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B-SC-LIBERAL-ARTS-HONS-SSLA in Statistics at Symbiosis International University (SIU)

Symbiosis International University, Pune, established in 1971, is a premier UGC-recognized Deemed University with an A++ NAAC grade. Spanning over 400 acres, it offers over 60 diverse UG, PG, and doctoral programs. Known for academic excellence and global recognition, it consistently ranks high in NIRF and boasts strong placements.

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Pune, Maharashtra

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About the Specialization

What is Statistics at Symbiosis International University (SIU) Pune?

This Statistics specialization program at Symbiosis School for Liberal Arts (SSLA) focuses on developing strong analytical and quantitative skills crucial for data-driven decision making. It offers a comprehensive understanding of statistical theories, methods, and their practical applications, preparing students for diverse roles in India''''s rapidly growing data science and analytics industry. The program emphasizes a blend of theoretical rigor and hands-on computational skills.

Who Should Apply?

This program is ideal for students with a strong aptitude for mathematics and logical reasoning, seeking entry into analytical roles. It also suits fresh graduates interested in data science, actuarial science, market research, or quantitative finance. Individuals aiming to pursue higher studies in statistics or related fields in India or abroad will find this foundation highly beneficial.

Why Choose This Course?

Graduates of this program can expect diverse career paths in India as data analysts, statisticians, business intelligence specialists, or research associates across various sectors like IT, finance, healthcare, and e-commerce. Entry-level salaries typically range from INR 3.5-6 LPA, with experienced professionals earning significantly more. The program aligns with skills required for certifications like SAS or R-based data analytics.

Student Success Practices

Foundation Stage

Build a Strong Mathematical Foundation- (Semester 1-2)

Dedicate time to master core mathematical concepts, especially calculus, linear algebra, and basic probability. These are fundamental to understanding advanced statistics. Actively solve problems from textbooks and online resources like Khan Academy.

Tools & Resources

NCERT Mathematics books (Classes XI-XII), Khan Academy (Calculus, Linear Algebra), Practice problem sets

Career Connection

A robust mathematical background is essential for grasping statistical models and algorithms, which directly impacts your ability to perform complex data analysis in future roles.

Develop Foundational Programming Skills in Python/R- (Semester 1-2)

Start learning basic programming concepts and syntax in Python or R early on, even before formal courses begin. Focus on data structures, control flows, and basic data manipulation. Online tutorials and coding platforms are excellent starting points.

Tools & Resources

Codecademy (Python/R), DataCamp (Introduction to R/Python), HackerRank (basic coding challenges)

Career Connection

Proficiency in at least one statistical programming language is non-negotiable for data-related careers. Early adoption gives you a significant edge in practical assignments and internships.

Engage Actively in Data Analysis Projects- (Semester 1-2)

Participate in small-scale data analysis projects, even if they are self-initiated or part of college clubs. This helps apply theoretical knowledge, understand data cleaning, and visualize findings. Seek feedback from professors or seniors.

Tools & Resources

Kaggle (beginner datasets), Excel for basic analysis, Google Sheets

Career Connection

Practical project experience showcases your ability to work with real data, which is highly valued by recruiters for internships and entry-level positions.

Intermediate Stage

Deep Dive into Statistical Software and Libraries- (Semester 3-5)

Beyond basic programming, thoroughly learn to use statistical libraries in Python (e.g., NumPy, Pandas, SciPy, Scikit-learn) and R (e.g., Tidyverse, base R for statistics). Understand how to implement regression, time series, and other models.

Tools & Resources

Official documentation (Pandas, Scikit-learn), Coursera/Udemy courses on R/Python for Data Science, Stack Overflow

Career Connection

Mastery of these tools is critical for quantitative analysis roles. Employers look for candidates who can efficiently work with large datasets and build complex models.

Seek Industry Internships and Workshops- (Semester 3-5)

Actively look for internships in data analytics, market research, or financial analysis firms during summer breaks. Attend industry workshops and guest lectures organized by SSLA or other institutions to gain exposure to real-world challenges and network with professionals.

Tools & Resources

College placement cell, LinkedIn Jobs, Internshala, Industry conferences/webinars

Career Connection

Internships provide invaluable practical experience, build your professional network, and often lead to pre-placement offers, significantly boosting your employability after graduation.

Participate in Data Science Competitions- (Semester 3-5)

Regularly participate in online data science and statistics competitions on platforms like Kaggle or HackerEarth. These challenges push you to apply advanced techniques, learn new algorithms, and improve problem-solving skills under timed conditions.

