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BSC-HONOURS in Statistics at Gokhale Memorial Girls' College
Kolkata, West Bengal
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About the Specialization
What is Statistics at Gokhale Memorial Girls' College Kolkata?
This BSc Honours Statistics program at Gokhale Memorial Girls'''' College focuses on building a strong foundation in statistical theory, methods, and their practical applications. Designed under the CBCS framework of the University of Calcutta, it equips students with analytical and computational skills crucial for modern data-driven decision-making. The curriculum is tailored to meet the growing demand for skilled statisticians in various Indian sectors, offering a blend of theoretical rigor and hands-on experience.
Who Should Apply?
This program is ideal for high school graduates with a keen interest in mathematics and data analysis, aspiring to build careers in analytics, research, or academia. It suits those who enjoy problem-solving, critical thinking, and working with numbers to extract insights. Individuals looking to pursue postgraduate studies in Statistics, Data Science, or related quantitative fields will find the robust theoretical base invaluable.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including roles as Data Analysts, Statisticians, Business Intelligence Analysts, or Research Associates in industries like finance, healthcare, and IT. Entry-level salaries typically range from INR 3-5 LPA, with experienced professionals earning significantly more. The strong foundation also prepares students for competitive exams for government statistical services or higher education.
Student Success Practices
Foundation Stage
Master Core Concepts with Regular Practice- (Semester 1-2)
Focus intensely on understanding fundamental concepts of probability, statistical methods, and distributions. Regularly solve textbook problems and examples. Use online resources like Khan Academy or NPTEL for supplementary explanations. This solid conceptual grounding is vital for advanced topics and crucial for entrance exams for higher studies.
Tools & Resources
Textbooks, NPTEL courses, Khan Academy, Problem sets, solved examples
Career Connection
Strong conceptual foundation is essential for succeeding in advanced statistics courses and for analytical roles requiring fundamental statistical understanding.
Develop Programming Skills Early (R/Python)- (Semester 1-2)
Begin hands-on practice with R or Python as early as Semester 1, even before it''''s formally introduced. Utilize free online tutorials and work on small data manipulation and visualization projects. Early proficiency in these languages is a significant advantage for internships and future data roles in India''''s tech sector.
Tools & Resources
DataCamp (free modules), Coursera (free courses), GeeksforGeeks, Kaggle for beginner datasets
Career Connection
Early coding skills are highly valued for data analyst, business intelligence, and junior data science positions.
Engage in Peer Study Groups & Discussions- (Semester 1-2)
Form study groups with classmates to discuss challenging topics, solve problems together, and prepare for internal assessments. Teaching concepts to peers helps solidify your own understanding. Participate actively in departmental seminars or workshops to broaden your academic perspective and build a supportive network.
Tools & Resources
College library, common study areas, Departmental events, online collaborative tools
Career Connection
Enhances critical thinking, communication skills, and builds a professional network valuable for future collaborations and referrals.
Intermediate Stage
Apply Statistical Software to Real-World Data- (Semester 3-4)
Move beyond theoretical problems by applying statistical techniques (inference, sampling, linear models) to real datasets. Download open datasets from Kaggle, UCI Machine Learning Repository, or government portals. This practical application deepens understanding and builds a portfolio for future employers.
Tools & Resources
RStudio, Jupyter Notebook (Python), Kaggle, data.gov.in, UCI Machine Learning Repository
Career Connection
Develops practical skills highly sought after by Indian companies for roles involving data analysis and modeling.
Seek Internships or Mini-Projects- (Semester 3-4)
Actively look for short-term internships or volunteer for data-related mini-projects during semester breaks. Even unpaid experiences provide invaluable industry exposure and practical problem-solving skills. Connect with college seniors and alumni for guidance on opportunities in Kolkata''''s growing analytics scene.
Tools & Resources
LinkedIn, Internshala, college placement cell, Alumni network, industry contacts
Career Connection
Gains hands-on experience, builds resume, and often leads to pre-placement offers or valuable networking for future job searches.
Participate in Data Competitions & Workshops- (Semester 3-4)
Engage in online data analysis competitions or attend workshops on specialized topics like machine learning or econometrics. These experiences enhance problem-solving abilities, introduce new tools, and provide networking opportunities with industry professionals and potential mentors.
Tools & Resources
HackerEarth, Analytics Vidhya, DataHack, Industry-sponsored workshops, university seminars
Career Connection
Showcases initiative, practical skills, and competitiveness, making you a more attractive candidate for recruiters.
