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M-SC in Statistics at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Statistics at SRM Institute of Science and Technology Chengalpattu?
This M.Sc. Statistics program at SRM Institute of Science and Technology focuses on equipping students with advanced theoretical knowledge and practical skills in statistical methods and their applications. It is meticulously designed to meet the growing demand for data professionals and statisticians in various Indian industries, including IT, finance, healthcare, and research. The program emphasizes computational statistics, leveraging popular tools like R and Python, making graduates highly adaptable to modern data challenges.
Who Should Apply?
This program is ideal for fresh graduates holding a B.Sc. in Statistics, Mathematics, or Computer Science with a strong mathematical background, as well as B.E./B.Tech. graduates seeking to specialize in data science and analytics. It also caters to working professionals who wish to upskill or transition into data-intensive roles, leveraging their prior quantitative aptitude to build a career in statistical modeling, machine learning, and data interpretation.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including Data Scientist, Statistician, Business Analyst, Market Research Analyst, and Biostatistician. Entry-level salaries typically range from INR 4-7 lakhs per annum, with experienced professionals earning significantly more. The program fosters critical thinking and problem-solving skills, aligning with industry demand for professionals who can derive insights from complex datasets and contribute to data-driven decision-making in Indian and global firms.
Student Success Practices
Foundation Stage
Build Strong Quantitative Foundations- (Semester 1)
Dedicate significant time to mastering core mathematical and statistical concepts from Linear Algebra and Probability. Regularly solve problems, understand derivations, and form study groups for discussions to solidify foundational knowledge.
Tools & Resources
NPTEL courses on Linear Algebra and Probability, Khan Academy, Faculty office hours, Standard textbooks
Career Connection
A solid theoretical base is critical for understanding advanced statistical models and algorithms, essential for future roles in data science, quantitative analysis, and research positions.
Master Statistical Computing Tools (R)- (Semester 1)
Go beyond lab assignments; consistently practice R programming. Work on small data manipulation and visualization projects using publicly available datasets. Participate in online R coding challenges to enhance practical coding skills.
Tools & Resources
Kaggle, DataCamp for R programming, Swirl (interactive R tutorials), R-bloggers for insights, GeeksforGeeks
Career Connection
Proficiency in R is a foundational skill for data analysts and statisticians, enabling efficient data processing, statistical modeling, and advanced data visualization in various industry settings.
Proactive Learning and Peer Collaboration- (Semester 1)
Actively engage in lectures, ask questions, and form peer learning groups to discuss course material and tackle assignments together. Utilize departmental resources like tutoring or faculty mentorship programs for deeper understanding.
Tools & Resources
SRMIST Departmental study rooms, Online collaboration tools (e.g., Google Meet for study groups), Academic support services
Career Connection
Fosters problem-solving skills, enhances understanding through diverse perspectives, and builds a professional network that can be beneficial for future career opportunities and collaborative projects.
Intermediate Stage
Deepen Statistical Inference and Modeling Skills- (Semester 2)
Focus on practical applications of statistical inference and linear models. Work through case studies, apply different estimation and hypothesis testing techniques, and interpret results in real-world contexts to build practical expertise.
Tools & Resources
Statistical software packages (e.g., SAS/SPSS if required, but primarily R/Python), Research papers on applied statistics, Industry-specific datasets (e.g., from UCI Machine Learning Repository)
Career Connection
These skills are directly applicable to roles requiring robust data analysis, A/B testing, and predictive modeling, crucial for data scientists and business analysts in various Indian industries.
Expand Programming Proficiency to Python- (Semester 2)
Transition and deepen your programming skills in Python for data science. Focus on libraries like NumPy, Pandas, Scikit-learn, and Matplotlib. Attempt mini-projects to build end-to-end data analysis pipelines and strengthen your portfolio.
Tools & Resources
Coursera/edX courses on Python for Data Science, Towards Data Science articles, Official library documentation (NumPy, Pandas), Jupyter Notebooks
Career Connection
Python is a versatile tool for machine learning, big data, and web development, significantly broadening career options beyond traditional statistics roles into AI/ML engineering and advanced analytics.
Cultivate Research and Communication Acumen- (Semester 2)
Actively participate in Research Methodology and IPR discussions. Start exploring topics for potential mini-projects or research interests. Practice articulating research ideas and findings clearly through presentations and written reports.
