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B-SC in Statistics at Shri Shivaji Science College (Autonomous)
Amravati, Maharashtra
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About the Specialization
What is Statistics at Shri Shivaji Science College (Autonomous) Amravati?
This B.Sc Statistics program at Shri Shivaji Science College, Amravati focuses on developing a strong foundation in statistical theory, methods, and applications. The curriculum, prescribed by Sant Gadge Baba Amravati University, emphasizes quantitative reasoning and data analysis skills highly demanded in various Indian industries. It prepares students for a dynamic career in data-driven fields, making them proficient in handling, analyzing, and interpreting complex datasets.
Who Should Apply?
This program is ideal for 10+2 graduates with a keen interest in mathematics and data, aspiring to careers in analytics, research, or finance. It suits individuals who enjoy problem-solving using quantitative techniques and seek a robust academic foundation. Career changers looking to transition into data science or working professionals aiming to upskill in statistical methodologies will also find immense value in this comprehensive program.
Why Choose This Course?
Graduates of this program can expect diverse career paths as data analysts, statisticians, research assistants, and actuarial professionals in India. Entry-level salaries typically range from INR 3-5 LPA, growing significantly with experience to INR 8-15 LPA. This degree provides a strong base for postgraduate studies like M.Sc. Statistics or Data Science, leading to advanced roles in Indian and international organizations.
Student Success Practices
Foundation Stage
Master Core Statistical Concepts- (Semester 1-2)
Focus on understanding fundamental concepts of descriptive statistics, probability theory, and basic distributions. Regular practice of numerical problems and derivation of formulae is crucial. Attend all lectures and practicals diligently to build a strong base for advanced topics.
Tools & Resources
NCERT Mathematics books (XI, XII), Introduction to Statistical Theory by Sher Muhammad Chaudhry, Khan Academy for Probability and Statistics, MS Excel for basic data handling
Career Connection
A solid foundation is essential for all future statistical applications and forms the bedrock for entry-level data analysis roles.
Develop Strong Mathematical Acumen- (Semester 1-2)
Statistics relies heavily on mathematical concepts. Dedicate time to strengthen calculus, algebra, and discrete mathematics skills. Solve a variety of problems daily from textbooks and online resources to enhance problem-solving speed and accuracy.
Tools & Resources
R.D. Sharma Mathematics, Online platforms like Byju''''s, Unacademy for math refreshers, Peer study groups for collaborative learning
Career Connection
Mathematical proficiency is critical for understanding advanced statistical models, inferential techniques, and excelling in quantitative roles.
Engage in Practical Application- (Semester 1-2)
Actively participate in all practical sessions and focus on hands-on data analysis. Learn to use basic statistical software or tools available in the college lab. Practice creating graphs, tables, and performing basic computations. Understand the ''''why'''' behind each statistical method.
Tools & Resources
MS Excel, Scientific Calculator, Basic Statistical Software (e.g., MiniTab trials or R-Studio basic intro if available)
Career Connection
Practical skills in data manipulation and visualization are directly transferable to roles like Junior Data Analyst or Research Assistant.
Intermediate Stage
Deep Dive into Statistical Inference- (Semester 3-5)
Focus intently on hypothesis testing, estimation, and understanding different probability distributions. Work through numerous examples to grasp the nuances of various tests and their assumptions. Participate in workshops or online courses to supplement classroom learning.
Tools & Resources
Statistical Inference by George Casella and Roger L. Berger, NPTEL courses on Probability and Statistics, R programming basics for statistical tests
Career Connection
Mastery of inference is crucial for roles involving research, quality control, and data-driven decision-making in industries like finance and healthcare.
Build Programming Skills for Data- (Semester 3-5)
Start learning a programming language widely used in data science, such as R or Python. Focus on libraries like ''''dplyr'''' and ''''ggplot2'''' in R or ''''Pandas'''' and ''''Matplotlib'''' in Python. Practice data cleaning, manipulation, and basic statistical analysis using these tools.
