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BSC in Statistics at SSR College of Arts, Commerce and Science
Dadra and Nagar Haveli, Dadra and Nagar Haveli and Daman and Diu
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About the Specialization
What is Statistics at SSR College of Arts, Commerce and Science Dadra and Nagar Haveli?
This Statistics program at SSR College of Arts, Commerce and Science focuses on equipping students with robust analytical and data interpretation skills, highly relevant to India''''s burgeoning data-driven economy. The curriculum, aligned with VNSGU''''s NEP framework, covers foundational to advanced statistical methodologies, fostering critical thinking and problem-solving abilities crucial for various Indian industries including finance, healthcare, and market research.
Who Should Apply?
This program is ideal for fresh science graduates with a strong aptitude for mathematics and logical reasoning seeking entry into the analytical domain. It also caters to individuals aiming to build a career in data science, actuarial science, or research in India. Prior knowledge of basic mathematics and an eagerness to work with data are key prerequisites for this intellectually stimulating program.
Why Choose This Course?
Graduates of this program can expect promising career paths in India as Data Analysts, Research Statisticians, Actuarial Analysts, or Business Intelligence professionals. Entry-level salaries typically range from INR 3-6 LPA, growing significantly with experience. The program provides a strong foundation for pursuing higher studies like M.Sc. Statistics, Data Science, or specialized certifications in areas like SAS, R, or Python, which are highly valued in the Indian job market.
Student Success Practices
Foundation Stage
Build Strong Mathematical and Probabilistic Foundations- (Semester 1-2)
Dedicate consistent time to mastering core mathematical concepts, probability theory, and introductory statistics. Utilize online platforms like Khan Academy for calculus and linear algebra refreshers, and practice problems from standard Indian textbooks like S.C. Gupta for Statistics to solidify understanding.
Tools & Resources
Khan Academy (Math), NPTEL (Probability & Statistics), Standard Statistics Textbooks (e.g., S.C. Gupta)
Career Connection
A strong foundation is critical for advanced topics and crucial for clearing competitive exams or technical interviews for analyst roles.
Develop Early Programming Skills (R/Python)- (Semester 1-2)
Begin learning R or Python programming concurrently with theoretical subjects. Focus on data manipulation, descriptive statistics, and basic visualization. Participate in beginner-friendly coding challenges on platforms like HackerRank or GeeksforGeeks, and explore introductory projects on Kaggle.
Tools & Resources
DataCamp (free courses), Coursera (Python/R for Data Science), HackerRank, GeeksforGeeks, Kaggle (datasets)
Career Connection
Proficiency in statistical software is non-negotiable for modern data roles and greatly enhances internship opportunities.
Engage in Peer Learning and Discussion Groups- (Semester 1-2)
Form study groups with classmates to discuss challenging concepts, solve problems collaboratively, and prepare for exams. Actively participate in classroom discussions and seek clarification from professors, fostering a deeper understanding of the curriculum.
Tools & Resources
College Library, Classroom/Online collaboration tools
Career Connection
Improves communication skills and problem-solving abilities, which are vital for team-based projects in industry.
Intermediate Stage
Apply Statistical Concepts to Real-world Data- (Semester 3-5)
Beyond textbook problems, actively seek and work on datasets related to Indian economic, social, or business scenarios. Use R/Python to implement sampling techniques, hypothesis testing, and regression analysis. Look for open data portals from Indian government or research bodies.
Tools & Resources
Kaggle (Indian datasets), data.gov.in, Reserve Bank of India (RBI) Data, Ministry of Statistics and Program Implementation
Career Connection
Practical application bridges theory-practice gap, making you job-ready for data-intensive roles in India.
Undertake Mini-Projects and Certifications- (Semester 3-5)
Complete small statistical projects individually or in groups, focusing on specific methodologies like Time Series Analysis or Multivariate Analysis. Consider pursuing industry-recognized certifications in Excel for Data Analysis, SQL, or specific R/Python libraries (e.g., Pandas, NumPy, SciPy) from platforms like NPTEL or Udemy.
Tools & Resources
NPTEL courses, Udemy/Coursera certifications, GitHub (for project showcasing)
Career Connection
Enhances your resume, demonstrates practical skills to Indian recruiters, and opens doors for specialized roles.
Network and Attend Industry Workshops/Webinars- (Semester 3-5)
Connect with professionals on LinkedIn working in analytics or data science in India. Attend virtual or local workshops and webinars organized by professional bodies or colleges on topics like Machine Learning or Actuarial Science to gain insights into industry trends and job requirements.
