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B-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 B.Sc Statistics program at SRM Institute of Science and Technology focuses on building a strong foundation in statistical theories and their practical applications. It equips students with skills in data analysis, modeling, and interpretation, crucial for data-driven decision-making in various Indian industries. The program emphasizes both theoretical rigor and computational proficiency, preparing graduates for the evolving demands of the analytics sector.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for Mathematics and an interest in data. It targets those aspiring to enter fields like data science, market research, financial analytics, or actuarial science. Working professionals looking to transition into data roles or upskill their analytical capabilities will also find value in its comprehensive curriculum.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as Data Analysts, Statisticians, Business Analysts, or Market Research Analysts. Entry-level salaries typically range from 3-6 LPA, with experienced professionals earning 8-15 LPA or more, depending on skills and industry. The program aligns with certifications in data science and analytics, offering strong growth trajectories in Indian IT, finance, and healthcare sectors.
Student Success Practices
Foundation Stage
Strengthen Core Mathematical & Statistical Concepts- (Semester 1-2)
Dedicate time to thoroughly understand fundamental concepts of Calculus, Linear Algebra, Probability, and Descriptive Statistics. These form the bedrock for advanced topics. Form study groups with peers to discuss challenging problems and clarify doubts regularly.
Tools & Resources
Khan Academy, NPTEL lectures on Statistics/Mathematics, Reference textbooks
Career Connection
A strong conceptual base is essential for excelling in entrance exams for higher studies (e.g., ISI, IIT JAM) and performing well in the analytical rounds of placement interviews.
Develop Basic Computational Proficiency- (Semester 1-2)
Beyond theoretical knowledge, learn to apply statistical concepts using basic computer tools. Master spreadsheet software like MS Excel for data organization and preliminary analysis, which is a fundamental skill in any data-centric role.
Tools & Resources
Microsoft Excel tutorials, Google Sheets, Online courses on data manipulation
Career Connection
Proficiency in basic computational tools is a non-negotiable skill for entry-level data analyst positions and will be built upon with advanced tools later.
Engage in Early Problem-Solving Challenges- (Semester 1-2)
Actively participate in departmental quizzes, simple data interpretation competitions, or online puzzle-solving platforms that test logical and quantitative reasoning. This builds confidence and sharpens analytical thinking.
Tools & Resources
DataFlair quizzes, IndiaBIX aptitude section, Basic logical reasoning platforms
Career Connection
Early engagement in problem-solving hones the analytical mindset crucial for tackling complex real-world data problems in future internships and jobs.
Intermediate Stage
Master Statistical Software (R/Python)- (Semester 3-4)
Focus on gaining hands-on expertise in industry-standard statistical programming languages like R or Python. Work on small projects, recreate textbook examples, and explore various libraries for data manipulation, visualization, and modeling.
Tools & Resources
RStudio, Anaconda/Jupyter Notebooks, Coursera/edX courses on R/Python for Data Science, Kaggle tutorials
Career Connection
Fluency in R or Python is a core requirement for almost all data science, analytics, and statistical roles in the Indian job market, directly impacting employability.
Undertake Mini-Projects and Data Challenges- (Semester 3-5)
Apply learned concepts to real-world datasets through mini-projects. Participate in online data challenges or hackathons (e.g., on Kaggle, Analytics Vidhya). This provides practical experience and builds a portfolio.
Tools & Resources
Kaggle.com, Analytics Vidhya DataHack, GitHub for project showcasing
Career Connection
A strong project portfolio demonstrates practical skills to recruiters, making you a more attractive candidate for internships and entry-level positions in analytics firms.
Seek Internships and Industry Exposure- (Semester 4-5)
Proactively look for summer internships or part-time projects in analytics, finance, or research firms. Even short stints provide invaluable industry exposure, networking opportunities, and a chance to apply academic knowledge in a professional setting.
Tools & Resources
LinkedIn Jobs, Internshala, Company career pages, Department''''s placement cell
Career Connection
Internships are often a direct gateway to pre-placement offers (PPOs) and significantly enhance your resume, providing real-world context to your theoretical learning.
Advanced Stage
Focus on Capstone Project & Specialization- (Semester 5-6)
Invest significant effort in your final year project, choosing a topic that aligns with your career aspirations and allows for in-depth statistical analysis. Consider specializing in areas like Machine Learning, Biostatistics, or Financial Statistics through electives.
Tools & Resources
Research papers, Academic journals, Mentorship from faculty, Advanced statistical software
Career Connection
A well-executed project acts as a strong talking point in interviews and showcases your ability to independently tackle complex problems, often leading to specialized roles.
Intensive Placement & Interview Preparation- (Semester 5-6)
Begin rigorous preparation for placements covering aptitude tests, logical reasoning, verbal ability, and technical statistical concepts. Practice mock interviews, focusing on explaining projects, statistical methodologies, and behavioral aspects.
Tools & Resources
Online aptitude platforms (e.g., FacePrep, GeeksforGeeks), Mock interview sessions, Company-specific previous year papers
Career Connection
Dedicated placement preparation is crucial for securing desired job roles in top analytics companies, ensuring you can confidently showcase your skills and knowledge.
Build a Professional Network and Personal Brand- (Semester 6)
Connect with alumni, industry professionals, and faculty mentors. Attend webinars, conferences, and workshops to stay updated on industry trends. Create a professional LinkedIn profile highlighting your skills, projects, and achievements.
Tools & Resources
LinkedIn, Professional conferences (e.g., Data Science Summits), Alumni networking events
Career Connection
Networking opens doors to hidden job opportunities, mentorship, and insights into career progression, establishing you as a recognizable professional in the data community.
