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MA-MASTER-OF-ARTS in Statistics at Dibrugarh University
Dibrugarh, Assam
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About the Specialization
What is Statistics at Dibrugarh University Dibrugarh?
This Statistics program at Dibrugarh University focuses on equipping students with a robust foundation in theoretical statistics and practical data analysis techniques. With India''''s rapidly growing data-driven economy, the program emphasizes quantitative methods, statistical inference, modeling, and computation using modern software like R. It''''s designed to meet the increasing demand for skilled statisticians and data scientists across various Indian industries.
Who Should Apply?
This program is ideal for fresh graduates holding a B.A. or B.Sc. degree with Statistics or Mathematics as a major, who aspire to build a career in data science, analytics, or statistical research. It also suits working professionals seeking to upskill in advanced statistical methodologies and data manipulation. Candidates should possess strong analytical skills and a keen interest in quantitative problem-solving to thrive in this rigorous curriculum.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including roles such as Data Scientist, Statistician, Business Analyst, Market Research Analyst, or Biostatistician within IT firms, financial services, healthcare, and government sectors. Entry-level salaries typically range from INR 4-7 LPA, with experienced professionals potentially earning INR 8-15+ LPA. The program provides a solid base for advanced studies or certifications in areas like SAS or Python for data analysis, fostering significant career growth.
Student Success Practices
Foundation Stage
Master Core Statistical Concepts- (Semester 1-2)
Dedicate significant time to thoroughly understand fundamental statistical theories, probability, and inference. Utilize standard textbooks, supplementary online courses like NPTEL lectures, and consistently solve practice problems to build a strong theoretical base.
Tools & Resources
Standard Statistics Textbooks, NPTEL Online Courses, Problem Sets and Exercise Books
Career Connection
A robust theoretical foundation is crucial for excelling in advanced statistical modeling and machine learning roles, providing the bedrock for analytical thinking essential in any data-driven career.
Become Proficient in R Programming- (Semester 1-2)
Actively engage in hands-on practice with R. Work through tutorials, implement statistical methods learned in class, and explore various R packages for data manipulation, visualization, and basic modeling. Participate in beginner-friendly coding challenges.
Tools & Resources
RStudio IDE, Coursera/edX R Programming Courses, Kaggle Datasets, GeeksforGeeks R Tutorials
Career Connection
Proficiency in R is a highly sought-after skill for data analyst and data scientist positions, enabling efficient data processing, analysis, and visualization in professional settings.
Form Study Groups and Engage in Peer Learning- (Semester 1-2)
Collaborate with peers to discuss complex topics, clarify doubts, and jointly solve challenging statistical problems. Active participation in discussions and reciprocal teaching reinforces understanding and develops collaborative skills.
Tools & Resources
University Library Resources, Online Discussion Forums, Peer Study Sessions
Career Connection
Develops teamwork and communication skills, which are vital for working in multi-disciplinary teams in the industry, enhancing problem-solving efficiency and knowledge sharing.
Intermediate Stage
Apply Statistical Models to Real-world Data- (Semester 3)
Move beyond theoretical exercises by applying learned statistical models (regression, multivariate analysis, time series) to public datasets. Focus on interpreting results and communicating insights effectively. This builds a practical portfolio.
Tools & Resources
Kaggle, UCI Machine Learning Repository, Government Data Portals (e.g., data.gov.in), R Markdown for Reporting
Career Connection
Directly enhances capabilities required for roles involving data modeling and predictive analytics, allowing students to showcase practical application skills during interviews and projects.
Seek Internships and Short-term Projects- (Semester 3)
Actively look for internship opportunities or short-term projects in local businesses, NGOs, or research institutions. Even unpaid internships offer invaluable practical experience and industry exposure. Engage with faculty for potential academic projects.
Tools & Resources
University Placement Cell, LinkedIn, Networking with Faculty and Alumni, Internshala
Career Connection
Provides critical real-world experience, helps in building a professional network, and often leads to pre-placement offers, significantly boosting employability upon graduation.
Participate in Data Science Competitions- (Semester 3)
Engage in online data science competitions on platforms like Kaggle or Analytics Vidhya. This helps apply learned techniques under time pressure, fosters problem-solving skills, and allows learning from diverse approaches and solutions.
Tools & Resources
Kaggle.com, Analytics Vidhya, HackerRank
Career Connection
Showcases initiative and practical skills to potential employers, makes a resume stand out, and provides tangible projects to discuss during technical interviews.
Advanced Stage
Develop a Strong Capstone Project Portfolio- (Semester 4)
Focus intensely on the project work (STAT 40400). Choose a challenging, impactful research question, meticulously collect and analyze data, and present your findings in a professional, articulate manner. This serves as a cornerstone of your professional portfolio.
Tools & Resources
Research Papers and Journals, Advanced R Libraries, Professional Presentation Tools (e.g., LaTeX Beamer), Faculty Mentorship
Career Connection
A well-executed project demonstrates advanced analytical abilities, independent problem-solving skills, and research aptitude, which are highly valued in academic and industry research roles.
Network with Professionals and Alumni- (Semester 4)
Attend university-organized career fairs, industry seminars, and alumni networking events. Connect with professionals on LinkedIn, seeking mentorship and insights into industry trends and job opportunities in the Indian market.
Tools & Resources
LinkedIn, University Alumni Network, Industry Conferences and Webinars
Career Connection
Effective networking can open doors to internships, job referrals, and valuable career advice, significantly aiding in securing a desirable position post-graduation.