Tools & Resources
LinkedIn, Professional statistical societies in India, College career cell
Career Connection
Expands your professional network, provides mentorship opportunities, and helps you discover potential job openings and career paths in the Indian market.
Advanced Stage
Focus on Specialization and Advanced Tools- (Semester 6)
Deep dive into your chosen DSEs (Econometrics, Biostatistics, Bayesian Inference, Operations Research). Master advanced statistical software like SAS, SPSS, or specialized R/Python libraries relevant to your chosen area. Aim to contribute to a research paper or present a project at a college-level symposium.
Tools & Resources
SAS/SPSS (academic licenses), Advanced R/Python libraries (e.g., caret, tidyverse, statsmodels), Research journals
Career Connection
Positions you as a specialist, highly attractive to specific industry roles (e.g., Actuarial Analyst, Econometrician) and for higher studies.
Undertake a Comprehensive Project/Internship- (Semester 6)
Secure a full-time internship in a relevant Indian company or embark on a significant research project. This should involve real-world data, complex statistical modeling, and clear interpretation of results. Document your work meticulously and prepare a strong project report and presentation.
Tools & Resources
College Placement Cell, Internshala, LetsIntern, Industry contacts
Career Connection
Provides invaluable industry experience, often leading to pre-placement offers (PPOs) in Indian companies, and strengthens your resume for placements.
Intensive Placement Preparation- (Semester 6)
Engage in rigorous aptitude training, mock interviews, and group discussions tailored for data science and analytics roles. Practice coding interview questions, brush up on statistical concepts, and prepare a portfolio of your projects to showcase your skills effectively to Indian employers.
Tools & Resources
Placement coaching centers, Online aptitude tests, Mock interview sessions by faculty/alumni, LinkedIn profiles of professionals
Career Connection
Directly prepares you for the competitive Indian job market, maximizing chances of securing a good placement or admission to a top postgraduate program.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 (HSC) or equivalent examination with Science stream having Mathematics as one of the subjects.
Duration: 6 semesters / 3 years
Credits: 110 Credits
Assessment: Internal: 30% (Theory), 50% (Practical/Project), External: 70% (Theory), 50% (Practical/Project)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT 101 | Introductory Statistics | Core (Discipline Specific Course - DSC) | 6 | Introduction to Statistics, Data Representation, Measures of Central Tendency, Measures of Dispersion, Moments, Skewness, Kurtosis |
| STAT 102 | Probability and Probability Distributions | Core (Discipline Specific Course - DSC) | 4 | Probability Theory, Random Variables, Expectation and Variance, Binomial and Poisson Distributions, Normal Distribution |
| ENV 101 | Environmental Science | Ability Enhancement Compulsory Course (AECC) | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Environmental Ethics, Sustainable Development |
| VAC 101 | Indian Constitution | Value Added Course (VAC) | 2 | Preamble and Fundamental Rights, Directive Principles of State Policy, Union and State Governments, Judiciary and Local Governance, Constitutional Amendments |
| MDC XXX | Multi-Disciplinary Course (Elective) | Multi-Disciplinary Course (MDC) | 2 | Chosen from a pool of subjects across disciplines |
| STAT 103 | Practical based on STAT 101 & 102 | Practical (DSC) | 2 | Data Organization and Presentation, Descriptive Statistics using Software, Probability Calculations, Fitting of Distributions |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT 201 | Sampling Techniques and Design of Experiments | Core (Discipline Specific Course - DSC) | 6 | Census vs. Sample Survey, Simple Random Sampling, Stratified and Systematic Sampling, Analysis of Variance (ANOVA), CRD, RBD, Latin Square Design |
| STAT 202 | Statistical Inference - I | Core (Discipline Specific Course - DSC) | 4 | Estimation Theory, Properties of Estimators, Methods of Estimation (MLE, MOM), Testing of Hypotheses, Large Sample Tests (Z-tests) |
| ENG 201 | English Communication | Ability Enhancement Compulsory Course (AECC) | 2 | Grammar and Vocabulary, Reading Comprehension, Writing Skills (Essays, Reports), Oral Communication, Presentation Skills |
| VAC 201 | Yoga and Meditation | Value Added Course (VAC) | 2 | Basics of Yoga and Asanas, Breathing Techniques (Pranayama), Meditation Practices, Benefits for Physical Health, Stress Management |
