.png&w=1920&q=75)
B-SC in Statistics at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
.png&w=1920&q=75)
About the Specialization
What is Statistics at SRM Institute of Science and Technology Chengalpattu?
This B.Sc. Statistics program at SRM Institute of Science and Technology focuses on equipping students with robust analytical and quantitative skills crucial for the data-driven Indian industry. It covers foundational statistical theories, computational tools like R and Python, and advanced areas like machine learning and big data analytics, preparing graduates for diverse roles in a rapidly expanding market. The program emphasizes both theoretical rigor and practical application.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for Mathematics and analytical thinking, keen to pursue a career in data science, analytics, or research. It also suits individuals seeking a strong quantitative foundation for further studies in fields like actuarial science, econometrics, or biostatistics. Aspiring data scientists, statisticians, and researchers will find this curriculum highly beneficial.
Why Choose This Course?
Graduates of this program can expect to secure roles as Data Analysts, Business Intelligence Analysts, Research Statisticians, or Jr. Data Scientists across various Indian sectors. Entry-level salaries typically range from INR 3.5 to 6 LPA, with significant growth potential up to INR 10-15 LPA with experience. The strong foundation also prepares students for competitive exams, actuarial certifications, and postgraduate studies in India and abroad.
Student Success Practices
Foundation Stage
Master Core Statistical Concepts- (Semester 1-2)
Dedicate significant time to understanding the mathematical foundations of probability, descriptive statistics, and calculus. Regularly solve problems from textbooks and practice previous year''''s questions. This builds a strong base for advanced topics.
Tools & Resources
NPTEL courses on Probability and Statistics, Khan Academy for Calculus, Dedicated problem-solving sessions with faculty
Career Connection
A solid foundation is crucial for cracking technical interviews and understanding complex algorithms in later semesters, which are essential for data science roles.
Develop Programming Proficiency (R & Python Basics)- (Semester 1-2)
Actively engage with the R and Python lab sessions. Practice coding challenges on platforms like HackerRank or LeetCode specific to data structures and basic algorithms. Work on small data manipulation projects.
Tools & Resources
DataCamp, Coursera courses on R and Python for Data Science, GeeksforGeeks, Jupyter Notebooks
Career Connection
Proficiency in R and Python is non-negotiable for most data analyst and data science roles in India, as these are the primary tools used for statistical computing.
Engage in Peer Learning & Discussion Groups- (Semester 1-2)
Form study groups with peers to discuss challenging concepts, clarify doubts, and collaboratively work on assignments. Teaching others reinforces your own understanding and exposes you to different perspectives.
Tools & Resources
College library discussion rooms, Online collaborative platforms like Google Meet or Discord
Career Connection
Enhances communication skills, teamwork, and problem-solving abilities, which are highly valued in professional environments during team projects and interviews.
Intermediate Stage
Apply Statistical Models to Real-world Data- (Semester 3-5)
Go beyond theoretical understanding by applying statistical inference, regression, and experimental design techniques to public datasets. Participate in hackathons or create personal projects using data from Kaggle or government open data portals.
Tools & Resources
Kaggle, UCI Machine Learning Repository, Government of India Open Data Portal, R/Python libraries (Scikit-learn, StatsModels)
Career Connection
Demonstrates practical skills to potential employers, builds a portfolio, and deepens understanding of how statistical methods solve business problems.
Seek Internships and Industry Exposure- (Semester 3-5)
Actively search for internships during semester breaks at analytics firms, IT companies, or research institutions. Even short-term projects or virtual internships provide invaluable industry experience and networking opportunities.
Tools & Resources
Internshala, LinkedIn Jobs, College placement cell, Networking events
Career Connection
Internships are crucial for understanding corporate culture, gaining hands-on experience, and often lead to pre-placement offers, significantly boosting employability in the Indian job market.
Specialize in a Niche Area (Electives & Certifications)- (Semester 3-5)
Based on your interest (e.g., actuarial science, biostatistics, machine learning), choose relevant elective subjects. Supplement this with online certifications or specialized workshops to build expertise in that niche.
Tools & Resources
Online courses from platforms like Coursera (e.g., IBM Data Science Professional Certificate), Professional body certifications (e.g., actuarial exams)
Career Connection
Specialization makes you a more attractive candidate for specific roles, allows for deeper learning, and can lead to higher-paying jobs in targeted industries.