Tools & Resources
Academic databases (e.g., Google Scholar, institutional library resources), Academic writing workshops, Departmental research seminars and guest lectures
Career Connection
Essential for higher studies, R&D roles, and for effectively communicating data-driven insights to diverse audiences in any professional statistical role, a highly valued skill in Indian corporate settings.
Advanced Stage
Specialization through Electives and Advanced Topics- (Semesters 3-4)
Strategically choose electives that align with your career aspirations (e.g., Actuarial, Biostatistics, Data Mining). Dive deep into the chosen areas, explore advanced concepts beyond the curriculum, and read relevant industry reports to gain specialized knowledge.
Tools & Resources
MOOCs specific to chosen specialization, Industry journals and publications, Professional body certifications (e.g., actuarial exams, SAS/R certifications if relevant)
Career Connection
Builds specialized expertise, making you a more attractive candidate for niche roles like Actuarial Analyst or Biostatistician, and demonstrates a clear career trajectory to potential employers.
Execute Capstone Project and Portfolio Building- (Semesters 3-4)
Treat Project Work I and II as opportunities to apply all learned skills to a significant real-world problem. Focus on robust methodology, thorough analysis, clear presentation, and comprehensive documentation. Build a portfolio of projects on platforms like GitHub.
Tools & Resources
GitHub/GitLab for version control and portfolio display, Professional project management tools, Advanced statistical software for analysis
Career Connection
The project serves as a practical demonstration of your capabilities, a key discussion point in interviews, and a tangible asset for your professional portfolio to showcase to recruiters and land desirable roles.
Engage in Networking and Placement Preparation- (Semesters 3-4)
Attend industry seminars, workshops, and career fairs. Connect with alumni and professionals on LinkedIn. Start preparing for placements early by practicing aptitude tests, technical interviews (statistics and coding), and HR rounds. Seek guidance from the placement cell.
Tools & Resources
LinkedIn for professional networking, SRMIST placement cell resources, Mock interview platforms, Company-specific preparation guides
Career Connection
Direct path to securing internships and full-time placements in top companies. Networking can open doors to opportunities not advertised publicly and provide valuable career insights from industry veterans.
Program Structure and Curriculum
Eligibility:
- Candidates should possess a minimum of 60% in B.Sc (Statistics/Mathematics/Computer Science with Mathematics) or BE/B.Tech (Any discipline with Mathematics as one of the subjects).
Duration: 2 years / 4 semesters
Credits: 94 Credits
Assessment: Internal: 40% (Theory), 50% (Practical/Project), External: 60% (Theory), 50% (Practical/Project)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MST21101 | Linear Algebra | Core | 4 | Vector Spaces, Linear Transformations, Eigenvalues and Eigenvectors, Quadratic Forms, Diagonalization |
| MST21102 | Probability and Distribution Theory | Core | 4 | Axiomatic Definition of Probability, Random Variables and Distributions, Joint and Conditional Distributions, Central Limit Theorem, Order Statistics |
| MST21103 | Statistical Computing I (R) | Core | 4 | Introduction to R, Data Structures in R, Data Input/Output, Statistical Graphics, Programming with R |
| MST21104 | Sampling Theory | Core | 4 | Simple Random Sampling, Stratified Random Sampling, Ratio and Regression Estimation, Systematic Sampling, Cluster Sampling |
| MST21105 | Statistical Computing Lab I (R) | Practical | 4 | R data types and operations, Data frames and lists, Graphical representation in R, Descriptive statistics in R, Functions and loops in R |
| MST21106 | Professional Skills | Soft Skill | 2 | Communication Skills, Presentation Skills, Group Discussion Techniques, Interview Skills, Ethics and Professionalism |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MST21201 | Real Analysis | Core | 4 | Sequences and Series, Continuity and Uniform Continuity, Differentiation, Riemann Integral, Metric Spaces |
| MST21202 | Statistical Inference | Core | 4 | Point Estimation, Confidence Intervals, Hypothesis Testing, Likelihood Ratio Tests, Non-parametric Methods |