| MDC XXX | Multi-Disciplinary Course (Elective) | Multi-Disciplinary Course (MDC) | 2 | Chosen from a pool of subjects across disciplines |
| STAT 203 | Practical based on STAT 201 & 202 | Practical (DSC) | 2 | Sampling Methods Implementation, ANOVA computations, Hypothesis Testing for Large Samples, Using Statistical Packages for Analysis |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT 301 | Distribution Theory | Core (Discipline Specific Course - DSC) | 6 | Joint Probability Distributions, Marginal and Conditional Distributions, Transformation of Random Variables, Sampling Distributions (Chi-square, t, F), Order Statistics |
| STAT 302 | Statistical Inference - II | Core (Discipline Specific Course - DSC) | 4 | Small Sample Tests (t, F, Chi-square), Non-parametric Tests, Sign Test, Wilcoxon Signed-Rank Test, Mann-Whitney U Test, Kruskal-Wallis Test |
| STAT 303 | Statistical Software (R/Python) | Skill Enhancement Course (SEC) | 2 | Introduction to R/Python Programming, Data Import and Export, Data Manipulation and Cleaning, Basic Statistical Analysis in R/Python, Creating Simple Visualizations |
| STAT 304 | Practical based on STAT 301 & 302 | Practical (DSC) | 2 | Fitting of Various Distributions, Application of Small Sample Tests, Implementation of Non-parametric Tests, Using R/Python for Statistical Inference |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT 401 | Regression Analysis and Forecasting | Core (Discipline Specific Course - DSC) | 6 | Simple Linear Regression, Multiple Linear Regression, Assumptions and Diagnostics, Time Series Components, Forecasting Models (ARIMA) |
| STAT 402 | Demography and Actuarial Statistics | Core (Discipline Specific Course - DSC) | 4 | Population Theories, Measures of Fertility and Mortality, Life Tables and Population Projections, Principles of Insurance, Premium Calculation |
| STAT 403 | Data Visualization | Skill Enhancement Course (SEC) | 2 | Principles of Effective Data Visualization, Types of Charts and Graphs, Tools for Data Visualization (ggplot2, Tableau), Interactive Visualizations, Storytelling with Data |
| STAT 404 | Practical based on STAT 401 & 402 | Practical (DSC) | 2 | Linear Regression Modeling, Time Series Decomposition, Forecasting using Statistical Software, Demographic Rate Calculation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT 501 | Multivariate Analysis | Core (Discipline Specific Course - DSC) | 6 | Vector and Matrix Algebra Review, Multivariate Normal Distribution, Hotelling''''s T-square Test, MANOVA, Principal Component Analysis, Factor Analysis |
| STAT 502 | Quality Control and Reliability Theory | Core (Discipline Specific Course - DSC) | 4 | Statistical Process Control, Control Charts (X-bar, R, p, np, c, u), Acceptance Sampling, Reliability Concepts, System Reliability and Redundancy |
| STAT 503-A | Econometrics | Elective (Discipline Specific Elective - DSE) | 4 | Introduction to Econometrics, Classical Linear Regression Model, Problems with OLS, Time Series Econometrics, Forecasting in Econometrics |
| STAT 503-B | Biostatistics | Elective (Discipline Specific Elective - DSE) | 4 | Medical Data Analysis, Epidemiology Measures, Clinical Trials Design, Survival Analysis Basics, Genetics and Biostatistical Methods |
| STAT 505 | Practical based on STAT 501, 502 & DSEs | Practical (DSC/DSE) | 2 | Multivariate Data Analysis, Control Chart Construction, Acceptance Sampling Plan Design, Econometric Model Building, Biostatistical Data Analysis |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT 601 | Operations Research | Core (Discipline Specific Course - DSC) | 6 | Linear Programming Problems, Simplex Method, Transportation and Assignment Problems, Game Theory, Queuing Theory Models |
| STAT 602 | Stochastic Processes | Core (Discipline Specific Course - DSC) | 4 | Markov Chains, Chapman-Kolmogorov Equations, Classification of States, Poisson Process, Birth and Death Process |
| STAT 603-A | Bayesian Inference | Elective (Discipline Specific Elective - DSE) | 4 | Bayes'''' Theorem, Prior and Posterior Distributions, Conjugate Priors, Bayesian Estimation, Hypothesis Testing in Bayesian Framework |
| STAT 603-B | Actuarial Modelling | Elective (Discipline Specific Elective - DSE) | 4 | Life Insurance Models, Survival Models, Annuities, Risk Theory Basics, Pension Fund Mathematics |
| STAT 605 | Practical based on STAT 601, 602 & DSEs | Practical (DSC/DSE) | 2 | Solving LP and OR problems, Stochastic Process Simulation, Bayesian Data Analysis, Actuarial Calculations |
| STAT 606 | Project Work / Dissertation | Project | 6 | Research Question Formulation, Data Collection and Cleaning, Application of Statistical Methods, Report Writing, Presentation and Viva-Voce |