Tools & Resources
Coursera/edX introductory courses on R/Python for Data Science, Hackerrank/LeetCode for coding practice, Kaggle datasets for practice projects
Career Connection
Proficiency in R/Python makes you highly competitive for roles in data analytics, machine learning, and business intelligence in Indian tech companies and startups.
Explore Real-world Datasets and Projects- (Semester 3-5)
Seek opportunities to work on small data projects, perhaps for local businesses or college departments. Apply learned statistical methods to real-world datasets. This practical experience is invaluable for understanding challenges and building a portfolio.
Tools & Resources
Kaggle, UCI Machine Learning Repository for datasets, LinkedIn for connecting with local startups for mini-projects, College research projects and faculty guidance
Career Connection
A project portfolio demonstrates practical application, which is a key differentiator in campus placements and job interviews for analytics positions.
Advanced Stage
Specialize and Gain Advanced Skills- (Semester 6)
Identify an area of interest within statistics, such as econometrics, biostatistics, or machine learning. Take advanced online courses or pursue self-study in these areas. Focus on advanced topics like multivariate analysis, time series, or specific experimental designs.
Tools & Resources
Advanced textbooks in chosen specialization, Udemy/Coursera specializations, Research papers and academic journals
Career Connection
Specialized skills open doors to niche roles, higher salaries, and advanced research opportunities in India''''s growing data-driven sectors.
Undertake an Internship or Major Project- (Semester 6)
Secure an internship at a company, research institution, or a local NGO to apply your statistical knowledge in a professional setting. Alternatively, work on a comprehensive final-year project that solves a complex data problem, demonstrating independent research and analytical capabilities.
Tools & Resources
Internshala, LinkedIn for internship searches, Faculty mentorship for project guidance, GitHub for showcasing project work
Career Connection
Internships provide invaluable industry exposure, networking opportunities, and often lead to pre-placement offers. A strong project enhances your resume significantly.
Prepare for Placements and Higher Studies- (Semester 6)
Actively prepare for campus placements by honing interview skills, mock tests, and resume building. If aspiring for higher education, prepare for entrance exams like GATE Statistics, or pursue applications for M.Sc. programs in India or abroad. Network with alumni for career insights.
Tools & Resources
College placement cell resources, Online aptitude test platforms, GRE/CAT/GATE preparation materials, Alumni network platforms and professional communities
Career Connection
Proactive preparation ensures smooth transition into desired career paths or secures admission into prestigious postgraduate programs, leading to long-term professional growth.
Program Structure and Curriculum
Eligibility:
- Candidate must have passed 10+2 (HSC) examination with Mathematics/Statistics as one of the subjects from a recognized board, as per Sant Gadge Baba Amravati University norms for B.Sc. programs.
Duration: 3 years (6 semesters)
Credits: 64 Credits
Assessment: Internal: 20%, External: 80%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BST-101 | Descriptive Statistics-I | Core Theory | 4 | Data presentation and tabulation, Measures of central tendency, Measures of dispersion, Moments, Skewness, and Kurtosis, Principle of Least Squares |
| BST-102 | Probability Theory-I | Core Theory | 4 | Sample Space and Events, Axiomatic Definition of Probability, Conditional Probability and Independence, Bayes'''' Theorem, Random Variables and their distributions |
| BSTP-101 | Statistics Practical - I | Core Practical | 2 | Data tabulation and classification, Diagrammatic and graphical representation, Computation of central tendency measures, Computation of dispersion measures, Moments, skewness and kurtosis calculations |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BST-201 | Descriptive Statistics-II | Core Theory | 4 | Bivariate data and correlation analysis, Linear regression techniques, Attributes and their association, Ratios, Rates, and Population estimation, Index Numbers (construction and types) |
