• V. Sasikumar et al., A Course in Listening and Speaking I, 2nd Edition, Cambridge University Press, 2006.
• Raymond Murphy, English Grammar in Use, 5th Edition, Cambridge University Press, 2019.
• Alice Oshima and Ann Hogue, Introduction to Academic Writing, 3rd Edition, Pearson Longman, 2007.

• Oxford Advanced Learner's Dictionary of English, 9th Edition, 2016.
• John and Liz Soars, New Headway, 3rd Edition, Oxford University Press, 2007.

• Wayne Winston, Microsoft Excel Data Analysis and Business Modeling, 7th Edition, Pearson Education, Inc., 2022.
• Michael Alexander, Richard Kusleika, John Walkenbach, Excel 2019 Bible, 1st Edition, Wiley, 2018.

• Gordon S. Linoff, Data Analysis Using SQL and Excel, 2nd Edition, Wiley Publishing Inc., 2016.

• Hogg, R.V., McKean, J. and Craig, A.T., Introduction to Mathematical Statistics, 7th Edition, Pearson Education, 2012.
• Rice, J.A., Mathematical Statistics and Data Analysis, 3rd Edition, Cengage Learning, 2006.

• Rohatgi, V.K. and Saleh, A.M.E., An Introduction to Probability and Statistics, 3rd Edition, John Wiley & Sons, 2015.

• Kernighan B.W and Dennis M. Ritchie, The C Programming Language, 2nd Edition, Pearson Education India, 2015.
• Byron S. Gottfried, Programming with C, 4th Edition, McGraw Hill (Schaum's Outlines), 2018.

• David Poole, Linear Algebra: A Modern Introduction, 4th Edition, Cengage Learning, 2014.
• Seymour Lipschutz and Marc Lars Lipson, Linear Algebra, 4th Edition, McGraw Hill (Schaum’s Outline Series), 2009.

• Rafael A. Irizarry, Introduction to Data Science: Data Analysis and Prediction Algorithms with R, 1st Edition, CRC Press, 2019.
• Hui Lin and Ming Li, Practitioner’s Guide to Data Science, 1st Edition, Chapman & Hall/CRC, 2023.

• Joan Lambert and Steve Lambert, Windows 10 Step by Step, Microsoft Press, 2015.
• Mark Sobell and Matthew Helmke, A Practical Guide to Linux Commands, Editors, and Shell Programming, 4th Edition, Addison-Wesley, 2017.

• Randal E. Bryant and David R. O’Hallaron, Computer Systems: A Programmer’s Perspective, 3rd Edition, Pearson Education Limited, 2016.

• Charles Dierbach, Introduction to Computer Science using Python: A Computational Problem-Solving Focus, 1st Edition, Wiley, 2015.
• Mark Lutz, Learning Python, 5th Edition, O’Reilly Media, Inc., 2013.

• Wes McKinney, Python for Data Analysis, O’Reilly Media, Inc., 2012.

• S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th Edition, Pearson, 2020.
• R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction, 2nd Edition, MIT Press, 2018.

• Michael T. Goodrich, Roberto Tamassia, and Michael H. Goldwasser, Data Structures and Algorithms in Python, John Wiley & Sons, 2013.
• Mark Allen Weiss, Data Structures and Algorithm Analysis in C++, 4th Edition, Pearson Education, Inc., 2014.

• T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, Prentice-Hall of India, 2009.
• A. V. Aho, J. E. Hopcroft, and J. D. Ullman, The Design and Analysis of Computer Algorithms, Pearson Education, 2006.

• Pierre Lafaye de Micheaux, Rémy Drouilhet, Benoit Liquet, The R Software: Fundamentals of Programming and Statistical Analysis, Springer, 2013.
• Alain F. Zuur, Elena N. Ieno, Erik H.W.G. Meesters, A Beginner's Guide to R (Use R), Springer, 2009.
• Christian Heumann, Michael Schomaker, Shalabh, Introduction to Statistics and Data Analysis with Exercises, Solutions and Applications in R, Springer, 2016.

Relational DBMS: Entity relationship (ER) Model, relational algebras; SQL queries, constraints, triggers; SQL and front-end tools; Storage and file structure: Overview of secondary storage, RAID and flash storage, indexing (tree, hash, and bitmap), implementation of relational operators; Transaction management: ACID properties, concurrency control, crash recovery.

Introduction to Databases: Concepts, importance, and types; Relational Data Model: Fundamentals, data integrity, normalization; Entity-Relationship (ER) Model; Storage and File structure; Structured Query Language (SQL); Database Design; Database Administration; Transaction management; Database Connectivity and Application Development; NoSQL Databases

Practical Component: Assignments based on theory.

• R. Ramakrishnan, J. Geherke, Database Management Systems, Third Edition, McGraw Hill, 2014.
• Elmasri Ramez and Navathe Shamkant, Fundamentals of Database System, Seventh Edition, Pearson Education, 2017.

• H. Garcia-Molina, J. Ullman, J. Widom, Database System: The Complete Book, Second Edition, Pearson, 2013.
• P. Bailis, J. Hellerstein, M. Stonebraker, Readings in Database Systems, Fifth Edition, 2015.

