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BACHELOR-OF-SCIENCE in Statistics at JSS College For Women
Mysuru, Karnataka
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About the Specialization
What is Statistics at JSS College For Women Mysuru?
This Statistics program at JSS College for Women, Mysuru, focuses on equipping students with robust analytical skills and quantitative reasoning essential for data-driven decision-making. In India, with the rapid growth of data science and analytics sectors, skilled statisticians are in high demand across various industries. This program differentiates itself by providing a strong theoretical foundation coupled with practical application, preparing students for diverse challenges in a rapidly evolving job market. The curriculum adheres to the National Education Policy 2020, ensuring contemporary relevance.
Who Should Apply?
This program is ideal for high school graduates (10+2 Science stream with Mathematics) possessing a keen interest in numbers, problem-solving, and interpreting complex data. It caters to aspiring data analysts, researchers, actuarial science enthusiasts, and anyone seeking a strong quantitative base for higher studies or entry-level positions in analytics. Students who thrive on logical reasoning and abstract concepts will find this specialization particularly engaging and rewarding.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding career paths in India as data analysts, business intelligence analysts, market researchers, junior statisticians, or actuarial assistants in sectors like IT, finance, healthcare, and government. Entry-level salaries typically range from INR 3-6 lakhs per annum, with significant growth potential up to INR 8-15 lakhs or more for experienced professionals in leading Indian companies. The curriculum also aligns with preparatory certifications like actuarial science exams, enhancing employability.
Student Success Practices
Foundation Stage
Master Core Statistical Concepts- (Semester 1-2)
Actively engage with Descriptive Statistics and Probability fundamentals. Utilize online platforms like NPTEL for supplementary lectures and practice problems to build a strong analytical base. This foundation is crucial for all advanced statistical applications and future career roles in data analysis.
Tools & Resources
NPTEL courses, Khan Academy, Textbooks, Study Groups
Career Connection
Develops foundational quantitative skills highly valued in entry-level data roles and sets the stage for advanced specialization.
Develop Programming Aptitude for Data- (Semester 1-2)
Focus on understanding basic programming logic through languages like Python or R, even if not explicitly taught early. Practice data handling and basic computations on platforms like HackerRank or Kaggle to prepare for data-intensive projects and future data science careers.
Tools & Resources
Python (NumPy, Pandas), R (dplyr, ggplot2), HackerRank, Kaggle
Career Connection
Essential for modern data analysis, improving efficiency and opening doors to data science and machine learning roles.
Cultivate Strong Study Habits and Peer Learning- (Semester 1-2)
Form study groups with peers to discuss complex topics, clarify doubts, and solve problems together. Regularly practice problems from textbooks and previous year question papers. This enhances academic performance and prepares for rigorous internal and external assessments, fostering collaborative skills.
Tools & Resources
Library resources, Previous year question papers, Peer study groups
Career Connection
Improves problem-solving skills, academic grades, and teamwork abilities, beneficial for group projects and workplace collaboration.
Intermediate Stage
Apply Statistical Methods Practically- (Semester 3-4)
Actively participate in labs focusing on correlation, regression, and distributions. Utilize statistical software like R, Python with libraries (pandas, numpy, scipy), or even advanced Excel for hands-on data analysis. This provides practical experience vital for internships and data analyst roles.
Tools & Resources
R Studio, Anaconda Python, Microsoft Excel, Real-world datasets
Career Connection
Translates theoretical knowledge into practical skills, making you job-ready for analytical roles and project-based work.
Explore Data Science and Analytics Fundamentals- (Semester 3-4)
Take advantage of Skill Enhancement Courses (SEC) like ''''Data Analysis Using R'''' or ''''Introduction to Data Science''''. Engage with online courses on platforms like Coursera or edX to grasp machine learning basics, enhancing your resume for emerging tech roles in India''''s booming data industry.
Tools & Resources
Coursera, edX, Udemy, LinkedIn Learning
Career Connection
Broadens career horizons into data science, artificial intelligence, and machine learning, highly sought after in the Indian tech market.
Build a Professional Network- (Semester 3-4)
Attend webinars, workshops, and seminars organized by the college or professional bodies (e.g., Indian Statistical Institute, Data Science India). Connect with faculty, seniors, and industry experts on LinkedIn to learn about career opportunities, internships, and industry trends specific to India.
Tools & Resources
LinkedIn, Industry conferences, College career fair
Career Connection
Opens doors to internships, mentorship, and job opportunities through referrals and industry insights.
Advanced Stage
Specialize and Undertake Capstone Projects- (Semester 5-6)
Focus on advanced topics like Statistical Inference, Design of Experiments, Time Series Analysis, and Quality Control. Undertake a capstone project that applies these concepts to real-world datasets, showcasing your problem-solving abilities and analytical prowess to potential employers or for academic research.