| MST21203 | Statistical Computing II (Python) | Core | 4 | Introduction to Python, Python Data Structures, NumPy and Pandas, Data Visualization (Matplotlib, Seaborn), Introduction to Scikit-learn |
| MST21204 | Linear Models and Regression Analysis | Core | 4 | Simple Linear Regression, Multiple Linear Regression, Model Diagnostics, ANOVA for Regression, Polynomial Regression |
| MST21205 | Statistical Computing Lab II (Python) | Practical | 4 | Python for data cleaning, Statistical modeling with Python, Machine learning algorithms, Web scraping basics, Database connectivity |
| MST21206 | Research Methodology and IPR | Soft Skill | 2 | Research Problem Formulation, Research Design, Data Collection Methods, Report Writing and Presentation, Intellectual Property Rights |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MST21301 | Multivariate Analysis | Core | 4 | Multivariate Normal Distribution, Principal Component Analysis, Factor Analysis, Cluster Analysis, Discriminant Analysis |
| MST21302 | Design and Analysis of Experiments | Core | 4 | Analysis of Variance (ANOVA), Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), Factorial Experiments |
| MST21303 | Stochastic Processes | Core | 4 | Markov Chains, Poisson Processes, Birth and Death Processes, Branching Processes, Renewal Theory |
| MST21304 | Time Series Analysis | Core | 4 | Components of Time Series, Stationary Processes, ARIMA Models, Forecasting Techniques, Spectral Analysis |
| MST21E01 | Actuarial Statistics (Elective I Option) | Elective | 4 | Survival Models, Life Contingencies, Life Tables, Insurance Models, Risk Theory |
| MST21E02 | Biostatistics (Elective I Option) | Elective | 4 | Clinical Trials, Survival Analysis, Epidemiological Studies, Bioassay, Health Statistics |
| MST21E03 | Financial Statistics (Elective I Option) | Elective | 4 | Financial Markets, Option Pricing Models, Risk Management, Portfolio Theory, Time Series in Finance |
| MST21E04 | Data Mining (Elective I Option) | Elective | 4 | Classification Techniques, Clustering Algorithms, Association Rule Mining, Decision Trees, Neural Networks |
| MST21305 | Project Work I | Project | 4 | Problem Identification, Literature Survey, Methodology Design, Data Collection and Preparation, Preliminary Analysis |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MST21401 | Non-Parametric Inference | Core | 4 | Order Statistics, Sign Test, Wilcoxon Rank Sum Test, Kruskal-Wallis Test, Kolmogorov-Smirnov Test |
| MST21402 | Statistical Quality Control | Core | 4 | Control Charts for Variables, Control Charts for Attributes, Acceptance Sampling, Process Capability Analysis, Six Sigma Methodology |
| MST21E05 | Econometrics (Elective II Option) | Elective | 4 | Classical Linear Regression Model, Multicollinearity, Heteroscedasticity, Autocorrelation, Simultaneous Equation Models |
| MST21E06 | Bayesian Inference (Elective II Option) | Elective | 4 | Prior and Posterior Distributions, Bayesian Estimation, Hypothesis Testing, Markov Chain Monte Carlo (MCMC), Gibbs Sampling |
| MST21E07 | Reliability Theory (Elective II Option) | Elective | 4 | Life Distributions, System Reliability, Maintainability and Availability, Accelerated Life Testing, Repairable Systems |
| MST21E08 | Official Statistics (Elective II Option) | Elective | 4 | Indian Statistical System, National Accounts Statistics, Population Census, Agricultural Statistics, Industrial Statistics |
| MST21E09 | Operations Research (Elective III Option) | Elective | 4 | Linear Programming, Simplex Method, Transportation Problem, Queuing Theory, Inventory Control Models |
| MST21E10 | Generalized Linear Models (Elective III Option) | Elective | 4 | Exponential Family Distributions, Link Functions, Logistic Regression, Poisson Regression, Quasi-likelihood |
| MST21E11 | Statistical Genetics (Elective III Option) | Elective | 4 | Population Genetics, Linkage Analysis, Quantitative Trait Loci (QTL), Genome-Wide Association Studies (GWAS), Bioinformatics |
| MST21E12 | Categorical Data Analysis (Elective III Option) | Elective | 4 | Contingency Tables, Odds Ratios, Log-linear Models, Logistic Regression for Categorical Data, Matched Pairs Analysis |
| MST21403 | Project Work II | Project | 10 | Advanced Data Analysis, Model Development and Validation, Interpretation of Results, Technical Report Writing, Thesis Defense |
