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B-SC-HONS 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 B.Sc. (Hons) Statistics program at Maharaja Purna Chandra Autonomous College focuses on equipping students with a robust foundation in statistical theory, methodology, and applications. The curriculum, designed with India''''s evolving data landscape in mind, covers key areas from descriptive statistics and probability to advanced multivariate analysis and statistical computing. The program emphasizes both theoretical knowledge and practical skills, preparing graduates for data-intensive roles across various sectors in the Indian economy.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics and a keen interest in data analysis and interpretation. It caters to aspiring data scientists, statisticians, and researchers seeking entry into the burgeoning Indian data analytics market. Students looking to pursue higher education in Statistics or related quantitative fields will also find this a suitable foundation, enabling them to tackle complex real-world problems effectively.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including Data Analyst, Business Analyst, Statistician, Market Research Analyst, and Actuarial Analyst. Entry-level salaries typically range from INR 3-6 LPA, with experienced professionals earning significantly more. The strong quantitative skills developed are highly valued across IT, finance, healthcare, and government sectors, fostering continuous growth and opportunities for specialization in areas like AI/ML or Big Data analytics.
Student Success Practices
Foundation Stage
Master Core Mathematical and Statistical Concepts- (Semester 1-2)
Dedicate significant time in Semesters 1 and 2 to build a solid foundation in calculus, linear algebra, and probability theory, which are crucial for advanced statistical topics. Regularly practice problems from textbooks and online platforms.
Tools & Resources
NCERT Math textbooks, Khan Academy, NPTEL online courses for basic calculus/probability, Peer study groups
Career Connection
A strong foundation ensures a deeper understanding of advanced subjects, crucial for analytical roles in finance or data science, making future learning and problem-solving much easier.
Develop Early Programming Proficiency- (Semester 1-2)
Begin learning a statistical programming language like R or Python in Semester 1 itself, beyond the curriculum. Utilize online tutorials and practical exercises to become comfortable with data manipulation and basic statistical functions.
Tools & Resources
Coursera/edX (Python/R for Data Science courses), HackerRank, Kaggle (entry-level datasets), GeeksforGeeks
Career Connection
Early programming skills are indispensable for data analyst and data scientist roles, making you highly competitive for internships and entry-level positions right after graduation.
Engage Actively in Problem-Solving and Peer Learning- (Semester 1-2)
Form study groups to discuss complex statistical problems, share understanding, and work through practical assignments. Participate in departmental quizzes and problem-solving competitions to enhance analytical thinking.
Tools & Resources
College library resources, Collaborative online whiteboards, Departmental faculty for guidance
Career Connection
Effective collaboration and problem-solving skills are highly valued in team-oriented industry environments, fostering critical thinking necessary for real-world data challenges.
Intermediate Stage
Undertake Mini-Projects and Internships- (Semester 3-5)
Actively seek out mini-projects or short internships (even unpaid ones) during semester breaks or alongside academic work, focusing on applying statistical concepts to real datasets. This builds practical experience.
Tools & Resources
LinkedIn (for internship searches), Internshala, College career cell, Freelancing platforms for data analysis tasks
Career Connection
Practical project experience is vital for demonstrating your skills to recruiters, significantly boosting your resume for placements in roles like market research or business intelligence.
Specialize through Electives and Certifications- (Semester 3-5)
Carefully choose Discipline Specific Electives (DSEs) aligning with your career interests (e.g., Actuarial, Financial, Bio-Statistics). Supplement with relevant online certifications in areas like SQL, Power BI, or specific statistical packages.
Tools & Resources
NPTEL courses for specialized Statistics, IBM Data Science Professional Certificate, Google Data Analytics Professional Certificate
Career Connection
Specialized knowledge and certifications make you a more attractive candidate for niche roles and higher-paying jobs in sectors like actuarial science or financial analytics.
Participate in Data Science Competitions- (Semester 3-5)
Engage in online data science or statistics competitions on platforms like Kaggle, Analytics Vidhya, or local hackathons. This helps in honing problem-solving skills under pressure and exposes you to diverse datasets.
Tools & Resources
Kaggle.com, Analytics Vidhya, University/College tech fests and hackathons
Career Connection
Success in competitions demonstrates advanced analytical capabilities and critical thinking, acting as a strong portfolio builder and attracting attention from top companies for placements.