Tools & Resources
Advanced statistical software (SAS, SPSS), Industry case studies, Research papers
Career Connection
Demonstrates deep subject matter expertise and project management skills, crucial for specialist roles and higher academic pursuits.
Prepare Rigorously for Placements and Higher Studies- (Semester 5-6)
Actively participate in campus placement drives, practicing aptitude tests, technical interviews, and group discussions. For those aiming for higher studies in India or abroad, prepare for entrance exams like GATE Statistics, JNU, ISI admissions, or GRE/GMAT, focusing on quantitative sections.
Tools & Resources
Placement cell resources, Mock interview platforms, Competitive exam prep books
Career Connection
Maximizes chances of securing desirable placements in top companies or gaining admission to prestigious postgraduate programs.
Develop Effective Communication and Presentation Skills- (Semester 5-6)
Practice presenting complex statistical findings clearly and concisely, both verbally and through professional reports and visualizations. Join debate clubs or presentation workshops. This skill is critical for conveying insights in business settings and for securing roles in data consulting, research, or academia.
Tools & Resources
Presentation software (PowerPoint, Google Slides), Data visualization tools (Tableau, Power BI), Toastmasters International
Career Connection
Enhances your ability to influence decisions and effectively convey value in any professional setting, making you a more impactful professional.
Program Structure and Curriculum
Eligibility:
- Pass in PUC / 10 + 2 with Science stream (e.g., PCMB/PCM/PMS) with Mathematics as one of the subjects.
Duration: 3 Years / 6 Semesters
Credits: 120 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCT1 | Descriptive Statistics - I (Theory) | Core | 3 | Introduction to Statistics, Data Collection Methods, Measures of Central Tendency, Measures of Dispersion, Moments, Skewness and Kurtosis |
| DSCP1 | Descriptive Statistics - I (Practical) | Lab | 2 | Data organization and presentation, Calculation of Mean, Median, Mode, Calculation of Standard Deviation, Variance, Measures of Skewness and Kurtosis |
| MIN-DSC1-T1 | Minor Discipline 1 - Paper 1 (Theory) | Core | 3 | |
| MIN-DSC1-P1 | Minor Discipline 1 - Paper 1 (Practical) | Lab | 2 | |
| MIN-DSC2-T1 | Minor Discipline 2 - Paper 1 (Theory) | Core | 3 | |
| MIN-DSC2-P1 | Minor Discipline 2 - Paper 1 (Practical) | Lab | 2 | |
| AECC-ENG-1 | English - I | Ability Enhancement Compulsory Course | 2 | |
| AECC-MIL-1 | MIL/Indian Language - I | Ability Enhancement Compulsory Course | 2 | |
| VAC-1 | Value Added Course - I | Value Added Course | 1 | |
| SEC-1 | Skill Enhancement Course - I | Skill Enhancement Course | 2 |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCT2 | Probability and Probability Distributions - I (Theory) | Core | 3 | Random Experiments and Sample Space, Events and Probability Axioms, Conditional Probability and Bayes Theorem, Random Variables and their types, Expectation and Variance of Random Variables |
| DSCP2 | Probability and Probability Distributions - I (Practical) | Lab | 2 | Problems on Probability and Conditional Probability, Applications of Bayes Theorem, Calculation of Expectation and Variance, Drawing probability functions |
| MIN-DSC1-T2 | Minor Discipline 1 - Paper 2 (Theory) | Core | 3 | |
| MIN-DSC1-P2 | Minor Discipline 1 - Paper 2 (Practical) | Lab | 2 | |
| MIN-DSC2-T2 | Minor Discipline 2 - Paper 2 (Theory) | Core | 3 | |
| MIN-DSC2-P2 | Minor Discipline 2 - Paper 2 (Practical) | Lab | 2 | |
| AECC-ENG-2 | English - II | Ability Enhancement Compulsory Course | 2 | |
| AECC-ENV | Environmental Studies | Ability Enhancement Compulsory Course | 2 | |
| VAC-2 | Value Added Course - II | Value Added Course | 1 | |
| SEC-2 | Skill Enhancement Course - II | Skill Enhancement Course | 2 |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCT3 | Statistical Methods - I (Theory) | Core | 3 | Correlation Analysis (Karl Pearson, Spearman''''s), Simple Linear Regression, Multiple and Partial Regression, Curve Fitting (Least Squares), Measures of Association for Attributes |
| DSCP3 | Statistical Methods - I (Practical) | Lab | 2 | Problems on Correlation and Regression, Fitting of curves, Analysis of attributes, Applications of least squares method |
