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B-SC in Statistics at SRM Institute of Science and Technology (Deemed to be University)
Chengalpattu, Tamil Nadu
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About the Specialization
What is Statistics at SRM Institute of Science and Technology (Deemed to be University) Chengalpattu?
This B.Sc Statistics program at Sri Ramaswamy Memorial Institute of Science and Technology focuses on equipping students with a robust foundation in statistical theory, data analysis, and computational tools. It emphasizes quantitative reasoning and problem-solving skills crucial for understanding complex data, preparing graduates for various data-driven roles across India''''s rapidly expanding digital economy. The program integrates theoretical knowledge with practical applications.
Who Should Apply?
This program is ideal for 10+2 graduates with a strong aptitude for mathematics, logical reasoning, and a keen interest in data. It suits aspiring data analysts, statisticians, market researchers, and business intelligence professionals. Individuals seeking a solid academic base before pursuing higher studies in data science or actuarial science will also find this program beneficial. It serves as an excellent stepping stone into the analytical world.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as Data Analysts, Junior Statisticians, Business Intelligence Executives, or Market Research Analysts. Entry-level salaries typically range from INR 3 LPA to 6 LPA, with experienced professionals earning INR 8 LPA to 15+ LPA. The curriculum aligns with requirements for professional certifications in data analytics and actuarial exams, fostering strong growth trajectories in Indian companies.
Student Success Practices
Foundation Stage
Master Mathematical and Statistical Fundamentals- (Semester 1-2)
Dedicate time to thoroughly understand core concepts in probability, calculus, and linear algebra. Utilize online platforms like NPTEL and Khan Academy for supplementary learning and practice problems regularly to build a strong theoretical base. This ensures a solid foundation for advanced statistical concepts.
Tools & Resources
NPTEL, Khan Academy, Textbook exercises, Peer study groups
Career Connection
Strong fundamentals are essential for excelling in advanced statistical modeling and machine learning, directly impacting analytical job roles and higher education prospects.
Develop Basic Statistical Computing Proficiency (R & Python)- (Semester 1-2)
Actively engage with the R and Python practical labs, going beyond assigned tasks to explore additional datasets and functions. Complete online introductory courses on platforms like DataCamp or Coursera to solidify coding skills, focusing on data manipulation, descriptive statistics, and basic visualization. This provides practical data handling skills.
Tools & Resources
RStudio, Anaconda Python, DataCamp, Coursera introductory courses, GeeksforGeeks for practice
Career Connection
Proficiency in R and Python is non-negotiable for data analyst and statistician roles, directly enhancing your resume and interview performance for entry-level positions.
Participate in Departmental Workshops and Quizzes- (Semester 1-2)
Actively participate in workshops organized by the Department of Mathematics and Statistics, focusing on problem-solving, logical reasoning, and initial exposure to statistical software applications. Engage in departmental quizzes and competitions to apply learned concepts and foster peer learning. This builds academic confidence.
Tools & Resources
Departmental announcements, Peer collaboration, Quiz platforms
Career Connection
Early engagement improves conceptual clarity and competitive aptitude, crucial for university exams and initial assessment rounds in recruitment drives.
Intermediate Stage
Undertake Applied Statistics Mini-Projects- (Semester 3-5)
Beyond coursework, identify real-world datasets (e.g., from Kaggle, government data portals) and apply learned statistical methods and software (SPSS/SAS/MINITAB) to perform analysis and draw conclusions. Document findings thoroughly and seek feedback from faculty. This builds a portfolio of practical work.
Tools & Resources
Kaggle, Indian Government Data Portals, SPSS, SAS, MINITAB
Career Connection
Showcasing practical projects demonstrates problem-solving abilities and hands-on experience, significantly boosting internship and job application success.
Engage with Industry Guest Lectures and Webinars- (Semester 3-5)
Attend guest lectures, webinars, and seminars organized by the institution or external bodies featuring data professionals from Indian industries. Network with speakers and leverage these interactions to understand current industry trends, required skill sets, and potential career paths in the Indian context. This provides valuable industry insights.
Tools & Resources
LinkedIn, SRMIST Career Development Centre, Industry association events
Career Connection
Direct exposure to industry experts helps tailor skill development to market demands and opens doors for networking and mentorship opportunities.
Participate in Data Science Competitions- (Semester 3-5)
Form teams and participate in online data science competitions or hackathons on platforms like Kaggle or Analytics Vidhya. This provides exposure to diverse problem statements, encourages learning advanced techniques, and develops teamwork and time-bound problem-solving skills under pressure. This fosters a competitive spirit.
