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INTEGRATED-M-SC in Statistics And Informatics at Indian Institute of Technology Kharagpur
Paschim Medinipur, West Bengal
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About the Specialization
What is Statistics and Informatics at Indian Institute of Technology Kharagpur Paschim Medinipur?
This Integrated M.Sc. in Statistics and Informatics program at Indian Institute of Technology Kharagpur focuses on developing expertise in statistical theory, computational methods, and data analysis. It is designed to meet the growing demand in India for professionals who can leverage data for informed decision-making across various sectors like finance, healthcare, and technology. The program uniquely blends core statistical principles with modern informatics tools, preparing students for cutting-edge challenges.
Who Should Apply?
This program is ideal for bright young graduates with a strong aptitude for mathematics and quantitative reasoning, seeking entry into data science, analytics, or research roles. It also suits individuals interested in applying statistical rigor to complex real-world problems. Fresh graduates from a science background, especially those with a keen interest in programming and data, would find this comprehensive program particularly rewarding.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths in India as Data Scientists, Business Analysts, Quantitative Researchers, Statisticians, or Machine Learning Engineers. Entry-level salaries typically range from INR 8-15 LPA, with experienced professionals earning significantly more. The strong foundation also prepares students for higher studies (PhD) or positions in leading analytics firms, financial institutions, and tech companies within India.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consistently practice programming (C/C++, Python) on platforms like CodeChef and HackerRank to build a strong logical and computational base. Focus on data structures and algorithms early.
Tools & Resources
CodeChef, HackerRank, GeeksforGeeks, NPTEL courses on Programming
Career Connection
Essential for any data-related role, improving problem-solving skills critical for technical interviews and coding rounds in placements.
Excel in Core Mathematics- (Semester 1-2)
Develop a deep understanding of Calculus, Linear Algebra, and Probability concepts. Form study groups, solve challenging problems from standard textbooks, and seek clarification from faculty.
Tools & Resources
Standard textbooks (e.g., NCERT, foreign authors), Khan Academy, NPTEL Mathematics courses
Career Connection
Forms the theoretical backbone for advanced statistics, machine learning, and quantitative finance, crucial for analytical roles.
Engage in Interdisciplinary Exploration- (Semester 1-2)
Participate in introductory workshops or online courses in areas like basic electronics, engineering drawing, or biology to broaden perspectives and understand real-world applications of science.
Tools & Resources
Coursera/edX introductory courses, Campus clubs, Departmental workshops
Career Connection
Develops holistic thinking and appreciation for diverse problem domains, useful for interdisciplinary data projects in industry.
Intermediate Stage
Build Statistical Software Proficiency- (Semester 3-5)
Become highly proficient in statistical software like R and Python for data manipulation, analysis, and visualization. Work on small data projects using real datasets.
Tools & Resources
RStudio, Jupyter Notebooks, Kaggle datasets, DataCamp, Swirl (for R tutorials)
Career Connection
Direct skill for Data Scientist, Statistician, and Business Analyst roles, enhancing practical application during internships and job interviews.
Seek Early Research/Project Opportunities- (Semester 4-5)
Approach professors for short-term research projects, summer internships, or term projects related to statistics and informatics. Apply theoretical knowledge to practical problems.
Tools & Resources
Departmental notices, Faculty research profiles, LinkedIn for internship searches
Career Connection
Builds a project portfolio, develops research aptitude, and provides practical experience highly valued by recruiters for placements.
Participate in Data Science Competitions- (Semester 4-5)
Join Kaggle or other data science competitions to test skills, learn from peers, and gain experience in real-world problem-solving under time constraints.
Tools & Resources
Kaggle, Analytics Vidhya, GitHub for team collaboration
Career Connection
Enhances problem-solving skills, exposes to diverse datasets, and provides tangible achievements to showcase on resumes during placements.
Advanced Stage
Specialize through Electives and Advanced Projects- (Semester 6-8)
Strategically choose departmental and open electives to specialize in areas like Machine Learning, Financial Statistics, or Biostatistics. Focus final year projects on industry-relevant problems.
Tools & Resources
Departmental elective guides, Research papers, Industry reports
Career Connection
Develops a niche expertise, making students highly desirable for specialized roles and enhancing their prospects for higher-paying positions.
Intensive Placement Preparation- (Semester 7-8)
Dedicate time to rigorous interview preparation, including mock interviews, aptitude tests, technical discussions, and soft skills training. Network with alumni and industry professionals.
Tools & Resources
Placement cell resources, Glassdoor, LinkedIn, Alumni mentors
Career Connection
Maximizes chances of securing top placements in core statistics and informatics roles, ensuring a smooth transition into the professional world.