| MIN-DSC1-T3 | Minor Discipline 1 - Paper 3 (Theory) | Core | 3 | |
| MIN-DSC1-P3 | Minor Discipline 1 - Paper 3 (Practical) | Lab | 2 | |
| MIN-DSC2-T3 | Minor Discipline 2 - Paper 3 (Theory) | Core | 3 | |
| MIN-DSC2-P3 | Minor Discipline 2 - Paper 3 (Practical) | Lab | 2 | |
| OE-1 | Open Elective - I | Elective | 3 | |
| SEC-3 | Skill Enhancement Course - III | Skill Enhancement Course | 2 |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCT4 | Probability and Probability Distributions - II (Theory) | Core | 3 | Discrete Probability Distributions (Binomial, Poisson, Geometric), Hypergeometric and Negative Binomial Distributions, Continuous Probability Distributions (Uniform, Exponential), Normal Distribution: Properties and Applications, Central Limit Theorem (introduction) |
| DSCP4 | Probability and Probability Distributions - II (Practical) | Lab | 2 | Problems on Binomial, Poisson, Normal distributions, Fitting of various probability distributions, Computation of probabilities using distribution tables |
| MIN-DSC1-T4 | Minor Discipline 1 - Paper 4 (Theory) | Core | 3 | |
| MIN-DSC1-P4 | Minor Discipline 1 - Paper 4 (Practical) | Lab | 2 | |
| MIN-DSC2-T4 | Minor Discipline 2 - Paper 4 (Theory) | Core | 3 | |
| MIN-DSC2-P4 | Minor Discipline 2 - Paper 4 (Practical) | Lab | 2 | |
| OE-2 | Open Elective - II | Elective | 3 | |
| SEC-4 | Skill Enhancement Course - IV | Skill Enhancement Course | 2 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCT5 | Statistical Inference - I (Theory) | Core | 3 | Population and Sample, Sampling Distributions, Central Limit Theorem and Law of Large Numbers, Point Estimation (Properties, Methods), Interval Estimation (Confidence Intervals), Fundamentals of Hypothesis Testing (Type I & II Errors) |
| DSCP5 | Statistical Inference - I (Practical) | Lab | 2 | Problems on Point and Interval Estimation, Large sample tests (Z-tests for mean, proportion, difference), Construction of confidence intervals |
| DSCT6 | Design of Experiments (Theory) | Core | 3 | Basic principles of Experimental Design, Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), Factorial Experiments (introduction) |
| DSCP6 | Design of Experiments (Practical) | Lab | 2 | Analysis of Variance (ANOVA) for CRD, ANOVA for RBD and LSD, Hypothesis testing in experimental designs, Interpretation of experimental results |
| DSET1 | Time Series Analysis (Theory) | Elective | 3 | Components of Time Series (Trend, Seasonality, Cyclical, Irregular), Measurement of Trend (Moving Average, Least Squares), Measurement of Seasonal Variation, Forecasting methods (Exponential Smoothing), Applications in business and economics |
| DSEP1 | Time Series Analysis (Practical) | Lab | 2 | Computation of Trend, Seasonal, Cyclical variations, Forecasting using various methods, Interpretation of time series data |
| OE-3 | Open Elective - III | Elective | 3 |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSCT7 | Statistical Inference - II (Theory) | Core | 3 | Small Sample Tests (t-test for mean, paired t-test), Chi-square tests (Goodness of Fit, Independence of Attributes), F-test for equality of variances, Non-parametric tests (Sign test, Wilcoxon, Mann-Whitney), Analysis of Variance for one-way and two-way classifications |
| DSCP7 | Statistical Inference - II (Practical) | Lab | 2 | Problems on t, Chi-square, F-tests, Application of Non-parametric tests, ANOVA computations and interpretation |
| DSCT8 | Vital Statistics and Demography (Theory) | Core | 3 | Sources of demographic data, Measures of Mortality (CDR, SDR, IMR), Measures of Fertility (CBR, GFR, TFR), Reproduction Rates (GRR, NRR), Construction and Uses of Life Tables |
| DSCP8 | Vital Statistics and Demography (Practical) | Lab | 2 | Problems on various mortality and fertility rates, Construction of Life Tables, Population projection methods |
| DSET2 | Statistical Quality Control (Theory) | Elective | 3 | Introduction to Quality Control, Control Charts for Variables (X-bar, R, Sigma charts), Control Charts for Attributes (p, np, c, u charts), Acceptance Sampling (Single, Double Sampling Plans), Operating Characteristic (OC) Curve |
| DSEP2 | Statistical Quality Control (Practical) | Lab | 2 | Construction of various control charts for variables and attributes, Drawing OC curves, Implementation of acceptance sampling plans |
| OE-4 | Open Elective - IV | Elective | 3 |