Advanced Stage
Focus on Specialization & Project Excellence- (Semester 5-6)
Deep dive into your chosen DSE subjects (e.g., Econometrics, Operations Research, Actuarial Statistics) and integrate this knowledge into your Semester 6 project. Aim to produce a high-quality project report that showcases strong analytical and presentation skills, which is a key differentiator in placement interviews.
Tools & Resources
Specialized software (e.g., EViews for Econometrics, specific OR solvers), Academic journals, research papers
Career Connection
A well-executed project demonstrates expertise and can be a strong talking point in interviews, securing roles in specialized statistical domains.
Intensive Placement Preparation & Mock Interviews- (Semester 5-6)
By Semester 6, begin focused preparation for placements. Polish your resume, practice coding challenges (if targeting data science roles), and participate in mock interviews. Focus on articulating your statistical knowledge and project experiences clearly to potential employers, highlighting your problem-solving approach for Indian hiring contexts.
Tools & Resources
Online interview platforms (LeetCode, HackerRank), College placement cell, career counseling services
Career Connection
Crucial for securing placements in leading Indian companies, optimizing your chances for desired roles and salary packages.
Network Strategically & Explore Higher Education- (Semester 5-6)
Actively connect with professionals in your areas of interest through LinkedIn and college alumni networks. Attend career fairs. Simultaneously, research postgraduate programs (MSc Statistics, Data Science, MBA with Analytics) if higher education is a goal, preparing for entrance exams like ISI, IIT JAM, or CAT for management programs.
Tools & Resources
LinkedIn, alumni mentorship programs, University websites, coaching institutes for entrance exams
Career Connection
Opens doors to advanced career opportunities, leadership roles, or specialized academic pursuits, fostering long-term professional growth.
Program Structure and Curriculum
Eligibility:
- Minimum 50% in aggregate and 45% in Statistics (or Mathematics/Business Mathematics/Computer Science/Economics) in the 10+2 level. Or Minimum 55% marks in Statistics/Mathematics/Business Mathematics/Computer Science/Economics in 10+2 level.
Duration: 6 semesters / 3 years
Credits: 140 Credits
Assessment: Internal: For Core/DSE papers: Internal Assessment (IA) 10 marks, Attendance 10 marks (out of 70 total for theory component). For AECC/SEC papers: IA 20 marks (out of 100 total)., External: For Core/DSE papers: End-Semester Examination (ESE) Theory 50 marks, ESE Practical 30 marks. For AECC/SEC papers: ESE Theory 80 marks.
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STSA-CC1-1-TH | Statistical Methods I (Theory) | Core (Theory) | 4 | Descriptive Statistics, Probability Theory, Random Variable, Probability Distributions (Binomial, Poisson, Normal), Correlation, Regression |
| STSA-CC1-1-PR | Statistical Methods I (Practical) | Core (Practical) | 2 | Data tabulation and graphical representation, Measures of central tendency and dispersion, Moments, skewness, kurtosis, Correlation and Regression analysis |
| STSA-CC2-1-TH | Introductory Probability (Theory) | Core (Theory) | 4 | Random Experiment, Sample Space, Events, Probability (Classical, Statistical), Conditional Probability, Bayes'''' Theorem, Random Variables, Expectation, Variance, Moment Generating Functions |
| STSA-CC2-1-PR | Introductory Probability (Practical) | Core (Practical) | 2 | Calculations of expectation and variance, Fitting of Binomial and Poisson distributions, Problems on normal distribution, Simulating random variables |
| GE1 | Generic Elective 1 | Elective | 6 | |
| AECC1 | Environmental Studies | AECC | 2 | Environment and Ecosystem, Natural Resources, Biodiversity and Conservation, Environmental Pollution, Social Issues and the Environment |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STSA-CC3-2-TH | Statistical Methods II (Theory) | Core (Theory) | 4 | Theory of Attributes, Bivariate Data Analysis, Curve Fitting (Principle of Least Squares), Index Numbers (Construction and Testing), Time Series Analysis (Components, Measurement) |
| STSA-CC3-2-PR | Statistical Methods II (Practical) | Core (Practical) | 2 | Analysis of attributes, Bivariate frequency distribution, Fitting of polynomials and exponential curves, Construction of various index numbers, Measurement of time series components |
