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Last date of admission: 22nd Sep
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Semester 1
Computational Mathematics
This subject explores core computational mathematics concepts, covering linear algebra, matrix methods, eigen analysis, graph algorithms, calculus fundamentals, and numerical techniques such as interpolation and integration.
Probability and Probability Distribution
This subject introduces the fundamentals of probability theory and probability distributions, and key statistical theorems such as the law of large numbers and central limit theorem.
Programming with R and Python
This subject provides hands-on training in R and Python programming, focusing on data manipulation, visualization, exploratory analysis, control structures, and essential libraries like tidyverse, ggplot2, NumPy, and Pandas for practical data-driven applications.
Statistical Inference
This subject explores the foundations of statistical inference, covering sampling distributions, estimation methods, confidence intervals, hypothesis testing, sample size determination, and non-parametric techniques for drawing reliable conclusions from data.
Database Management
This subject provides a comprehensive understanding of database management, covering data modeling, SQL querying, transaction processing, concurrency control, query optimization, and modern concepts like data warehousing and big data systems.
Semester 2
Linear Regression Models
This subject offers an in-depth exploration of linear regression models, focusing on estimation techniques, model interpretation, feature engineering, diagnostic testing, validation methods, and advanced approaches such as regularization, model selection, bootstrapping, and Bayesian regression.
Applied Multivariate Data Analysis
This subject explores multivariate data techniques, covering dimensionality reduction, factor and cluster analysis, classification methods, scaling approaches, and advanced tools like conjoint and canonical correlation analysis.
Machine Learning Methods
This subject explores core machine learning methods, covering data preparation, model training, feature engineering, supervised and unsupervised learning techniques, clustering approaches, neural network foundations, and key AI applications including computer vision.
Big Data Analytics
This subject offers a deep dive into big data technologies, including the Hadoop ecosystem (HDFS, MapReduce, YARN), NoSQL databases under the CAP theorem, Spark’s multi-language architecture, Kafka streaming, and essential management tools like Hive, Pig, and Zookeeper.
Data Visualization using Tableau and Power BI
This subject introduces data visualization using Tableau and Power BI. Learners develop skills to transform datasets into interactive dashboards and visual reports, enabling effective data analysis and communication of insights.
Stochastic Processes
This subject introduces models for analysing random and time-dependent phenomena. Learners explore probability-based methods to understand uncertainty, patterns, and behaviour in data and apply them to real-world problems.
Skill Lab - I
This subject provides hands-on experience in applying data science concepts and tools. Learners work on practical tasks to strengthen their technical, analytical, and problem-solving skills.
Semester 3
Design and Analysis of Experiments
This subject explores the principles of design of experiments, covering randomized and blocked designs, factorial and repeated measures approaches, and advanced layouts like Latin square, split-plot, and crossover experiments for effective data-driven decision-making.
Research Methodology
This subject provides a comprehensive foundation in research methodology, covering research design, problem formulation, data collection, sampling, measurement, questionnaire development, data analysis techniques, report writing, and ethical considerations in research.
Deep Learning Principles and Applications
This subject covers core deep learning concepts, including neural network building blocks, optimization, regularization, and CNN foundations for image classification.
Categorical Data Analysis and Generalized Linear Models
This subject provides a comprehensive understanding of categorical data analysis and generalized linear models, GLM components and link functions, logistic regression techniques, model diagnostics, variable selection methods, and extensions to multinomial, ordinal, Poisson, and log-linear regression models.
Time Series Analysis
This subject introduces methods for analysing data collected over time. Learners explore time series patterns, trends, and forecasting techniques to interpret historical data and support data-driven predictions.
Skill Lab - II
This subject provides hands-on experience in applying advanced data science concepts and tools. Learners work on practical tasks to strengthen their technical, analytical, and problem-solving skills.
Semester 4
Computer Vision
This subject introduces computer vision fundamentals, covering image processing, feature extraction, 3D vision, object detection and recognition, deep learning techniques, and real-world applications in healthcare, robotics, and ethical AI.
Natural Language Processing
This subject introduces Natural Language Processing fundamentals, including text processing, finite-state methods, n-gram language models, part-of-speech tagging, syntactic structures, and statistical parsing for understanding human language.
Bayesian Statistical Modelling
This subject introduces Bayesian statistical modelling, focusing on priors and posterior inference, Bayesian analysis of key distributions, MCMC and Gibbs sampling, hypothesis testing with Bayes factors, regression and survival models, and model evaluation using posterior predictive methods and BIC.
Generative AI
This subject introduces the principles and applications of generative artificial intelligence. Learners explore generative AI tools and techniques for creating content, analysing data, automating tasks, and solving real-world data science problems.
Capstone Project
By completing a capstone project, learners showcase their ability to independently plan, execute, and deliver a project that reflects industry standards and career readiness.
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