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BSC in Statistics at Maharaja Purna Chandra (Autonomous) College
Mayurbhanj, Odisha
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About the Specialization
What is Statistics at Maharaja Purna Chandra (Autonomous) College Mayurbhanj?
This Statistics program at Maharaja Purna Chandra Autonomous College focuses on equipping students with a robust foundation in statistical theory and its diverse applications. It covers essential areas from probability and inference to data analysis and experimental design. In the Indian context, this program is highly relevant for aspiring data professionals and researchers, meeting the growing demand across various sectors.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics and logical reasoning, seeking entry into data-driven fields. It also caters to individuals interested in quantitative research, actuarial science, or careers in government statistical organizations. Students from science or commerce backgrounds passionate about data interpretation will find this program beneficial.
Why Choose This Course?
Graduates can expect promising career paths in India as Data Analysts, Research Statisticians, or Actuarial Trainees. Entry-level salaries often range from INR 3-5 LPA, growing with experience. The program develops strong analytical and problem-solving skills, highly valued across private and public sectors, aligning with India''''s increasing demand for data science professionals and researchers.
Student Success Practices
Foundation Stage
Master Core Probability & Statistics- (Semester 1-2)
Dedicating ample time to understand the fundamental concepts of probability theory and statistical methods is crucial. Regularly solve textbook problems, attend practical sessions diligently, and clarify doubts promptly. Forming study groups can facilitate peer learning and reinforce understanding of complex topics.
Tools & Resources
NCERT Mathematics books, Introduction to Probability and Statistics by Sheldon Ross, Online tutorials for basic concepts
Career Connection
A strong foundation ensures clarity in advanced subjects and is vital for competitive exams and entry-level analytical roles.
Develop Computational Skills with R/Python- (Semester 1-3)
Begin familiarizing yourself with statistical software like R or Python early on. Utilize the SEC course on ''''Statistical Data Analysis Using R/Python'''' fully. Practice coding for data manipulation, descriptive statistics, and basic visualizations. Work on small data projects to apply theoretical knowledge.
Tools & Resources
RStudio, Anaconda Python, Coursera/NPTEL introductory courses on R/Python, GeeksforGeeks for coding practice
Career Connection
Proficiency in statistical programming languages is a non-negotiable skill for almost all data science and analytics jobs today.
Engage in Interdisciplinary Learning- (Semester 1-4)
Utilize the Generic Elective (GE) courses to explore subjects complementary to Statistics, such as Economics, Computer Science, or Mathematics. This broadens your perspective and can reveal interesting applications of statistics in other domains, fostering a more holistic understanding.
Tools & Resources
College library resources for GE subjects, Online articles and documentaries related to interdisciplinary fields
Career Connection
Interdisciplinary knowledge enhances problem-solving abilities and makes you a more versatile candidate for diverse roles.
Intermediate Stage
Apply Statistical Inference to Real Data- (Semester 3-4)
Actively participate in practical sessions for Statistical Inference and Design of Experiments. Focus on applying various estimation and hypothesis testing techniques to real-world datasets, understanding the assumptions and interpretations. This builds confidence in making data-driven decisions.
Tools & Resources
Datasets from Kaggle/UCI Machine Learning Repository, SPSS/Minitab for practical application (if available), Syllabus prescribed textbooks for examples
Career Connection
Mastering inferential statistics is crucial for roles involving research, quality control, and advanced analytics, enabling you to draw meaningful conclusions from data.
Explore Specialization Pathways- (Semester 3-4)
In semesters 3-4, start researching the various DSE options available for later semesters (e.g., Econometrics, Actuarial Statistics, Biostatistics, Operations Research). Attend seminars, read about these fields, and talk to faculty to understand which areas align with your career interests. Choose SECs wisely to build relevant skills.
Tools & Resources
Career counseling sessions, Online resources about different statistical specializations, LinkedIn profiles of professionals
Career Connection
Early specialization helps in tailoring your skills for specific industries and improves your chances of securing internships and job offers in your chosen field.
Undertake Mini Projects & Internships- (Semester 3-5)
Seek opportunities for mini-projects or short internships during semester breaks or as part of coursework. Even small projects involving data collection, analysis, and reporting can significantly enhance practical skills and build a portfolio. Network with seniors for internship leads.
Tools & Resources
College project cell, Local NGOs or small businesses for data analysis tasks, Internshala for internship search
Career Connection
Practical experience is highly valued by employers and provides a competitive edge during placements, demonstrating real-world problem-solving abilities.