Introduction: Overview of Java programming language, Setting up Java Development Environment, Basic syntax and structure of Java programs; Data Types and Variables; Type conversion and casting; Control Flow Statements; Looping statements; Methods and Functions, Object-Oriented Programming (OOP) Concepts; Exception handling; Collections framework; File handling; Data Structures: Arrays and multidimensional arrays, Linked lists, stacks, and queues, Trees and graphs; GUI Programming; Database Connectivity; Multithreading; Networking; Data Science and AI Libraries.

• Herbert Schildt, Java: The Complete Reference, Eleventh Edition, McGraw-Hill Education, 2019.

• David Kopec, Classic Computer Science Problems in Java, First Edition, Manning, 2021.

Introduction: Optimization problems and existence of optimal solutions, convex sets and convex functions; Unconstrained optimization: Basic properties of solutions and algorithms, gradient method, Newton’s method, quasi-Newton method; Linear optimization: Simplex algorithm, duality; Constrained optimization: Equality and inequality constraints, projected gradient method; Convex optimization.

• Edwin K. P. Chong & Stanislaw H. Zak, An Introduction To Optimization, Fourth Edition, Wiley India, 2017.

• S. Boyd and L. Vandenberghe, Convex Optimization, First Edition, Cambridge India, 2004.

Brief review of random variables: expectation, variance, covariance, estimation and inference in the context of economics; Classical Linear Regression Model: least squares estimation, unbiasedness and efficiency, Gauss-Markov theorem, hypothesis testing, goodness of fit; Nonlinear models, Dummy variables; Heteroscadasticity, Autocorrelation and Multicollinearity: detection, implications and possible remedies; Omitted variable, measurement errors and instrumental variables; Binary response model, Sample selection problem.

• Jeffrey M. Wooldridge, Introductory Econometrics: A Modern Approach, Seventh Edition, South-Western College Publishing, 2020.

• Russell Davidson and James Mackinnon, Econometric Theory and Methods, Revised Edition, Oxford University Press, 2009.

Introduction: Definitions, Review of basics of data analysis, applications; Data Warehousing: Definition, architectures, dimensional modeling and star schema, ETL (extract, transform, load) processes; Finding Similar Items: Similarity measures and distance metrics, Shingling and locality-sensitive hashing; Frequent Pattern Mining: Itemset, substring, sequence, pattern evaluation and interestingness measures; Graph Mining: Graph data and representations, link analysis, pattern mining, graph clustering techniques; Mining Data Streams: characteristics of data streams, sliding window models, approximate and sketching techniques, change detection and concept drift.

• Han, Jiawei, Jian Pei, and Hanghang Tong, Data mining: Concepts and Techniques, 4th Edition, Morgan Kaufmann, 2022.
• Robinson, Ian, Jim Webber, and Emil Eifrem, Graph Databases: New Opportunities for Connected Data, 2nd Edition, O'Reilly Media, Inc., 2015.
• Ponniah, Paulraj, Data Warehousing Fundamentals for IT Professionals, 2nd Edition, John Wiley & Sons, 2012.

• Jure Leskovec, Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets, 3rd Edition, Cambridge University Press, 2020.
• Kimball, Ralph, and Margy Ross, The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, Wiley, 2013.
• Reis, Joe, and Matt Housley, Fundamentals of Data Engineering, O'Reilly Media, Inc., 2022.

Principles of point estimation; Properties of estimators: unbiasedness, consistency, sufficiency, mean squared errors; Methods of estimation: least squares estimation, method of moments estimators, maximum likelihood estimators (MLEs), statistical properties of MLEs, Fisher information, Cramer Rao Lower Bound; Confidence intervals, Bootstrap percentile method; Testing of hypothesis: Binary hypothesis testing, Type-I and type-II errors, power function, likelihood ratio tests, Neyman-Pearson lemma; Significance testing: general approach, generalized likelihood ratio tests; Bayesian vs Classical statistics, Bayesian inference and posterior distribution, Maximum a Posteriori probability rule, Bayesian least means squares estimation.

• Wasserman, L., All of Statistics: A concise course in statistical inference, First Edition, Springer, New York, 2004.
• Pishro-Nik, Hossein, Introduction to Probability, Statistics, and Random processes, First Edition, Kappa Research, LLC, 2014.

• Dimitri P. Bertsekas, John N. Tsitsiklis, Introduction To Probability, Second Edition, Athena Scientific, 2008.

Introduction to Signals: continuous-time, discrete-time signals, properties of signals and various signal operations; Signal representation: signal space and orthogonal bases, Fourier series representation of continuous-time signals, Fourier transform (FT) and its properties; Sampling theorem: aliasing, signal reconstruction, ideal interpolation; Discrete-time FT, Discrete Fourier Transform (DFT) and its properties; System classification and properties; Linear Time Invariant (LTI) systems; Time-domain representation of LTI systems: Impulse response, convolution and response to arbitrary input; Frequency response of LTI systems.

