Course Name: 

Data Science for AI (AI253)


B.Tech (AI)


Programme Core (PC)

Credits (L-T-P): 

(3-0-2) 4


Introduction to Data science fundamentals, Nature of Data and its characteristics, Total information awareness, Bonferroni's Principle, Rhine’s paradox, Recap of Statistical and Inferential Analysis, Data preprocessing, Data wrangling, Data exploration, Dealing with missing data – single and multiple data imputation, Entropy based techniques, Monte Carlo and MCMC simulations; Correcting inconsistent data – Deduplication, Entity resolution, Pairwise Matching; Fellegi-Sunter Model, Advanced processing- Regression, Correlation, Covariance analysis, Aggregation, Sampling, Dimensionality Reduction; Feature extraction and feature selection; Graph data analysis, Stream processing and online analytics, Dealing with infinite length, concept drift, concept/feature evolution, Visual analytics, Current trends and research.


Jure Leskovec, Anand Rajaraman and Jeffrey Ullman, "Mining of Massive Datasets" Cambridge University Press, 2014
Sinan Ozdemir, "Principles of Data Science - Second Edition" Packt Publishing, 2018
Sam Lau, Joey Gonzalez, and Deb Nolan, “Principles and Techniques of Data Science “
Jeffrey S. Saltz and Jeffrey M. Stanton, "An Introduction to Data Science", Sage Publications, 2017
Davy Cielen, Arno D.B. Meysman, Mohamed Ali Introducing Data Science: Big Data, Machine Learning, and More", 2016
Garrett Grolemund, Hadley Wickham, “R for Data Science”O’Reilly, 2017
Nina Zumel and John Mount, "Practical Data Science with R", 2014


Information Technology

Contact us

Head of the Department,
Department of Information Technology,
National Institute of Technology Karnataka,
SurathkalP. O. Srinivasnagar, Mangalore - 575 025
Ph.:    +91-824-2474056
Email:  hodit [at] nitk [dot] edu [dot] in

Web Admin: Sowmya Kamath S

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