Advanced Stage
Undertake a Comprehensive Capstone Project- (Semester 6)
Work on a substantial project that integrates multiple statistical techniques and programming skills learned throughout the degree. Aim for a project that addresses a real-world problem, ideally in collaboration with industry.
Tools & Resources
Access to university labs, Faculty mentorship, Industry connections, Git/GitHub for version control
Career Connection
This project is a major talking point in interviews, showcasing your ability to execute a complete data analysis pipeline and deliver tangible results, vital for securing good placements.
Intensive Placement Preparation & Mock Interviews- (Semester 6)
Participate in campus placement drives, attend workshops on resume building, interview etiquette, and aptitude test preparation. Practice technical and HR mock interviews extensively with career advisors and peers.
Tools & Resources
College placement cell resources, Online aptitude test platforms (e.g., IndiaBix), Glassdoor for company-specific interview questions
Career Connection
Direct preparation for the job market. This stage is critical for converting your academic achievements into a successful career launch in top Indian companies.
Network with Alumni and Industry Professionals- (Semester 6)
Leverage SRMIST''''s alumni network and participate in industry webinars, conferences, or career fairs. Build connections on LinkedIn to gain insights, mentorship, and potential job leads.
Tools & Resources
LinkedIn, SRMIST Alumni Association, Industry events (online/offline)
Career Connection
Networking opens doors to hidden job opportunities, provides mentorship, and helps you stay updated with industry trends, significantly aiding long-term career growth.
Program Structure and Curriculum
Eligibility:
- A pass in H.Sc. (10+2) or its equivalent with Mathematics as one of the subjects.
Duration: 3 years (6 semesters)
Credits: 120 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UCC2101 | Value Education | Ability Enhancement Compulsory Course | 2 | Human Values, Social Values, Environmental Ethics, Universal Ethics, Professional Ethics |
| ULN2101 | English I | Core | 3 | Communication Skills, Grammar Fundamentals, Reading Comprehension, Basic Writing Skills, Listening Practice |
| UMT2101 | Algebra and Calculus | Core | 4 | Matrices and Determinants, Vector Algebra, Differential Calculus, Integral Calculus, Applications of Calculus |
| UST2101 | Descriptive Statistics | Core | 4 | Data Collection and Classification, Measures of Central Tendency, Measures of Dispersion, Skewness and Kurtosis, Correlation and Regression |
| UST2102 | Introduction to Probability | Core | 4 | Basic Probability Concepts, Conditional Probability, Bayes'''' Theorem, Random Variables, Elementary Probability Distributions |
| UST2103 | Descriptive Statistics and R Programming Lab | Core Practical | 2 | R Programming Basics, Data Import and Manipulation in R, Descriptive Statistics using R, Graphical Representation of Data, Correlation and Regression in R |
| UEF2101 | Environmental Science | Ability Enhancement Compulsory Course | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Global Environmental Issues, Sustainable Development |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| ULN2102 | English II | Core | 3 | Advanced Communication, Report Writing, Presentation Skills, Public Speaking, Literary Appreciation |
| UMT2102 | Differential Equations and Transforms | Core | 4 | First Order Differential Equations, Higher Order Differential Equations, Laplace Transforms, Fourier Transforms, Partial Differential Equations |
| UST2104 | Theory of Attributes and Sampling Distributions | Core | 4 | Association of Attributes, Chi-square Test for Attributes, Standard Error, Sampling Distributions (t, F, Chi-square), Central Limit Theorem |
| UST2105 | Probability Distributions | Core | 4 | Discrete Probability Distributions (Binomial, Poisson), Continuous Probability Distributions (Normal, Exponential), Moment Generating Functions, Characteristics of Distributions, Law of Large Numbers |
| UST2106 | Introduction to Python | Skill Enhancement Course | 2 | Python Fundamentals, Data Types and Operators, Control Flow Statements, Functions and Modules, Introduction to NumPy and Pandas |
| UST2107 | Probability Distributions and Python Lab | Core Practical | 2 | Python for Statistical Computations, Simulating Probability Distributions, Hypothesis Testing with Python, Data Visualization in Python, Applied Statistical Analysis |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UST2108 | Sampling Theory | Core | 4 | Simple Random Sampling, Stratified Random Sampling, Systematic Sampling, Cluster Sampling, Ratio and Regression Estimators |