Program Structure and Curriculum
Eligibility:
- A pass in 10+2 / HSC / CBSE / Equivalent Examination with Mathematics / Business Mathematics / Statistics / Computer Science / Informatics Practices as one of the subjects.
Duration: 3 years / 6 semesters
Credits: 140 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21BSM101J | Calculus and Matrix Algebra | Core | 4 | Differential Calculus, Integral Calculus, Matrices and Determinants, Eigenvalues and Eigenvectors, Vector Calculus |
| 21BST101J | Descriptive Statistics | Core | 4 | Data Collection and Presentation, Measures of Central Tendency, Measures of Dispersion, Moments, Skewness, and Kurtosis, Correlation and Regression |
| 21BPS101J | Basic Computer Skills for Statistics | Core | 3 | Computer Fundamentals, Operating Systems, MS Office Suite, Internet Basics, Data Handling in Spreadsheets |
| 21BST102L | Statistics Practical I (Descriptive Statistics) | Lab | 2 | Data Visualization, Calculation of Central Tendency, Calculation of Dispersion, Correlation Coefficients, Regression Line Fitting |
| 21BHS101T | Communicative English | Ability Enhancement Compulsory Course | 2 | Grammar and Usage, Reading Comprehension, Writing Skills, Listening and Speaking, Presentation Skills |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21BSM201J | Probability and Distributions | Core | 4 | Probability Concepts, Random Variables, Discrete Probability Distributions, Continuous Probability Distributions, Mathematical Expectation |
| 21BST201J | Statistical Methods | Core | 4 | Sampling Techniques, Testing of Hypothesis, Chi-Square Test, Analysis of Variance (ANOVA), Non-parametric Tests |
| 21BST202L | Statistics Practical II (Probability and Statistical Methods) | Lab | 2 | Probability Calculations, Fitting of Distributions, Hypothesis Testing for Means, ANOVA calculations, Non-parametric test implementation |
| 21BHS102T | Environmental Sciences | Ability Enhancement Compulsory Course | 2 | Ecosystems, Biodiversity, Pollution and Control, Natural Resources, Environmental Ethics |
| 21BGTXXX | Generic Elective I (Choose from list) | Generic Elective | 3 | Interdisciplinary subject, Basic concepts, Applications, Skill development, General knowledge |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21BST301J | Sampling Theory and Official Statistics | Core | 4 | Sampling Concepts, Simple Random Sampling, Stratified Sampling, Systematic Sampling, Official Indian Statistics Systems |
| 21BST302J | Statistical Computing using R | Skill Enhancement Course | 3 | Introduction to R, Data Structures in R, Data Import and Export, Statistical Graphics in R, Basic Statistical Analysis in R |
| 21BST303L | Statistics Practical III (R Programming) | Lab | 2 | R Environment Setup, Data Manipulation in R, Descriptive Statistics using R, Inferential Statistics using R, Data Visualization using R |
| 21BST304D | Discipline Specific Elective I | Discipline Specific Elective | 4 | Advanced topics in specific area, Methodologies, Applications, Problem-solving, Research frontiers |
| 21BHS301T | Indian Heritage and Culture | Ability Enhancement Compulsory Course | 2 | Ancient Indian History, Art and Architecture, Philosophy and Literature, Social Systems, Cultural Diversity |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21BST401J | Design of Experiments | Core | 4 | Principles of Experimentation, Completely Randomized Design, Randomized Block Design, Latin Square Design, Factorial Experiments |
| 21BST402J | Statistical Inference I | Core | 4 | Estimation Theory, Properties of Estimators, Methods of Estimation, Interval Estimation, Tests of Hypotheses |
| 21BST403L | Statistics Practical IV (Design of Experiments & Inference) | Lab | 2 | ANOVA for various designs, Estimation of parameters, Hypothesis testing problems, Confidence interval construction, Interpreting experimental results |
| 21BST404D | Discipline Specific Elective II | Discipline Specific Elective | 4 | Specialized statistical modeling, Advanced data analysis techniques, Applied case studies, Software implementation, Research methodology |
| 21BGTXXX | Generic Elective II (Choose from list) | Generic Elective | 3 | Interdisciplinary subject, Basic concepts, Applications, Skill development, General knowledge |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21BST501J | Econometrics | Core | 4 | Classical Linear Regression Model, Regression Assumptions and Violations, Time Series Econometrics, Panel Data Models, Forecasting in Econometrics |
| 21BST502J | Statistical Inference II | Core | 4 | Likelihood Ratio Tests, Sequential Probability Ratio Test, Decision Theory, Bayesian Inference, Non-parametric Inference |
| 21BST503L | Statistics Practical V (Econometrics & Inference) | Lab | 2 | Regression analysis using software, Hypothesis testing for econometric models, Non-parametric test implementation, Time series analysis basics, Interpreting statistical software output |
| 21BST504D | Discipline Specific Elective III | Discipline Specific Elective | 4 | Advanced statistical learning, Big data analytics, Data visualization tools, Machine learning algorithms, Predictive modeling |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21BST601J | Operations Research | Core | 4 | Linear Programming, Transportation Problem, Assignment Problem, Game Theory, Queuing Theory |
| 21BST602D | Discipline Specific Elective IV | Discipline Specific Elective | 4 | Specialized applied statistics, Industrial applications, Quality control techniques, Reliability theory, Time series forecasting models |
| 21BST603P | Project Work / Internship | Project | 6 | Problem Identification, Literature Review, Methodology Design, Data Analysis and Interpretation, Report Writing and Presentation |