| STSA-CC4-2-TH | Statistical Computing & Programming (Theory) | Core (Theory) | 4 | Introduction to Statistical Software (R/Python), Data Structures (vectors, matrices, data frames), Control Structures (loops, conditionals), Functions and Scripting, Data Input/Output, Basic Graphics |
| STSA-CC4-2-PR | Statistical Computing & Programming (Practical) | Core (Practical) | 2 | R/Python programming for descriptive statistics, Generating random samples from distributions, Data manipulation and basic visualization, Importing and exporting data sets |
| GE2 | Generic Elective 2 | Elective | 6 | |
| AECC2 | English Communication | AECC | 2 | Theories and Types of Communication, Verbal and Non-verbal Communication, Listening Skills, Speaking Skills, Writing Skills (reports, letters, emails), Presentation Strategies |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STSA-CC5-3-TH | Statistical Inference I (Theory) | Core (Theory) | 4 | Sampling Distributions (t, Chi-square, F), Point Estimation (Methods of Estimation), Properties of Estimators (Unbiasedness, Consistency), Cramer-Rao Lower Bound, Interval Estimation |
| STSA-CC5-3-PR | Statistical Inference I (Practical) | Core (Practical) | 2 | Confidence intervals for mean, variance, proportions, Using Normal, t, Chi-square distributions for interval estimation, Derivation of ML and Moment estimators |
| STSA-CC6-3-TH | Survey Sampling (Theory) | Core (Theory) | 4 | Census vs. Sample Survey, Sampling and Non-sampling Errors, Simple Random Sampling (SRS), Stratified Random Sampling, Systematic Sampling, Ratio and Regression Estimators |
| STSA-CC6-3-PR | Survey Sampling (Practical) | Core (Practical) | 2 | Estimation in SRSWR and SRSWOR, Estimation in stratified random sampling, Comparison of sampling schemes, Estimation using ratio and regression methods |
| STSA-CC7-3-TH | Applied Statistics (Theory) | Core (Theory) | 4 | Demographic Methods (Rates, Ratios, Sources of Data), Life Tables (Construction and Uses), Economic Statistics (National Income, Business Cycles), Statistical Quality Control (Control Charts for Variables and Attributes), Official Statistics in India |
| STSA-CC7-3-PR | Applied Statistics (Practical) | Core (Practical) | 2 | Calculation of demographic measures, Construction of life tables, Construction and interpretation of control charts, Calculation of national income aggregates |
| GE3 | Generic Elective 3 | Elective | 6 | |
| STSA-SEC-A-3-TH | Data Analysis using Software | Skill Enhancement Course (SEC) | 2 | Data entry and cleaning, Descriptive statistics using software (e.g., SPSS/R/Python), Correlation and Regression analysis, Hypothesis testing using statistical software, Generating basic reports |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STSA-CC8-4-TH | Statistical Inference II (Theory) | Core (Theory) | 4 | Hypothesis Testing (Null and Alternative Hypotheses), Type I and Type II Errors, Power of a Test, Neyman-Pearson Lemma, Likelihood Ratio Test, Chi-square tests (Goodness of Fit, Independence of Attributes) |
| STSA-CC8-4-PR | Statistical Inference II (Practical) | Core (Practical) | 2 | Tests for mean, variance, proportions (large and small samples), Paired and Unpaired t-tests, Chi-square tests for independence and goodness of fit, ANOVA for one-way classification |
| STSA-CC9-4-TH | Linear Models (Theory) | Core (Theory) | 4 | Simple Linear Regression Model, Multiple Linear Regression Model, Estimation of Parameters (Least Squares), Hypothesis Testing in Linear Models, Analysis of Variance (ANOVA) for Regression |
| STSA-CC9-4-PR | Linear Models (Practical) | Core (Practical) | 2 | Fitting simple and multiple linear regression models, Testing significance of regression coefficients, ANOVA table for linear models, Residual analysis |
| STSA-CC10-4-TH | Design of Experiments (Theory) | Core (Theory) | 4 | Principles of Experimentation (Randomization, Replication, Local Control), Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), Factorial Experiments (2^2, 2^3) |
| STSA-CC10-4-PR | Design of Experiments (Practical) | Core (Practical) | 2 | Analysis of CRD, RBD, LSD, Analysis of 2^2 and 2^3 factorial experiments, Missing plot techniques |
| GE4 | Generic Elective 4 | Elective | 6 | |