Advanced Stage
Excel in Discipline Specific Electives & Project Work- (Semester 5-6)
Deep dive into your chosen Discipline Specific Electives (DSEs) in Semesters 5 & 6. If opting for ''''Project Work'''', select a challenging topic and execute it meticulously, focusing on rigorous methodology, analysis, and presentation. This is your chance to showcase expertise.
Tools & Resources
Advanced textbooks for DSEs, Research papers in chosen area, Mentorship from faculty advisors
Career Connection
Strong performance in DSEs and a well-executed project demonstrates specialized knowledge and research capability, essential for higher studies or advanced roles.
Prepare for Placements and Higher Studies- (Semester 5-6)
Actively participate in campus placement drives, prepare a compelling resume, and practice interview skills, including technical and HR rounds. For higher studies, research postgraduate programs (MSc, MBA with analytics) and prepare for entrance exams like GATE, NET, or university-specific tests.
Tools & Resources
College placement cell, Mock interview sessions, Online aptitude test platforms, Previous year question papers
Career Connection
Strategic preparation ensures a smooth transition to either immediate employment in roles like Data Scientist/Analyst or admission to prestigious postgraduate programs.
Build a Professional Network- (Semester 4-6)
Attend workshops, conferences, and guest lectures to interact with industry professionals and academicians. Connect with alumni and faculty on platforms like LinkedIn. A strong professional network can open doors to mentorship, internships, and future career opportunities.
Tools & Resources
LinkedIn, Professional bodies like Indian Society for Probability and Statistics (ISPS), College alumni association events
Career Connection
Networking is crucial for career advancement, providing insights into industry trends and potential job leads beyond formal applications.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 3 years / 6 semesters
Credits: 144 Credits
Assessment: Internal: 20% (for theory), 40% (for practical), External: 80% (for theory), 60% (for practical)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-1A | Probability Theory | Core | 6 | Probability definition and theorems, Conditional probability and Bayes'''' theorem, Random variables and expectation, Discrete probability distributions (Binomial, Poisson), Continuous probability distributions (Normal, Exponential) |
| GE-1 | Generic Elective - 1 | Generic Elective (Student Choice) | 6 | |
| AECC-1 | Environmental Studies / MIL (Oriya / Alt. English) | Ability Enhancement Compulsory Course | 2 |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-1B | Statistical Methods | Core | 6 | Measures of central tendency and dispersion, Skewness, Kurtosis and moments, Correlation analysis (Simple, Partial, Multiple), Regression analysis (Linear regression, curves), Association of attributes, Contingency tables |
| GE-2 | Generic Elective - 2 | Generic Elective (Student Choice) | 6 | |
| AECC-2 | English Communication / MIL (Oriya / Alt. English) | Ability Enhancement Compulsory Course | 2 |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-1C | Statistical Inference | Core | 6 | Sampling distributions (Chi-square, t, F), Point and interval estimation, Properties of estimators, Testing of hypotheses (Large and small samples), Non-parametric tests (Sign, Wilcoxon, Mann-Whitney) |
| GE-3 | Generic Elective - 3 | Generic Elective (Student Choice) | 6 | |
| SEC-1 (Option 1) | Statistical Data Analysis Using R/Python | Skill Enhancement Course (Student Choice) | 4 | Introduction to R/Python programming, Data input, output, and manipulation, Descriptive statistics and visualization, Probability distributions and hypothesis testing in R/Python, Regression and correlation analysis |
| SEC-1 (Option 2) | Data Base Management System | Skill Enhancement Course (Student Choice) | 4 | Database systems architecture, Entity-Relationship (ER) model, Relational model and integrity constraints, Structured Query Language (SQL), Normalization and transaction management |
| SEC-1 (Option 3) | Research Methodology | Skill Enhancement Course (Student Choice) | 4 | Introduction to research and research design, Methods of data collection, Sampling techniques and sample size determination, Hypothesis formulation and testing, Report writing and presentation |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-1D | Sampling Techniques and Design of Experiments | Core | 6 | Sampling methods (SRS, Stratified, Systematic), Estimation of population parameters, Analysis of Variance (ANOVA - CRD, RBD, LSD), Factorial experiments (2^2, 2^3), Non-sampling errors and survey design |
| GE-4 | Generic Elective - 4 | Generic Elective (Student Choice) | 6 | |
| SEC-2 (Option 1) | Demography and Vital Statistics | Skill Enhancement Course (Student Choice) | 4 | Sources of demographic data, Measures of fertility (CDR, GFR, TFR), Measures of mortality (CDR, IMR, SMR), Population growth and population projection, Life table construction and its uses |