Develop Communication & Presentation Skills- (Semester 7-8)
Actively participate in seminars, workshops, and group presentations. Practice explaining complex statistical concepts clearly and concisely to diverse audiences.
Tools & Resources
Toastmasters International (if available), University presentation workshops, Peer feedback
Career Connection
Crucial for success in client-facing roles, team collaborations, and leadership positions where effective communication is paramount.
Program Structure and Curriculum
Eligibility:
- Admission through JEE (Advanced) followed by JoSAA counselling.
Duration: 5 years / 10 semesters
Credits: 235 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA10001 | Mathematics I | Core | 4 | Functions of several variables, Limits and continuity, Partial derivatives, Implicit function theorem, Riemann Integral, Infinite Series |
| PH10001 | Physics I | Core | 4 | Classical Mechanics, Special Relativity, Oscillations, Waves, Geometric Optics, Wave Optics |
| CY10001 | Chemistry I | Core | 4 | Quantum Chemistry, Chemical Bonding, Spectroscopy, Organic Chemistry, Stereochemistry, Thermodynamics |
| EE10001 | Electrical Technology | Core | 4 | DC and AC Circuits, Network theorems, Transformers, Electrical Machines, Basic Electronics, Power Systems |
| CE10001 | Engineering Drawing and Computer Graphics | Core | 3 | Engineering curves, Orthographic projections, Isometric views, Computer-aided drafting, 3D modeling basics, Sectional views |
| PH19001 | Physics I Lab | Lab | 2 | Measurement techniques, Error analysis, Experiments in mechanics, Optics experiments, Electricity experiments, Data analysis |
| CY19001 | Chemistry I Lab | Lab | 2 | Quantitative analysis, Titrations, pH measurements, Organic synthesis, Spectroscopic identification, Chemical Kinetics |
| EE19001 | Electrical Technology Lab | Lab | 2 | Verification of circuit laws, AC/DC circuit experiments, Transformer characteristics, Diode characteristics, Transistor characteristics, Circuit Simulation |
| CS19001 | Computing Laboratory | Lab | 2 | Problem solving with C/C++, Data types, Control structures, Functions, Arrays, Pointers, File I/O |
| HS13001 | English for Science & Technology | Core | 2 | Technical writing, Oral communication, Reading comprehension, Vocabulary building, Report writing, Presentation skills |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA10002 | Mathematics II | Core | 4 | Vector Calculus, Line integrals, Surface integrals, Green''''s, Stoke''''s, Gauss theorems, Laplace Transforms, Fourier Series |
| PH10002 | Physics II | Core | 4 | Electromagnetism, Maxwell''''s equations, Electromagnetic waves, Interference, Diffraction, Polarization |
| CS10001 | Programming and Data Structures | Core | 4 | C/C++ programming, Arrays, Pointers, Linked Lists, Stacks, Queues, Trees, Graphs, Sorting and Searching, Recursion |
| EC10001 | Basic Electronics | Core | 4 | Semiconductor devices, Diodes, BJTs, FETs, Rectifiers, Filters, Amplifiers, Operational Amplifiers, Digital Logic Gates |
| ME10001 | Engineering Thermodynamics | Core | 4 | Laws of Thermodynamics, Properties of pure substances, Entropy, Enthalpy, Power cycles, Refrigeration cycles, Heat transfer basics |
| BT19001 | Biology Laboratory | Lab | 2 | Microscopy, Cell structure, Biomolecules, Enzyme activity, Genetic engineering basics, Ecology experiments |
| HS13002 | Professional Communication | HSS Elective | 2 | Technical reports, Presentations, Group discussions, Interviews, Business correspondence, Interpersonal communication |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA20003 | Probability and Stochastic Processes | Core | 4 | Probability spaces, Random variables, Distributions, Expectation, Conditional probability, Stochastic processes, Markov chains |
| MA20005 | Calculus of Several Variables | Core | 4 | Multivariable calculus, Vector calculus, Optimization, Constrained optimization, Implicit function theorem, Line and surface integrals |
| CS20001 | Discrete Structures | Core | 4 | Logic and proof techniques, Set theory, Relations and functions, Counting and combinatorics, Graph theory, Algebraic structures |
| MA29001 | Probability and Statistics Lab | Lab | 2 | Data analysis in R/Python, Simulation of random processes, Hypothesis testing implementation, Regression analysis, Statistical software usage, Probability distributions |
| HSS Elective I | Humanities and Social Sciences Elective I | Elective | 3 | Topics from Economics, Sociology, Psychology, Philosophy, Literature, History |
| Open Elective I | Open Elective I | Elective | 3 | Interdisciplinary topics, Introduction to engineering fields, Science applications, Technology trends, Environmental studies, Management principles |
