Affiliation: JNTUK,Master of Technology (M.Tech.) in Data Science. Duration: 2 years all semesters text books semester wise

 For your M.Tech. in Data Science at JNTUK, the textbooks for each semester might vary based on the specific courses offered. Here's a general outline of what textbooks could be for each semester, though you may need to check with your course curriculum for exact details:

Semester 1:

• Mathematics for Data Science:
  • Discrete Mathematics by K.H. Rosen
  • Probability and Statistics for Engineers and Scientists by Ronald E. Walpole
• Programming for Data Science:
  • Python for Data Analysis by Wes McKinney
  • Data Science from Scratch by Joel Grus
• Computer Organization:
  • Computer Organization and Design by David A. Patterson and John L. Hennessy

Semester 2:

• Data Structures and Algorithms:
  • Data Structures and Algorithms in Python by Michael T. Goodrich
  • Introduction to Algorithms by Cormen, Leiserson, Rivest, Stein
• Database Management Systems:
  • Database System Concepts by Abraham Silberschatz
  • SQL for Smarties by Joe Celko
• Statistics and Probability:
  • Probability and Statistics by Morris H. DeGroot

Semester 3:

• Machine Learning:
  • Pattern Recognition and Machine Learning by Christopher M. Bishop
  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
• Big Data Analytics:
  • Big Data: Principles and Paradigms by Rajkumar Buyya
  • Hadoop: The Definitive Guide by Tom White
• Data Mining:
  • Data Mining: Concepts and Techniques by Jiawei Han and Micheline Kamber

Semester 4:

• Deep Learning:
  • Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  • Deep Learning with Python by François Chollet
• Natural Language Processing:
  • Speech and Language Processing by Daniel Jurafsky and James H. Martin
  • Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper
• Data Visualization:
  • Data Visualization with Python by Kyran Dale
  • Interactive Data Visualization for the Web by Scott Murray

Semester 5:

• Artificial Intelligence:
  • Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
  • Artificial Intelligence: Foundations of Computational Agents by David L. Poole and Alan K. Mackworth
• Optimization Algorithms:
  • Introduction to Optimization by Pablo Pedregal
  • Convex Optimization by Stephen Boyd and Lieven Vandenberghe

Semester 6:

• Cloud Computing:
  • Cloud Computing: Concepts, Technology & Architecture by Thomas Erl
  • Cloud Computing for Dummies by Judith Hurwitz
• Blockchain Technology:
  • Mastering Blockchain by Imran Bashir
  • Blockchain Basics by Daniel Drescher

This is a general guide, and your specific curriculum may include other subjects and books. Always check with your department for the most accurate textbook list!