| STSA-SEC-A-4-TH | Statistical Data Analysis using R/Python | Skill Enhancement Course (SEC) | 2 | Advanced data manipulation and transformation in R/Python, Statistical graphics and visualization, Implementation of basic inferential statistics, Regression and ANOVA using R/Python, Automated report generation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STSA-CC11-5-TH | Multivariate Analysis (Theory) | Core (Theory) | 4 | Multivariate Normal Distribution, Wishart Distribution, Hotelling''''s T-square statistic, Multivariate Analysis of Variance (MANOVA), Principal Component Analysis (PCA), Factor Analysis |
| STSA-CC11-5-PR | Multivariate Analysis (Practical) | Core (Practical) | 2 | Computation of sample mean vector and covariance matrix, Hotelling''''s T-square test implementation, Performing PCA and interpreting results using software, Cluster analysis and discriminant analysis |
| STSA-CC12-5-TH | Stochastic Processes (Theory) | Core (Theory) | 4 | Introduction to Stochastic Processes, Random Walks, Markov Chains (Discrete Time, Continuous Time), Poisson Processes, Birth and Death Processes, Branching Processes |
| STSA-CC12-5-PR | Stochastic Processes (Practical) | Core (Practical) | 2 | Simulation of random walks, Simulation of Markov chains, Estimation of parameters for Poisson process, Numerical problems on stochastic processes |
| STSA-DSE-A-5-TH | Econometrics (Theory) | Discipline Specific Elective (Theory) | 4 | Classical Linear Regression Model (CLRM), Violation of CLRM Assumptions (Multicollinearity), Heteroscedasticity, Autocorrelation, Dummy Variable Regression Models, Introduction to Time Series Econometrics |
| STSA-DSE-A-5-PR | Econometrics (Practical) | Discipline Specific Elective (Practical) | 2 | Estimation of regression models using software, Testing for multicollinearity, heteroscedasticity, autocorrelation, Applying dummy variables in regression, Forecasting using econometric models |
| STSA-DSE-B-5-TH | Operations Research (Theory) | Discipline Specific Elective (Theory) | 4 | Linear Programming Problems (LPP), Graphical and Simplex Methods, Duality in LPP, Transportation Problem, Assignment Problem, Game Theory |
| STSA-DSE-B-5-PR | Operations Research (Practical) | Discipline Specific Elective (Practical) | 2 | Solving LPP using graphical and simplex methods, Solving transportation problems, Solving assignment problems, Game theory problems (matrix method, graphical method) |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| STSA-CC13-6-TH | Statistical Computing with R/Python (Theory) | Core (Theory) | 4 | Advanced R/Python Programming, Data Visualization techniques, Statistical modeling using R/Python, Introduction to Machine Learning algorithms (Regression, Classification), Web Scraping and API interaction |
| STSA-CC13-6-PR | Statistical Computing with R/Python (Practical) | Core (Practical) | 2 | Advanced programming exercises in R/Python, Creating complex data visualizations, Implementing statistical models (GLMs), Applying basic machine learning algorithms, Working with larger datasets and optimizing code |
| STSA-CC14-6-PR | Project | Core (Project) | 6 | Problem definition and literature review, Data collection and preparation, Application of statistical methods and analysis, Report writing and presentation of findings, Interpretation of results and conclusion |
| STSA-DSE-C-6-TH | Statistical Quality Control (Theory) | Discipline Specific Elective (Theory) | 4 | Introduction to Quality Control, Control Charts for Variables (X-bar, R, s charts), Control Charts for Attributes (p, np, c, u charts), Acceptance Sampling (Single, Double, Multiple), Process Capability Analysis |
| STSA-DSE-C-6-PR | Statistical Quality Control (Practical) | Discipline Specific Elective (Practical) | 2 | Construction and interpretation of various control charts, Designing acceptance sampling plans, Calculating Operating Characteristic (OC) curves, Analyzing process capability |
| STSA-DSE-D-6-TH | Actuarial Statistics (Theory) | Discipline Specific Elective (Theory) | 4 | Insurance Principles and Risk Theory, Mortality Tables (Construction and Uses), Life Contingencies (Life Annuities, Assurances), Premiums and Policy Values, Net Single and Annual Premiums |
| STSA-DSE-D-6-PR | Actuarial Statistics (Practical) | Discipline Specific Elective (Practical) | 2 | Calculations involving mortality rates and probabilities, Determining present values of life annuities, Calculation of life insurance premiums, Analysis of various assurance contracts |