| SEC-2 (Option 2) | Statistical Quality Control | Skill Enhancement Course (Student Choice) | 4 | Quality control philosophy and tools, Control charts for variables (X-bar, R, S charts), Control charts for attributes (p, np, c, u charts), Acceptance sampling (Single, Double sampling plans), Process capability analysis |
| SEC-2 (Option 3) | Survey Sampling | Skill Enhancement Course (Student Choice) | 4 | Sample vs. census, sampling frame, Questionnaire design and pretesting, Field work, data collection and supervision, Errors in surveys (sampling and non-sampling), Ethical considerations in surveys |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSE-1A | Operation Research | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Linear Programming Problem (LPP) - Simplex method, Duality in LPP, Sensitivity analysis, Transportation Problem, Assignment Problem, Game Theory, Queuing Theory (M/M/1), Network analysis (CPM/PERT) |
| DSE-1B | Econometrics | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Classical Linear Regression Model (CLRM), Assumptions of CLRM, Estimation (OLS), Problems of multicollinearity, heteroscedasticity, Autocorrelation and its detection, Dummy variables, panel data basics |
| DSE-1C | Actuarial Statistics | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Insurance business and risk theory, Life tables and their construction, Annuities (pure endowment, term assurance), Premium calculation (net single, annual), Claims and policy valuation |
| DSE-1D | Applied Statistics | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Time series analysis (components, ARIMA models), Index numbers (construction, tests), Demand analysis and elasticity, Official statistics in India (CSO, NSSO), Economic statistics applications |
| DSE-1E | Biostatistics | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Introduction to epidemiology and clinical trials, Statistical methods in biological assays, Survival analysis basics (Kaplan-Meier), Demographic concepts in health, Genetic statistics basics |
| DSE-1F | Non-parametric Inference | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Non-parametric vs. parametric methods, Sign test, Wilcoxon signed-rank test, Mann-Whitney U test, Kruskal-Wallis H test, Rank correlation (Spearman''''s), Kendall''''s tau, Tests of randomness (Run test) |
| DSE-1H | Financial Statistics | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Financial markets and instruments, Asset returns and risk measurement, Portfolio theory (Markowitz model), Option pricing (Black-Scholes model), Risk management and Value at Risk (VaR) |
| DSE-1G | Project Work | Discipline Specific Elective (Can be chosen in lieu of one DSE paper in Sem 5 or 6) | 6 | Problem identification and literature review, Data collection and survey design, Statistical analysis and interpretation, Report writing and presentation, Ethical considerations in research |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSE-1A | Operation Research | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Linear Programming Problem (LPP) - Simplex method, Duality in LPP, Sensitivity analysis, Transportation Problem, Assignment Problem, Game Theory, Queuing Theory (M/M/1), Network analysis (CPM/PERT) |
| DSE-1B | Econometrics | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Classical Linear Regression Model (CLRM), Assumptions of CLRM, Estimation (OLS), Problems of multicollinearity, heteroscedasticity, Autocorrelation and its detection, Dummy variables, panel data basics |
| DSE-1C | Actuarial Statistics | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Insurance business and risk theory, Life tables and their construction, Annuities (pure endowment, term assurance), Premium calculation (net single, annual), Claims and policy valuation |
| DSE-1D | Applied Statistics | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Time series analysis (components, ARIMA models), Index numbers (construction, tests), Demand analysis and elasticity, Official statistics in India (CSO, NSSO), Economic statistics applications |
| DSE-1E | Biostatistics | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Introduction to epidemiology and clinical trials, Statistical methods in biological assays, Survival analysis basics (Kaplan-Meier), Demographic concepts in health, Genetic statistics basics |
| DSE-1F | Non-parametric Inference | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Non-parametric vs. parametric methods, Sign test, Wilcoxon signed-rank test, Mann-Whitney U test, Kruskal-Wallis H test, Rank correlation (Spearman''''s), Kendall''''s tau, Tests of randomness (Run test) |
| DSE-1H | Financial Statistics | Discipline Specific Elective (Student Choice - 2 from DSE pool in Sem 5 & 6) | 6 | Financial markets and instruments, Asset returns and risk measurement, Portfolio theory (Markowitz model), Option pricing (Black-Scholes model), Risk management and Value at Risk (VaR) |
| DSE-1G | Project Work | Discipline Specific Elective (Can be chosen in lieu of one DSE paper in Sem 5 or 6) | 6 | Problem identification and literature review, Data collection and survey design, Statistical analysis and interpretation, Report writing and presentation, Ethical considerations in research |