Advanced Stage
Build a Strong Portfolio and Network- (Semester 6)
Curate a portfolio of your best projects (academic, internship, or personal) on GitHub or a personal website. Actively network with alumni and industry professionals through LinkedIn and college career events.
Tools & Resources
GitHub, Personal website/blog, LinkedIn Premium (optional), Alumni association events
Career Connection
A compelling portfolio showcases your abilities directly to potential employers, while networking opens doors to referrals and hidden job opportunities, crucial for securing desired roles.
Intensive Placement and Interview Preparation- (Semester 6)
Begin rigorous preparation for aptitude tests, technical interviews (focusing on statistics, machine learning, and programming), and HR rounds. Practice mock interviews and participate in college placement training programs.
Tools & Resources
Placement cell workshops, Glassdoor (for company-specific interview questions), GeeksforGeeks interview sections, Mock interview platforms
Career Connection
Thorough preparation directly increases your chances of converting interviews into job offers, ensuring a smooth transition into your professional career.
Pursue Advanced Research or Project Work- (Semester 6)
Undertake a significant research project or a capstone project in your area of interest, ideally guided by a faculty member. This allows for deep dives into specialized statistical applications and demonstrates independent research capabilities.
Tools & Resources
Research journals (JSTOR, arXiv), College faculty for mentorship, Academic databases, Statistical software packages
Career Connection
A strong final-year project is a key differentiator, especially for roles requiring research acumen or for those aspiring to pursue postgraduate studies and academic careers.
Program Structure and Curriculum
Eligibility:
- Passed +2 Science Examination from CHSE, Odisha or its equivalent recognized by Utkal University. Specific minimum percentage for Honours not detailed in syllabus document.
Duration: 6 semesters / 3 years
Credits: 140 Credits
Assessment: Internal: 20% (Mid Semester Examination), External: 80% (End Semester Examination)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT-C1 | Descriptive Statistics | Core | 6 | Data types, Scales of measurement, Measures of central tendency, Dispersion, Skewness, Kurtosis, Correlation and Regression analysis, Index numbers, Time series components, Official statistics: CSO, NSSO |
| STAT-C2 | Probability Theory | Core | 6 | Classical, Empirical and Axiomatic definitions of Probability, Conditional Probability, Bayes'''' Theorem, Random variables, Probability mass and density functions, Expectation, Variance, Moments, Standard discrete and continuous distributions |
| AECC-1 | Environmental Studies | Ability Enhancement Compulsory Course | 2 | Multidisciplinary nature of environmental studies, Ecosystems, Biodiversity and its conservation, Environmental pollution, Solid waste management, Social issues and the environment, Environmental ethics, Human population and the environment |
| GE-1 | Generic Elective - 1 (from other discipline) | Generic Elective | 6 | As per chosen non-Statistics elective |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT-C3 | Theory of Statistical Inference | Core | 6 | Sampling distributions (t, chi-square, F), Point estimation: Properties of estimators, Methods of estimation: Maximum Likelihood, Moments, Interval estimation, Confidence intervals, Hypothesis testing: Neyman-Pearson Lemma, Likelihood Ratio Test |
| STAT-C4 | Survey Sampling & Indian Official Statistics | Core | 6 | Census vs. Sampling, Principal steps in sample survey, Simple Random Sampling (SRS), Stratified Random Sampling, Systematic Sampling, Ratio and Regression estimation, Functions and publications of CSO, NSSO, Registrar General of India, National Sample Survey, Agricultural Statistics System |
| AECC-2 | English / MIL Communication | Ability Enhancement Compulsory Course | 2 | Theories of communication, Types of communication, Grammar: Articles, Prepositions, Tenses, Voice, Speech, Listening, Speaking, Reading, Writing skills, Paragraph writing, Letter writing, Report writing, Public speaking, Presentation skills |
| GE-2 | Generic Elective - 2 (from other discipline) | Generic Elective | 6 | As per chosen non-Statistics elective |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT-C5 | Statistical Quality Control | Core | 6 | Quality control, Causes of variation, Statistical process control, Control charts for variables (X-bar, R, S), Control charts for attributes (p, np, c, u), Acceptance Sampling: Single, Double, Multiple sampling plans, Operating Characteristic (OC) curve |
