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M-SC-MASTER-OF-SCIENCE in Statistics at Dibrugarh University
Dibrugarh, Assam
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About the Specialization
What is Statistics at Dibrugarh University Dibrugarh?
This M.Sc Statistics program at Dibrugarh University focuses on developing advanced statistical theory and applied skills. It equips students with tools for data analysis, inference, and modeling essential for various sectors in the Indian economy, emphasizing both theoretical foundations and practical applications to meet contemporary industry demands.
Who Should Apply?
This program is ideal for mathematics or statistics graduates aspiring for data-driven careers. It suits fresh graduates seeking entry into analytics, research, or government statistics roles, and also working professionals looking to upskill in advanced statistical methods to enhance their career trajectory in growing Indian industries and research fields.
Why Choose This Course?
Graduates can expect diverse career paths in India, including Data Scientist, Statistician, Business Analyst, and Research Analyst. Entry-level salaries range from INR 4-7 LPA, with experienced professionals earning INR 10-20+ LPA in Indian companies. The program also prepares students for advanced research, PhD programs, or competitive examinations like those conducted by UPSC/SSC.
Student Success Practices
Foundation Stage
Master Foundational Statistical Concepts- (Semester 1-2)
Dedicate significant time to understanding core probability, inference, linear algebra, and sampling theory. Utilize textbooks, online resources like NPTEL videos, and class notes to build a strong conceptual base.
Tools & Resources
NPTEL courses on Probability and Statistics, Textbooks by Hogg & Craig, Casella & Berger, Online forums like Cross Validated
Career Connection
A solid foundation is crucial for mastering advanced topics and excelling in technical interviews for data analyst and statistician roles in India.
Develop Robust Programming Skills (R/Python)- (Semester 1-2)
Proactively learn statistical programming languages like R or Python, even before dedicated practical courses. Practice data manipulation, visualization, and basic statistical analysis using real-world datasets.
Tools & Resources
DataCamp, Coursera, Kaggle for datasets, ''''R for Data Science'''' by Wickham, ''''Python for Data Analysis'''' by McKinney
Career Connection
Proficiency in programming is a non-negotiable skill for almost all data science and analytics roles in the Indian job market.
Engage in Peer Learning and Problem Solving- (Semester 1-2)
Form study groups to discuss complex topics, solve problems collaboratively, and clarify doubts. Teach concepts to peers to solidify your own understanding. Participate in department-level quizzes and problem-solving sessions.
Tools & Resources
Whiteboards, Online collaboration tools (Google Docs), Previous year question papers, Shared notes
Career Connection
Improves communication skills, fosters critical thinking, and prepares for team-based projects common in the Indian industry.
Intermediate Stage
Apply Theory through Mini-Projects and Electives- (Semester 3)
Actively choose your elective based on career interest and undertake mini-projects in areas like multivariate analysis or stochastic processes. Focus on applying learned statistical methods to real or simulated datasets, documenting your process and findings thoroughly.
Tools & Resources
University faculty for project guidance, Statistical software manuals, Kaggle datasets, Academic journals for inspiration
Career Connection
Builds practical problem-solving skills and a portfolio showcasing your applied statistical abilities, crucial for job applications in India.
Gain Early Industry Exposure- (Semester 3)
Seek opportunities for short-term internships, particularly during semester breaks, or volunteer for data analysis tasks in local organizations. This hands-on experience helps in understanding industry requirements and applying classroom knowledge.
Tools & Resources
LinkedIn for internship searches, University placement cell, Local NGOs or startups, Industry-specific job boards
Career Connection
Provides valuable real-world context, enhances resume, and often leads to networking opportunities for future placements in the Indian market.
Enhance Presentation and Communication Skills- (Semester 3)
Participate actively in seminars, group discussions, and present your project work effectively. Focus on clearly articulating complex statistical concepts and findings to a diverse audience, a vital skill in any data role.
Tools & Resources
Presentation software (PowerPoint/Google Slides), Public speaking clubs, Peer feedback sessions, University communication workshops
Career Connection
Strong communication skills are essential for collaborating with teams and presenting insights to stakeholders, significantly boosting career prospects.
Advanced Stage
Excel in Dissertation/Project Work- (Semester 4)
Choose a challenging and relevant topic for your dissertation. Dedicate significant effort to research, data collection, advanced analysis, and meticulous report writing. Aim for original contributions or in-depth application of complex methods.
Tools & Resources
Research databases, Statistical software (R/Python, SAS, SPSS), Academic supervisors, Literature review tools
Career Connection
A high-quality dissertation demonstrates advanced research capabilities, problem-solving prowess, and a deep understanding of a specific area, greatly aiding in securing top placements or further academic pursuits.