| BST-202 | Probability Theory-II | Core Theory | 4 | Mathematical Expectation and its properties, Generating functions (MGF, PGF), Standard discrete probability distributions, Standard continuous probability distributions, Central Limit Theorem |
| BSTP-201 | Statistics Practical - II | Core Practical | 2 | Computation of correlation coefficient, Fitting of regression lines, Analysis of attributes, Calculation of ratios and rates, Construction of index numbers |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BST-301 | Statistical Methods - I | Core Theory | 4 | Hypothesis testing (basic concepts), Large Sample Tests (Z-tests), Chi-Square tests (goodness of fit, independence), Student''''s t-tests (single mean, two means, paired), F-test for equality of variances |
| BST-302 | Probability Distributions - I | Core Theory | 4 | Negative Binomial Distribution, Geometric and Hypergeometric Distribution, Rectangular (Uniform) Distribution, Exponential and Gamma Distributions, Weibull Distribution |
| BSTP-301 | Statistics Practical - III | Core Practical | 2 | Implementation of large sample tests, Performing Chi-square tests, Application of t-tests, Fitting of standard probability distributions, Tests of significance using F-distribution |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BST-401 | Statistical Methods - II | Core Theory | 4 | Analysis of Variance (ANOVA - One Way, Two Way), Non-parametric tests (Sign, Wilcoxon, Mann-Whitney), Kruskal-Wallis Test and Run Test, Statistical Quality Control (Control Charts for Variables and Attributes), Acceptance Sampling |
| BST-402 | Probability Distributions - II | Core Theory | 4 | Beta Distributions (Type I and II), Cauchy Distribution, Bivariate Normal Distribution, Order Statistics and their distributions, Joint and Marginal Distributions |
| BSTP-401 | Statistics Practical - IV | Core Practical | 2 | ANOVA calculations and interpretation, Application of various non-parametric tests, Construction and interpretation of control charts, Problem-solving related to different probability distributions, Testing of hypotheses for small samples |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BST-501 | Sampling Techniques & Design of Experiments - I | Core Theory | 4 | Census vs Sample Survey, Simple Random Sampling (SRS), Stratified Random Sampling, Systematic Sampling, Completely Randomized Design (CRD) |
| BST-502 | Statistical Inference - I | Core Theory | 4 | Point Estimation and properties of estimators, Methods of Estimation (MLE, Method of Moments), Interval Estimation (confidence intervals for mean, variance, proportion), Testing of Hypotheses (fundamental concepts, types of errors), Sufficiency and Completeness |
| BSTP-501 | Statistics Practical - V | Core Practical | 2 | Problems on Simple Random Sampling, Problems on Stratified Random Sampling, Problems on Systematic Sampling, Analysis of Completely Randomized Design, Estimation of population parameters |
| BSTP-502 | Statistics Practical - VI | Core Practical | 2 | Applications of Method of Moments, Applications of Maximum Likelihood Estimation, Construction of confidence intervals, Hypothesis testing for various parameters, Comparison of estimators |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BST-601 | Sampling Techniques & Design of Experiments - II | Core Theory | 4 | Cluster Sampling and Two-Stage Sampling, Randomized Block Design (RBD), Latin Square Design (LSD), Factorial Experiments (2^2, 2^3), Analysis of Covariance (ANOVA with one concomitant variable) |
| BST-602 | Statistical Inference - II | Core Theory | 4 | Rao-Blackwell Theorem and Cramer-Rao Inequality, Neyman-Pearson Lemma, Uniformly Most Powerful (UMP) Tests, Likelihood Ratio Test, Sequential Probability Ratio Test (SPRT) |
| BSTP-601 | Statistics Practical - VII | Core Practical | 2 | Analysis of Randomized Block Design, Analysis of Latin Square Design, Analysis of Factorial Experiments, Design of experiments practicals, Comparison of various sampling methods |
| BSTP-602 | Statistics Practical - VIII | Core Practical | 2 | Problems on sufficiency and completeness, Application of Cramer-Rao Inequality, Implementation of Neyman-Pearson Lemma, Likelihood Ratio Tests practicals, Sequential Probability Ratio Tests practicals |