| DE20001 | Environmental Science | Core | 2 | Ecosystems and biodiversity, Environmental pollution, Climate change, Sustainable development, Environmental policy, Natural resources |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA20004 | Statistical Methods | Core | 4 | Estimation theory, Hypothesis testing, Confidence intervals, Non-parametric methods, Bayesian inference basics, Goodness-of-fit tests |
| MA20006 | Linear Algebra | Core | 4 | Vector spaces, Linear transformations, Eigenvalues, Eigenvectors, Inner product spaces, Quadratic forms, Matrix decompositions |
| CS20002 | Algorithms | Core | 4 | Algorithm analysis, Sorting and Searching, Dynamic programming, Greedy algorithms, Graph algorithms, NP-completeness |
| MA29002 | Applied Statistics Lab | Lab | 2 | Regression analysis implementation, ANOVA and ANCOVA, Time series analysis basics, Categorical data analysis, Statistical modeling with software, Data visualization |
| HSS Elective II | Humanities and Social Sciences Elective II | Elective | 3 | Topics from Economics, Sociology, Psychology, Philosophy, Literature, Political Science |
| Open Elective II | Open Elective II | Elective | 3 | Interdisciplinary topics, Advanced engineering concepts, Computer applications, Business analytics basics, Material science, Energy systems |
| MA20008 | Data Structures | Core | 4 | Arrays and matrices, Linked lists and variations, Stacks and queues applications, Trees (BST, AVL, Red-Black), Graphs (traversals, shortest path), Hashing techniques |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA30001 | Statistical Inference | Core | 4 | Point estimation, Interval estimation, Hypothesis testing principles, Likelihood theory, Sufficiency and completeness, UMVUE and Cramer-Rao bound |
| MA30003 | Linear Models | Core | 4 | Simple linear regression, Multiple regression, ANOVA and ANCOVA, Model diagnostics, Variable selection, Generalized linear models basics |
| MA30005 | Numerical Methods | Core | 4 | Error analysis, Roots of equations, Interpolation techniques, Numerical integration, Ordinary differential equations, Linear systems |
| MA39001 | Statistical Computing Lab | Lab | 2 | R programming for statistics, Data manipulation with R, Statistical graphics, Simulation and Monte Carlo methods, Bootstrapping and Jackknife, Reproducible research |
| MA40007 | Optimization in Statistics | Department Elective | 3 | Linear programming, Non-linear programming, Convex optimization, Metaheuristics, Optimization algorithms in statistical modeling, Integer programming |
| HSS Elective III | Humanities and Social Sciences Elective III | Elective | 3 | Topics from Economics, Sociology, Psychology, Philosophy, Ethics, Arts and Culture |
| Open Elective III | Open Elective III | Elective | 3 | Interdisciplinary topics, Advanced programming, Digital signal processing, Robotics basics, Supply chain management, Cybersecurity fundamentals |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA30002 | Multivariate Analysis | Core | 4 | Multivariate normal distribution, Principal Component Analysis, Factor analysis, Discriminant analysis, Cluster analysis, Canonical Correlation |
| MA30004 | Design of Experiments | Core | 4 | Principles of experimentation, Completely Randomized Design, Block Designs (RBD, Latin Square), Factorial Experiments, Response Surface Methodology, Analysis of covariance |
| MA30006 | Optimization Techniques | Core | 4 | Linear programming, Simplex method, Duality theory, Transportation problems, Network models, Non-linear programming basics |
| MA39002 | Data Analysis Lab | Lab | 2 | Multivariate data analysis in R/Python, Design of experiments implementation, Optimization using software tools, Machine learning basics with scikit-learn, Hypothesis testing applications, Statistical report writing |
| MA40008 | Data Science with R | Department Elective | 3 | R programming for data science, Data wrangling and cleaning, Data visualization with ggplot2, Machine learning in R, Statistical modeling with R, Reproducible data analysis |
| HSS Elective IV | Humanities and Social Sciences Elective IV | Elective | 3 | Topics from Economics, Sociology, Psychology, Philosophy, Public Administration, Law |
| Open Elective IV | Open Elective IV | Elective | 3 | Interdisciplinary topics, Financial modeling, Bioinformatics, Advanced database systems, Geographical Information Systems, Cloud computing basics |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA40001 | Time Series Analysis | Core | 4 | ARIMA models, ARCH/GARCH models, Spectral analysis, Forecasting techniques, State-space models, Unit root tests |
| MA40003 | Machine Learning | Core | 4 | Supervised learning, Unsupervised learning, Regression and Classification algorithms, Deep learning basics, Model evaluation and selection, Bias-variance tradeoff |