Tools & Resources
Kaggle, Analytics Vidhya, GitHub for collaboration
Career Connection
Success in such competitions highlights analytical prowess and practical application skills, making candidates stand out to potential employers in India''''s competitive tech sector.
Advanced Stage
Secure and Excel in an Industry Internship- (Semester 6)
Actively seek and complete a summer or semester-long internship in a relevant industry (e.g., IT, finance, healthcare, market research). Focus on applying statistical knowledge to real business problems, learning industry best practices, and developing professional communication skills. A strong internship is key for placements.
Tools & Resources
SRMIST Placement Cell, Internshala, LinkedIn, Company career pages
Career Connection
An internship provides crucial industry experience, often leading to pre-placement offers or significantly enhancing your profile for full-time job applications in India.
Master Advanced Data Tools and Concepts for Placements- (Semester 6)
Deepen your understanding of advanced machine learning algorithms, big data concepts, and database management (SQL). Pursue certifications from platforms like Google Data Analytics or IBM Data Science. Practice coding challenges and case studies relevant to data science roles to ace technical interviews. This ensures readiness for industry.
Tools & Resources
Online certification courses (Coursera, edX), SQL practice platforms (HackerRank), LeetCode for coding
Career Connection
Advanced skills and certifications directly align with job descriptions for Data Scientist and Advanced Analyst roles, improving your chances of securing high-paying positions.
Focus on Career Planning and Networking- (Semester 6)
Utilize the university''''s career services for resume building, mock interviews, and placement preparation workshops. Build a strong professional network on LinkedIn, connecting with alumni and industry professionals. Research companies and roles thoroughly, tailoring applications to specific job requirements. This is crucial for successful career launch.
Tools & Resources
SRMIST Placement Cell, LinkedIn, Company websites for career pages, Job portals (Naukri, Indeed)
Career Connection
Effective career planning and networking are vital for identifying suitable job opportunities, excelling in interviews, and smoothly transitioning into the professional workforce.
Program Structure and Curriculum
Eligibility:
- A pass in H.Sc (10+2) or its equivalent with Mathematics/Business Mathematics/Statistics/Computer Science as one of the subjects and with a minimum of 50% marks.
Duration: 3 years (6 semesters)
Credits: 140 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18LEM101T | Communicative English (A) | Core | 3 | Functional Grammar, Listening & Speaking, Reading Comprehension, Writing Skills, Vocabulary Building |
| 18MA101T | Differential Equations and Transforms | Core | 4 | First Order Differential Equations, Higher Order Linear ODEs, Laplace Transforms, Inverse Laplace Transforms, Fourier Series |
| 18ST101T | Probability and Random Variables | Core | 4 | Axiomatic Approach to Probability, Conditional Probability, Random Variables, Probability Distributions, Mathematical Expectation |
| 18ST102P | Statistical Computing using R (Practical) | Core | 2 | Introduction to R, Data Input and Output, Data Structures in R, Descriptive Statistics using R, Graphical Representation in R |
| 18ST103T | Descriptive Statistics | Core | 4 | Data Classification and Tabulation, Measures of Central Tendency, Measures of Dispersion, Moments, Skewness and Kurtosis, Correlation and Regression |
| 18EN101 | Environmental Science | Allied | 2 | Ecosystems, Biodiversity, Environmental Pollution, Social Issues and the Environment, Human Population and the Environment |
| 18PD101 | Soft Skills (Part-A) | Value Added | 1 | Self-Awareness, Goal Setting, Time Management, Interpersonal Skills, Stress Management |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18LEM201T | Communicative English (B) | Core | 3 | Advanced Grammar and Usage, Effective Public Speaking, Group Discussion Techniques, Technical Report Writing, Interview Skills |
| 18MA201T | Algebra and Numerical Methods | Core | 4 | Matrices and Determinants, Eigenvalues and Eigenvectors, Solution of Linear Systems, Interpolation, Numerical Integration |
| 18ST201T | Sampling Distributions | Core | 4 | Sampling Techniques, Large Sample Theory, Chi-Square Distribution, t-Distribution, F-Distribution |
| 18ST202T | Applied Statistics | Core | 4 | Time Series Analysis, Index Numbers, Demographic Methods, Statistical Quality Control, Official Statistics |
| 18ST203P | Statistical Computing using Python (Practical) | Core | 2 | Python Fundamentals, Data Structures in Python, NumPy and Pandas, Data Visualization with Matplotlib/Seaborn, Basic Statistical Operations in Python |
