Semester 1: Introduction to Big Data Analytics-

  1. Introduction to Big Data
    • Definition of Big Data
    • Characteristics of Big Data (Volume, Velocity, Variety, Veracity)
    • Importance and applications of Big Data Analytics
  2. Data Collection and Storage
    • Data sources (structured, semi-structured, unstructured)
    • Data acquisition and collection techniques
    • Data storage technologies (relational databases, NoSQL databases)
  3. Data Preprocessing
    • Data cleaning and transformation
    • Data integration and aggregation
    • Data quality assessment
  4. Programming for Big Data
    • Introduction to programming languages (Python, R)
    • Data manipulation and analysis using programming languages

Reference Books:

  1. “Big Data: A Revolution That Will Transform How We Live, Work, and Think” by Viktor Mayer-Schönberger and Kenneth Cukier
  2. “Big Data Analytics: Turning Big Data into Big Money” by Frank J. Ohlhorst
  3. “Big Data Analytics: Methods and Applications” edited by S. Srinivasan, V. G. S. Kumar, and K. G. Srinivasagan
  4. “Big Data Analytics: A Practical Guide for Managers” by Kim H. Pries and Robert Dunnigan
  5. “Big Data: Principles and best practices of scalable realtime data systems” by Nathan Marz and James Warren
  6. “Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking” by Foster Provost and Tom Fawcett