| STAT-C6 | Economic Statistics | Core | 6 | Index numbers: Price, Quantity, Value indices, Tests for index numbers: Time Reversal, Factor Reversal, Time series: Components, Moving averages, Exponential smoothing, Analysis of Demand and Supply, Consumer Price Index, GNP, GDP, NNP, National Income estimation |
| STAT-C7 | Demography | Core | 6 | Sources of demographic data (Census, NSSO, SRS), Measures of Fertility: CBR, GFR, TFR, ASFR, Measures of Mortality: CDR, ASDR, IMR, SMR, Life Table: Construction and uses, Population growth models, Migration analysis |
| SEC-1 | Statistical Data Analysis using Software | Skill Enhancement Course | 2 | Introduction to R/SPSS/Python for statistical computing, Data input, manipulation, and output, Descriptive statistics, Graphical representations, Hypothesis testing (t-test, chi-square test, ANOVA), Correlation and regression analysis using software |
| GE-3 | Generic Elective - 3 (from other discipline) | Generic Elective | 6 | As per chosen non-Statistics elective |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT-C8 | Design of Experiments | Core | 6 | Principles of experimental design: Randomization, Replication, Local Control, Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), Factorial experiments, Analysis of Variance (ANOVA) for various designs, Missing plot technique, ANCOVA |
| STAT-C9 | Applied Regression Analysis | Core | 6 | Simple and Multiple Linear Regression models, Estimation of parameters, Hypothesis testing for regression coefficients, Assumptions of regression, Residual analysis, Diagnostics, Polynomial regression, Dummy variables, Introduction to Logistic Regression, Logit model |
| STAT-C10 | Time Series Analysis | Core | 6 | Components of time series: Trend, Seasonality, Cyclical, Irregular, Measurement of Trend: Moving averages, Exponential smoothing, Measurement of Seasonal Variation: Ratio-to-Moving Average Method, Forecasting methods, Auto-correlation, Partial Auto-correlation, Introduction to ARIMA models (AR, MA, ARMA, ARIMA) |
| SEC-2 | Research Methodology and Data Visualization | Skill Enhancement Course | 2 | Research problem formulation, Research design, Methods of data collection, Sampling designs, Tools for data visualization: Bar charts, Pie charts, Histograms, Scatter plots, Infographics, Dashboard creation basics, Report writing, Referencing styles |
| GE-4 | Generic Elective - 4 (from other discipline) | Generic Elective | 6 | As per chosen non-Statistics elective |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT-C11 | Multivariate Analysis | Core | 6 | Multivariate normal distribution, Mahalanobis D-square, Hotelling''''s T-square statistic, MANOVA, Discriminant analysis, Classification, Principal Component Analysis (PCA), Factor analysis, Cluster analysis |
| STAT-C12 | Stochastic Processes | Core | 6 | Stochastic processes: Definitions, Classification, Markov chains: Transition probability matrix, Stationary distribution, Classification of states, Random walk, Poisson process: Properties, Birth and Death processes, Applications in queueing theory, reliability |
| DSE-1 | Discipline Specific Elective - 1 (Choose one from list) | Elective | 6 | Actuarial Statistics / Bio-Statistics / Financial Statistics / Bayesian Inference / Reliability Theory (as per choice) |
| DSE-2 | Discipline Specific Elective - 2 (Choose one from list) | Elective | 6 | Actuarial Statistics / Bio-Statistics / Financial Statistics / Bayesian Inference / Reliability Theory (as per choice) |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT-C13 | Operations Research | Core | 6 | Linear Programming Problem (LPP): Formulation, Graphical method, Simplex method, Duality in LPP, Transportation Problem: Initial solution, Optimality tests, Assignment Problem, Game Theory: Two-person zero-sum games, Queuing Theory: M/M/1 model, Inventory control models |
| STAT-C14 | Statistical Computing using C/R/Python | Core | 6 | Programming fundamentals: Data types, Operators, Control structures, Functions, Arrays, Pointers, File I/O (in C/R/Python), Implementation of statistical methods: Descriptive statistics, Regression, Simulation of random variables, Hypothesis testing using programming, Data visualization and reporting |
| DSE-3 | Discipline Specific Elective - 3 (Choose one from list) | Elective | 6 | Actuarial Statistics / Bio-Statistics / Financial Statistics / Bayesian Inference / Reliability Theory / Project Work (as per choice) |
| DSE-4 | Discipline Specific Elective - 4 (Choose one from list) | Elective | 6 | Actuarial Statistics / Bio-Statistics / Financial Statistics / Bayesian Inference / Reliability Theory / Project Work (as per choice) |