| 18PD201 | Soft Skills (Part-B) | Value Added | 1 | Verbal Communication, Non-Verbal Communication, Presentation Skills, Teamwork and Collaboration, Critical Thinking |
| 18EV101 | Value Education | Allied | 2 | Human Values, Professional Ethics, Social Harmony, Spiritual Development, Global Ethics |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18ST301T | Statistical Inference - I (Estimation) | Core | 4 | Point Estimation, Properties of Estimators, Methods of Estimation, Interval Estimation, Bayesian Estimation |
| 18ST302T | Regression Analysis | Core | 4 | Simple Linear Regression, Multiple Linear Regression, Model Assumptions and Diagnostics, Variable Selection, Non-linear Regression |
| 18ST303T | Data Mining Techniques | Core | 4 | Introduction to Data Mining, Data Preprocessing, Association Rule Mining, Classification Algorithms, Clustering Techniques |
| 18ST304P | Statistical Software Lab - I (SPSS/SAS) | Core | 2 | Data Management in SPSS/SAS, Descriptive Statistics, Hypothesis Testing, ANOVA and Regression Analysis, Report Generation |
| 18ST305T | Operations Research | Core | 4 | Linear Programming, Transportation Problem, Assignment Problem, Game Theory, Queuing Theory |
| 18GS301 | Aptitude | Value Added | 1 | Quantitative Aptitude, Logical Reasoning, Verbal Ability, Data Interpretation, Analytical Skills |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18ST401T | Statistical Inference - II (Testing of Hypotheses) | Core | 4 | Concepts of Hypothesis Testing, Parametric Tests (Z, t, Chi-square), Non-parametric Tests, Analysis of Variance (ANOVA), Goodness of Fit Tests |
| 18ST402T | Econometrics | Core | 4 | Classical Linear Regression Model, Problems in Regression (Multicollinearity), Heteroscedasticity, Autocorrelation, Dummy Variables |
| 18ST403T | Design of Experiments | Core | 4 | Basic Principles of DOE, Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), Factorial Experiments |
| 18ST404P | Statistical Software Lab - II (MINITAB/STATISTICA) | Core | 2 | DOE Analysis in MINITAB/STATISTICA, Time Series Forecasting, Quality Control Charts, Regression and ANOVA, Data Analysis and Interpretation |
| 18ST405T | Actuarial Statistics | Core | 4 | Life Contingencies, Life Tables, Annuities, Premium Calculation, Reserves |
| 18GS401 | General Studies | Value Added | 1 | Current Affairs, Indian History and Culture, Indian Polity, Geography, Science and Technology |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18ST501T | Stochastic Processes | Core | 4 | Markov Chains, Poisson Process, Birth and Death Processes, Continuous Time Markov Chains, Renewal Processes |
| 18ST502T | Multivariate Analysis | Core | 4 | Multivariate Normal Distribution, Principal Component Analysis (PCA), Factor Analysis, Cluster Analysis, Discriminant Analysis |
| 18ST503T | Quality Control and Reliability | Core | 4 | Statistical Process Control, Control Charts for Variables and Attributes, Acceptance Sampling, Reliability Theory, Life Testing |
| 18ST504T | Biostatistics | Core | 4 | Basic Biostatistical Concepts, Clinical Trials, Epidemiological Study Designs, Survival Analysis, Statistical Genetics |
| 18ST5E01 | Data Science Concepts | Elective - I | 4 | Introduction to Data Science, Big Data Technologies, Data Visualization Principles, Fundamentals of Machine Learning, Cloud Computing for Data Science |
| 18ST5S01 | Data Visualization using Tableau/Power BI | Skill Development - I | 2 | Connecting to Data Sources, Creating Basic Charts, Building Interactive Dashboards, Data Filtering and Sorting, Storytelling with Data |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18ST601T | Categorical Data Analysis | Core | 4 | Contingency Tables, Measures of Association, Log-linear Models, Logistic Regression, Ordinal Data Analysis |
| 18ST602T | Time Series and Forecasting | Core | 4 | Components of Time Series, Stationary Time Series, ARIMA Models, Forecasting Methods, ARCH/GARCH Models |
| 18ST603T | Financial Statistics | Core | 4 | Financial Markets and Instruments, Portfolio Theory, Option Pricing Models, Risk Management, Time Series in Finance |
| 18ST6E01 | Machine Learning | Elective - II | 4 | Supervised Learning, Unsupervised Learning, Ensemble Methods, Neural Networks Basics, Model Evaluation and Selection |
| 18ST6S02 | Database Management using SQL | Skill Development - II | 2 | Relational Database Concepts, SQL Querying (SELECT, INSERT, UPDATE, DELETE), Joins and Subqueries, Database Design Principles, Data Integrity and Constraints |
| 18ST6PW | Project Work | Core | 6 | Problem Identification, Literature Review, Methodology Design, Data Collection and Analysis, Report Writing and Presentation |
