple, systematic, and application-oriented manner, making it accessible to undergraduate students while maintaining academic rigor.
The book is organized into five carefully structured modules each focusing on a key dimension of Data Analytics.
Module I, Introduction to Big Data
Module II, Big Data Ecosystem and Architecture
Module III, Big Data Analytics Tools and Techniques
Module IV, Business Applications of Big Data Analytics
Module V, Governance, Security, and Emerging Trends in Big Data
Throughout the book, special emphasis has been placed on conceptual clarity, practical relevance, and contemporary developments. Topics such as cloud computing, Hadoop, Apache Spark, artificial intelligence, machine learning, real-time analytics, business intelligence, data visualization, and digital transformation have been presented in a student-friendly manner with suitable illustrations, examples, tables, and case studies. The content bridges the gap between academic learning and real-world business practices, enabling students to understand not only the theoretical foundations of Data Analytics but also its practical applications in modern organizations.
This textbook is designed to serve as a comprehensive learning resource for B.Com. students of Bangalore University, while also being useful to teachers, researchers, professionals, and anyone interested in understanding the rapidly evolving field of Data Analytics. It is my sincere belief that this book will help students develop analytical thinking, problem-solving abilities, and data-driven decision-making skills that are highly valued in today’s business environment.
Contents –
MODULE – 1 INTRODUCTION TO BIG DATA
Meaning and definition of Big Data – Evolution of data and data explosion – Characteristics of Big Data (Volume, Velocity, Variety, Veracity, Value) – Types of Big Data: structured, semi-structured, and unstructured data – Traditional data processing vs Big Data processing – Need for Big Data analytics in business – Big Data applications in finance, marketing, HR, healthcare, and e-Business Administration – Business value of Big Data.
MODULE – 2 BIG DATA ECOSYSTEM AND ARCHITECTURE
Big Data ecosystem – Components of Big Data architecture – Distributed computing concepts – Introduction to Hadoop framework – Hadoop architecture – HDFS (Hadoop Distributed File System): features and advantages – MapReduce: concept and working (introductory) – Role of cloud computing in Big Data – Overview of Big Data platforms and services.
MODULE – 3 BIG DATA ANALYTICS TOOLS AND TECHNIQUES
Introduction to Big Data analytics tools – Overview of Apache Spark – Batch processing vs stream processing – Data ingestion tools (conceptual): Sqoop, Flume, Kafka – Data storage solutions: NoSQL databases (MongoDB, Cassandra – overview) – Big Data analytics workflow – Descriptive, diagnostic, and predictive analytics in Big Data – Visualization of Big Data insights (conceptual).
MODULE – 4 BUSINESS APPLICATIONS OF BIG DATA ANALYTICS
Big Data analytics in marketing: customer segmentation, personalization, and sentiment analysis – Big Data in finance: fraud detection, risk analytics, and algorithmic trading (conceptual) – Big Data in human resource analytics – Big Data in operations and supply chain management – Social media analytics – Real-time analytics and decision-making – Case studies of Big Data applications in Indian and global organizations.
MODULE – 5 GOVERNANCE, SECURITY, AND EMERGING TRENDS IN BIG DATA
Big Data governance – Data quality and data management – Data privacy and security issues – Ethical issues in Big Data analytics – Regulatory framework related to data protection – Big Data analytics and artificial intelligence – Machine learning and Big Data (overview) – Future trends in Big Data analytics – Challenges in implementing Big Data solutions – Career opportunities in Big Data analytics.