• B. P. Lathi, Principles of Linear Systems and Signals, Second Edition, Oxford, 2009.
• H. Hsu and R. Rajan, Signals and Systems, Second Edition, Schaum Series, 2017.

Fundamentals: Understanding social media contents, frameworks, characteristics, challenges; Mathematical foundations; Data Crawling: Techniques, policies, ethics, responsibilities; Different APIs of crawling data. Text content analysis: Tokenizer, lemmatization; Heaps law, Zipf law; retrieval models, relevance, ranking; Text Embedding; Feature selection, text classification; Network analytics: fundamentals, centrality, link prediction, community detection; network embedding; Applications: Real world applications of social media data.

• Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press, 2008.
• Subodha Kumar, Liangfei Qiu, Social Media Analytics and Practical Applications - The Change to the Competition Landscape, First Edition, CRC Press, 2021.

Understanding Structure of Data: Entities, attributes, relationships, data types, data models basics and types, dataset creation and best practices, data preparation for visualization; Introduction to Visual Perception; Guidelines for data visualization; Coordinate system and color scales; Visualizing Amounts; Visualizing Distributions; Visualizing proportions; Visualizing Associations Among Variables; Visualizing Time Series and trends; Visualizing geospatial data and Uncertainty: Projections; Principles of Figure Design; Advanced Topics: Image file formats, visualization softwares, and storytelling using data.

• Foxwell, Harry J., Creating Good Data: A Guide to Dataset Structure and Data Representation, First Edition, Apress, Berkeley, 2020.
• Claus O. Wilke, Fundamentals of Data Visualization, First Edition, Shroff/O'Reilly, 2019.
• Jonathan Schwabish, Better Data: A Guide for Scholars, Researchers, and Wonks, First Edition, Columbia University Press, 2021.

Introduction to time series data: Practical examples, Trend in time series data, Parametric trend, LS estimation, Differencing, Nonparametric methods, Trend and noise, Simple time series models, Analysis objectives; Stationary processes: basic properties, linear processes, ARMA(1,1) process, properties, forecasting stationary time series; ARMA(p,q) models: processes, ACF and PACF, spectral densities, periodogram, time-invariant linear filters, spectral density of an ARMA(p,q) process; Modeling and Forecasting with ARMA(p,q) processes: Yule-Walker estimation, Burg’s algorithm, Innovations algorithm, Maximum Likelihood Estimation, Diagnostic checking, Forecasting, Order selection criterion; Nonstationary and seasonal models; Applications and recent developments.

• Brockwell, Peter J., and Richard A. Davis, Introduction to Time Series and Forecasting, Third Edition, Springer New York, 2002.
• Hyndman, Rob J., and George Athanasopoulos, Forecasting: Principles and Practice, Third Edition, OTexts, 2021.

• Kay, Steven M., Fundamentals of Statistical Signal Processing: Estimation Theory, First Edition, Pearson, 1993.
• Nielsen, Aileen, Practical Time Series Analysis: Prediction with Statistics and Machine Learning, First Edition, O'Reilly Media, 2019.

Introduction: Supervised and unsupervised learning, Generative and discriminative models, Concept of dimensionality and feature vectors, Multi-dimensional Gaussian distribution, Mean vector and covariance matrix in Gaussian distribution; Supervised Learning: Bayesian classification principles, Computation of decision surfaces, Error calculation and performance measures, Risk minimization strategies, Zero-one loss function, Maximum Likelihood Estimation and Maximum A Posteriori Estimation, Bayesian learning concepts, Parzen windows and k-nearest neighbor algorithm, Distance measures and Dynamic Time Warping, Decision trees for classification tasks; Unsupervised Learning: K-means clustering, Hierarchical Agglomerative Clustering, Gaussian Mixture Models, Density-Based Spatial Clustering of Applications with Noise; Dimensionality Reduction Techniques: Curse of dimensionality, Applications of Principal Component Analysis and Fisher Discriminant Analysis to classification problems.

• E. Alpaydin, Introduction to Machine Learning, 3rd Edition, Prentice Hall (India), 2015.
• R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, Second Edition, John Wiley & Sons, 2006.

• C. M. Bishop, Pattern Recognition and Machine Learning, Second Edition, Springer, 2011.
• S. O. Haykin, Neural Networks and Learning Machines, Third Edition, Pearson Education, 2016.

Introduction to Recommender Systems; Traditional Recommendation Techniques: Nearest neighbor-based, associative rule-based, content-based filtering, collaborative filtering; Matrix Factorization Techniques: Introduction, SVD review, alternating least squares, non-negative matrix factorization; Advanced Recommendation Techniques: Context-aware, hybrid, model-based methods; Evaluation metrics and methodologies; Recommender System Challenges: Cold start problem, data sparsity, scalability, privacy, and explainability; Case Studies and Applications: E-commerce, social media, multimedia, and other domains; Ethical Considerations in recommendation systems.

• Charu C. Aggarwal, Recommender Systems: The Textbook, First Edition, Springer, 2016.
• Jannach D., Zanker M., and Fel Fering A., Recommender Systems: An Introduction, First Edition, Cambridge University Press, 2011.

• To be updated as required.