| UST2109 | Statistical Inference | Core | 4 | Point Estimation, Properties of Estimators, Interval Estimation, Hypothesis Testing, Large and Small Sample Tests |
| UST2110 | Linear Models and Regression Analysis | Core | 4 | Simple Linear Regression, Multiple Linear Regression, Assumptions of Regression, Model Diagnostics, ANOVA for Regression |
| UST2111 | Statistical Inference and Sampling Lab | Core Practical | 2 | Implementation of Sampling Techniques, Estimation Procedures in R/Python, Hypothesis Testing using Statistical Software, Power and Sample Size Calculations, Survey Data Analysis |
| UST21S03 | R Programming for Data Science | Skill Enhancement Course (Choice Based) | 2 | R Environment and Basics, Data Structures in R, Data Manipulation with dplyr, Data Visualization with ggplot2, Statistical Modeling in R |
| UGC21E01 | Introduction to Social Sciences | General Elective (Choice Based) | 3 | Nature of Social Sciences, Social Institutions, Culture and Society, Economic Systems, Political Structures |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UST2112 | Design of Experiments | Core | 4 | Analysis of Variance (ANOVA), Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), Factorial Experiments |
| UST2113 | Time Series Analysis | Core | 4 | Components of Time Series, Trend and Seasonality Analysis, Smoothing Techniques, ARIMA Models, Forecasting Methods |
| UST2114 | Econometrics | Core | 4 | Classical Linear Regression Model, Assumptions and Their Violations, Multicollinearity, Heteroscedasticity, Autocorrelation |
| UST2115 | Design of Experiments and Time Series Lab | Core Practical | 2 | ANOVA using Statistical Software, Implementation of Experimental Designs, Time Series Model Fitting, Forecasting with R/Python, Analysis of Real-world Experiments |
| UST21S05 | Data Analytics with Python | Skill Enhancement Course (Choice Based) | 2 | Data Cleaning and Preprocessing, Exploratory Data Analysis with Python, Statistical Modeling Libraries, Introduction to Machine Learning, Data Storytelling |
| UGC21E02 | Basics of Psychology | General Elective (Choice Based) | 3 | Introduction to Psychology, Learning and Cognition, Memory and Emotion, Motivation and Personality, Social Psychology |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UST2116 | Multivariate Analysis | Core | 4 | Multivariate Normal Distribution, Principal Component Analysis, Factor Analysis, Discriminant Analysis, Cluster Analysis |
| UST2117 | Operations Research | Core | 4 | Linear Programming, Simplex Method, Transportation Problem, Assignment Problem, Game Theory |
| UST2118 | Quality Control | Core | 4 | Statistical Process Control, Control Charts (X-bar, R, p, np, c, u), Acceptance Sampling, Process Capability Analysis, Six Sigma Concepts |
| UST2119 | Multivariate Analysis and Operations Research Lab | Core Practical | 2 | Multivariate Data Analysis Software, Principal Component Analysis Implementation, Linear Programming Solvers, Transportation and Assignment Problems, Simulations in Operations Research |
| UST21E01 | Stochastic Processes | Department Elective (Choice Based) | 4 | Markov Chains, Continuous Time Markov Processes, Poisson Process, Birth and Death Process, Queuing Theory |
| UST21E02 | Survival Analysis | Department Elective (Choice Based) | 4 | Survival Function and Hazard Function, Kaplan-Meier Estimator, Log-Rank Test, Cox Proportional Hazards Model, Accelerated Failure Time Models |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UST2120 | Data Mining and Big Data Analytics | Core | 4 | Introduction to Data Mining, Classification Algorithms, Clustering Techniques, Association Rule Mining, Big Data Concepts and Technologies |
| UST2121 | Non-parametric Methods | Core | 4 | Sign Test, Wilcoxon Signed-Rank Test, Mann-Whitney U Test, Kruskal-Wallis Test, Spearman''''s Rank Correlation |
| UST2122 | Data Mining and Big Data Analytics Lab | Core Practical | 2 | Implementation of Data Mining Algorithms, Working with Big Data Tools (Hadoop/Spark), Predictive Modeling Projects, Text Mining Applications, Cloud-based Analytics Platforms |
| UST2123 | Project Work | Core Project | 6 | Research Methodology, Problem Identification and Formulation, Data Collection and Analysis, Report Writing and Documentation, Project Presentation and Defense |
| UST21E03 | Biostatistics | Department Elective (Choice Based) | 4 | Clinical Trials Design and Analysis, Epidemiological Methods, Statistical Genetics, Dose-Response Modeling, Public Health Applications |