Rigorous Placement Preparation- (Semester 4)
Begin intensive preparation for placements, including quantitative aptitude, logical reasoning, data interpretation, and advanced statistical concepts. Practice mock interviews and group discussions, focusing on company-specific questions and case studies relevant to Indian companies.
Tools & Resources
Online aptitude platforms (IndiaBix, PrepInsta), Interview preparation books, University placement training, Alumni insights
Career Connection
Maximizes chances of securing desirable job offers from leading analytics, finance, and IT companies across India.
Build a Professional Network and Personal Brand- (Semester 4)
Actively connect with professionals in your target industry through LinkedIn, alumni events, and industry conferences. Develop a professional online presence and articulate your skills and career aspirations clearly to potential employers.
Tools & Resources
LinkedIn profiles, Professional portfolios (GitHub for code, blogs for insights), Career fairs, University career services
Career Connection
Facilitates job discovery, mentorship, and long-term career growth within the competitive Indian job market.
Program Structure and Curriculum
Eligibility:
- A candidate who has passed the B.A. or B.Sc. Examination with Honours/Major in Statistics from Dibrugarh University or any other University recognized by Dibrugarh University as equivalent thereto. Candidates with Mathematics Honours/Major having Statistics as one of the subjects in degree level are also eligible to apply.
Duration: 4 semesters / 2 years
Credits: 64 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT10100 | Statistical Methods | Core | 4 | Probability theory fundamentals, Random variables and distributions, Expectation and moments, Sampling distributions, Point and interval estimation |
| STAT10200 | Linear Algebra and Numerical Analysis | Core | 4 | Vector spaces and linear transformations, Matrix algebra and eigenvalues, Numerical differentiation and integration, Solution of differential equations, Interpolation and approximation |
| STAT10300 | Probability Theory | Core | 4 | Measure theory and probability space, Random variables and expectation, Conditional probability and expectation, Modes of convergence, Laws of Large Numbers and Central Limit Theorem |
| STAT10400 | Practical - I | Practical | 4 | Data collection and organization, Descriptive statistics calculation, Hypothesis testing problems, Regression and correlation analysis, Statistical software application (general) |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT20100 | Inference-I | Core | 4 | Sufficiency and completeness, Point estimation methods, MVUE and Cramer-Rao inequality, Maximum Likelihood Estimation, Bayesian estimation |
| STAT20200 | Sampling Theory | Core | 4 | Sampling methods, Simple Random Sampling, Stratified and Systematic Sampling, Ratio and Regression estimation, Cluster and Multi-stage Sampling |
| STAT20300 | Design of Experiments | Core | 4 | Analysis of Variance (ANOVA), Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), Factorial Experiments |
| STAT20400 | Practical - II | Practical | 4 | Estimation and confidence intervals, Parametric and non-parametric tests, Design of experiments practicals, Sampling techniques implementation, Data analysis using statistical packages |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT30100 | Inference-II | Core | 4 | Hypothesis testing concepts, Neyman-Pearson Lemma, UMP and unbiased tests, Likelihood Ratio Tests, Sequential Probability Ratio Test |
| STAT30200 | Multivariate Analysis | Core | 4 | Multivariate normal distribution, Wishart distribution, Hotelling''''s T-squared statistic, MANOVA and canonical correlation, Principal components and factor analysis |
| STAT30300 | Stochastic Processes | Core | 4 | Markov chains and their applications, Poisson process and generalizations, Birth and death processes, Renewal theory, Queuing models |
| STAT30400 | Elective Course - I | Elective | 4 | Applied Regression Analysis (Linear, Logistic), Official Statistics (Indian statistical system, NSSO), Financial Statistics (Stock price models, Options), Demography (Mortality, Fertility, Population projection) |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT40100 | Demography and Actuarial Statistics | Core | 4 | Measures of mortality and fertility, Life table construction, Population projection methods, Migration analysis, Introduction to actuarial concepts |
| STAT40200 | Project Work/Dissertation | Project | 4 | Research problem identification, Literature review and methodology design, Data collection and statistical analysis, Report writing and interpretation of results, Presentation and viva-voce |
| STAT40300 | Elective Course - II | Elective | 4 | Biostatistics (Clinical trials, Survival analysis), Categorical Data Analysis (Log-linear models), Reliability Theory (Life distributions, System reliability), Time Series Analysis (ARIMA models, Forecasting) |
| STAT40400 | Statistical Computing using R | Practical | 4 | R programming basics and data structures, Data manipulation and visualization in R, Descriptive and inferential statistics in R, Regression and generalized linear models in R, Report generation and automation using R |