| MA40005 | Statistical Quality Control | Core | 4 | Control charts (Shewhart, CUSUM, EWMA), Process capability analysis, Acceptance sampling, Six Sigma methodology, Quality management systems, Total Quality Management |
| MA49001 | Machine Learning Lab | Lab | 2 | Python for ML with Scikit-learn, Implementing ML algorithms, Model tuning and optimization, Deep learning frameworks (TensorFlow/PyTorch), Data preprocessing for ML, Feature engineering |
| MA40009 | Financial Time Series | Department Elective | 3 | Financial markets overview, Asset pricing models, Volatility modeling (GARCH), High-frequency data analysis, Risk management in finance, Market microstructure |
| HSS Elective V | Humanities and Social Sciences Elective V | Elective | 3 | Topics from Economics, Sociology, Psychology, Philosophy, Environmental Ethics, Media Studies |
| Open Elective V | Open Elective V | Elective | 3 | Interdisciplinary topics, Distributed systems, Natural Language Processing, Image processing, Operations Research, Entrepreneurship |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA40002 | Big Data Analytics | Core | 4 | Hadoop ecosystem, Spark framework, NoSQL databases, Data warehousing, Stream processing, Distributed computing principles |
| MA40004 | Data Mining | Core | 4 | Association rule mining, Classification techniques, Clustering algorithms, Predictive modeling, Web mining and Text mining, Data preprocessing for data mining |
| MA40006 | Bayesian Statistics | Core | 4 | Bayesian inference, Prior and posterior distributions, Markov Chain Monte Carlo (MCMC), Gibbs sampling, Hierarchical models, Bayesian model comparison |
| MA49002 | Big Data Lab | Lab | 2 | Hadoop MapReduce programming, Spark programming with PySpark, NoSQL databases (e.g., MongoDB, Cassandra), Cloud platforms for big data (e.g., AWS EMR), Data ingestion and processing, Big data visualization |
| MA40010 | Nonparametric Statistics | Department Elective | 3 | Rank tests (Wilcoxon, Kruskal-Wallis), Sign tests, Kernel density estimation, Nonparametric regression, Bootstrap and permutation tests, Resampling methods |
| MA40011 | Statistical Genetics | Department Elective | 3 | Population genetics, Linkage analysis, Quantitative trait loci (QTL), Genome-wide association studies (GWAS), Bioinformatics tools, Genetic epidemiology |
| Open Elective VI | Open Elective VI | Elective | 3 | Interdisciplinary topics, Internet of Things (IoT), Quantum computing basics, Renewable energy technologies, Medical imaging, Robotics and automation |
Semester 9
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA50001 | Advanced Stochastic Processes | Core | 4 | Martingales theory, Brownian motion, Ito calculus, Stochastic differential equations, Financial applications of SDEs, Poisson processes |
| MA50003 | Biostatistics | Core | 4 | Clinical trials design and analysis, Survival analysis, Longitudinal data analysis, Epidemiology methods, Genetic statistics, Bioassay |
| MA50005 | Functional Data Analysis | Core | 4 | Functional principal component analysis, Regression with functional data, Smoothing techniques, Functional time series, Applications in neuroscience, Curve and shape analysis |
| MA59001 | Project I | Project | 4 | Research methodology, Problem definition and scope, Literature review, Data collection and preparation, Preliminary analysis and modeling, Project report writing |
| MA40012 | Survival Analysis | Department Elective | 3 | Censoring and truncation, Kaplan-Meier estimator, Nelson-Aalen estimator, Cox proportional hazards regression, Accelerated failure time models, Frailty models |
| MA40013 | Spatial Statistics | Department Elective | 3 | Geostatistics, Spatial correlation, Kriging and interpolation, Point pattern analysis, Lattice data models, Environmental statistics applications |
Semester 10
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA50002 | Actuarial Statistics | Core | 4 | Life contingencies, Survival models in actuarial science, Premium calculation principles, Reserving methods, Ruin theory, Risk management for insurance |
| MA50004 | Deep Learning | Core | 4 | Neural networks architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs and Transformers, Generative models (GANs), Reinforcement learning basics |
| MA59002 | Project II | Project | 8 | Advanced research and development, Model development and validation, Implementation and experimentation, Thesis writing and documentation, Presentation and defense, Real-world problem solving |
| MA40014 | Stochastic Finance | Department Elective | 3 | Brownian motion models, Ito''''s Lemma, Black-Scholes model for option pricing, Hedging strategies, Risk-neutral valuation, Exotic options |
| Open Elective VII | Open Elective VII | Elective | 3 | Interdisciplinary topics, Management principles, Advanced materials, Communication systems, Data privacy and security, Digital marketing |