Intensive Placement and Interview Preparation- (Semester 4)
Actively prepare for placement season by practicing aptitude tests, technical interview questions (focusing on statistics, R, Python, SQL), and behavioral interviews. Utilize the university''''s career services for mock interviews and resume reviews.
Tools & Resources
University Career Services, Online Interview Platforms (e.g., LeetCode, HackerRank), Books on Interview Puzzles, Company-specific Interview Prep Materials
Career Connection
Thorough preparation maximizes the chances of clearing competitive placement processes and securing coveted roles in top companies within the Indian job market.
Program Structure and Curriculum
Eligibility:
- Candidates with a B.A./B.Sc. Degree with Honours/Major in Statistics with at least 45% marks or an aggregate of 50% marks having Statistics as one of the subjects, or with a B.A./B.Sc. Degree with Mathematics (with Statistics as one of the subjects) with at least 50% of marks in aggregate and 50% of marks in Statistics in the Bachelor Degree Examination are eligible to apply. Relaxation of 5% marks will be given to candidates belonging to SC/ST/OBC/MOBC categories.
Duration: 4 semesters / 2 years
Credits: 96 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT 10100 | Statistical Methods I | Core | 4 | Probability Theory, Random Variables, Probability Distributions (Discrete & Continuous), Expectations and Moments, Limit Theorems |
| STAT 10200 | Sampling Theory | Core | 4 | Simple Random Sampling, Stratified Random Sampling, Systematic Sampling, Ratio and Regression Estimation, PPS and Cluster Sampling |
| STAT 10300 | Linear Algebra and Matrix Theory | Core | 4 | Vector Spaces and Subspaces, Linear Transformations, Matrices (Inverse, Rank, Determinants), Eigenvalues and Eigenvectors, Quadratic Forms, Generalized Inverse |
| STAT 10400 | Statistical Computing using R I | Core (Practical) | 4 | R Environment and Data Types, Data Structures (Vectors, Matrices, Data Frames), Basic Operations and Functions, Data Import/Export, Graphics in R |
| STAT 10500 | Statistical Inference I | Core | 4 | Point Estimation (Properties), Methods of Estimation (MLE, MOM), Interval Estimation, Testing of Hypotheses (Neyman-Pearson Lemma), Uniformly Most Powerful Tests |
| STAT 10600 | Skill Enhancement Course (SEC) | Skill Enhancement Course | 4 |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT 20100 | Statistical Methods II | Core | 4 | Categorical Data Analysis, Measures of Association, Generalized Linear Models (Logistic, Poisson Regression), Survival Analysis, Decision Trees |
| STAT 20200 | Design of Experiments | Core | 4 | Analysis of Variance (ANOVA), Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), Factorial Experiments (2^k) |
| STAT 20300 | Econometrics and Time Series Analysis | Core | 4 | Classical Linear Regression Model (CLRM), Assumptions and Violations (Heteroscedasticity, Autocorrelation), Multicollinearity, Time Series Components, ARIMA Models and Forecasting |
| STAT 20400 | Statistical Computing using R II | Core (Practical) | 4 | Advanced R Programming (Functions, Loops), Data Manipulation (dplyr, tidyr), Statistical Modeling in R, Simulation Techniques, Report Generation (RMarkdown) |
| STAT 20500 | Statistical Inference II | Core | 4 | Sufficiency and Completeness, Rao-Blackwell and Lehmann-Scheffe Theorems, Likelihood Ratio Tests, Bayesian Inference (Prior, Posterior, Predictive), Decision Theory (Loss Functions, Risk) |
| STAT 20600 | Skill Enhancement Course (SEC) | Skill Enhancement Course | 4 |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT 30100 | Multivariate Analysis | Core | 4 | Multivariate Normal Distribution, Hotelling''''s T-square Test, Multivariate Analysis of Variance (MANOVA), Principal Component Analysis, Factor Analysis, Discriminant Analysis, Cluster Analysis |
| STAT 30200 | Stochastic Processes | Core | 4 | Markov Chains (Discrete and Continuous Time), Classification of States, Poisson Process, Birth and Death Processes, Renewal Theory |
| STAT 30300 | Operations Research | Core | 4 | Linear Programming (Simplex Method, Duality), Transportation and Assignment Problems, Network Analysis (PERT/CPM), Queuing Theory (M/M/1, M/M/C), Inventory Control |
| STAT 30400 | Non-Parametric Inference | Core | 4 | Order Statistics, Sign Test, Wilcoxon Signed-Rank Test, Mann-Whitney U Test, Kruskal-Wallis Test, Friedman Test, Kolmogorov-Smirnov Test |
| STAT 30500 | Discipline Specific Elective (DSE) I | Elective | 4 | |
| STAT 30600 | Discipline Specific Elective (DSE) II | Elective | 4 | |
| STAT 30700 | Open Elective (OE) | Open Elective | 4 |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STAT 40100 | Advanced Topics in Statistics | Core | 4 | Generalized Additive Models, Bayesian Hierarchical Models, Spatial Statistics, Functional Data Analysis, Causal Inference |
| STAT 40200 | Discipline Specific Elective (DSE) III | Elective | 4 | |
| STAT 40300 | Discipline Specific Elective (DSE) IV | Elective | 4 | |
| STAT 40400 | Project | Project | 4 | Research Problem Formulation, Data Collection and Cleaning, Statistical Analysis and Modeling, Report Writing and Interpretation, Presentation and Viva-voce |
| STAT 40500 | Open Elective (OE) | Open Elective | 4 |