Tools & Resources

Kaggle Competitions, HackerEarth challenges, Analytics Vidhya contests

Career Connection

Success in these competitions demonstrates advanced analytical abilities and a passion for the field, making your resume stand out to potential employers and showcasing your practical expertise.

Advanced Stage

Undertake a Comprehensive Research Project/Dissertation- (Semester 6)

Utilize your final year project to delve deep into a specific area of statistics or data science. Focus on a real-world problem, collect data, apply advanced models, and critically interpret your findings. This showcases your research capabilities.

Tools & Resources

Academic research papers, Guidance from faculty mentors, Advanced statistical software (SAS, SPSS, Stata)

Career Connection

A strong final project is a powerful portfolio piece, demonstrating independent research skills, specialized knowledge, and the ability to deliver a complete analytical solution, highly valued for both jobs and higher education.

Prepare for Specialized Industry Roles and Interviews- (Semester 6)

Tailor your preparation for specific roles like business analyst, data scientist, or quantitative researcher. Practice technical interview questions covering statistics, probability, SQL, and Python/R. Focus on case studies and problem-solving scenarios.

Tools & Resources

GeeksforGeeks (DSA, ML interview questions), LeetCode (SQL, Python), Cracking the Coding Interview, Mock interviews

Career Connection

Targeted preparation increases your chances of excelling in competitive recruitment processes, securing roles in leading companies, and achieving your desired career trajectory.

Build a Professional Portfolio and Network Strategically- (Semester 6)

Create an online portfolio (e.g., GitHub, personal website) to showcase your projects, code, and analytical reports. Actively network with alumni, industry professionals, and recruiters through LinkedIn, career fairs, and professional events.

Tools & Resources

GitHub, LinkedIn, SSLA Alumni Network, Industry meetups

Career Connection

A strong portfolio provides tangible evidence of your skills, while networking opens doors to hidden job opportunities and mentorship, crucial for long-term career growth in India''''s competitive job market.

Program Structure and Curriculum

Eligibility:

  • Passed Standard XII (10+2) or equivalent examination from any recognised Board with minimum 50% marks (45% for SC/ST).

Duration: 3 years (6 semesters)

Credits: 180 Credits

Assessment: Internal: 50%, External: 50%

Semester-wise Curriculum Table

Semester 1

Subject CodeSubject NameSubject TypeCreditsKey Topics
Environmental StudiesCore4Ecosystems, Biodiversity, Pollution, Climate change, Sustainable development, Environmental policies
Generic CommunicationCore3Principles of communication, Verbal and non-verbal, Active listening, Presentation skills, Written communication, Interpersonal communication
Indian ConstitutionCore2Preamble, Fundamental rights, Directive principles, Union and state government, Judiciary, Constitutional amendments
Elements of Computer ScienceFoundation4Basics of computing, Hardware and software, Operating systems, Networking fundamentals, Internet concepts, Data representation
General PsychologyFoundation3Introduction to psychology, Perception, Cognition, Learning, Memory, Personality and social psychology
Quantitative Aptitude ISkill Enhancement2Numbers, Percentages, Ratio & proportion, Profit & loss, Time & work, Simple & compound interest
Qualitative AptitudeSkill Enhancement2Logical reasoning, Verbal reasoning, Data interpretation, Analytical puzzles, Critical thinking
Physical Fitness IValue Added1Health components, Exercise principles, Aerobic activities, Strength training, Flexibility, Nutrition basics
Creative WritingValue Added2Storytelling, Poetry, Scriptwriting, Character development, Plot construction, Figurative language
Open Elective IElective2Introduction to chosen field, Basic concepts, Applications, Key theories

Semester 2

Subject CodeSubject NameSubject TypeCreditsKey Topics
Data Analysis and VisualizationCore4Data types, Data cleaning, Data aggregation, Basic statistical graphs, Dashboards, Interpretation
Research MethodologyCore3Research design, Data collection methods, Sampling techniques, Questionnaire design, Data analysis tools, Report writing
Introduction to SociologyFoundation3Basic sociological concepts, Social structure, Culture, Socialization, Stratification, Social change
Critical ThinkingFoundation3Logical fallacies, Argument analysis, Problem-solving, Decision-making, Bias identification, Reflective practice
Quantitative Aptitude IISkill Enhancement2Algebra, Geometry, Mensuration, Probability basics, Permutations & combinations, Data sufficiency
Business CommunicationSkill Enhancement2Professional writing, Email etiquette, Meeting protocols, Presentation skills, Negotiation, Cross-cultural communication
Physical Fitness IIValue Added1Advanced fitness concepts, Specific training methods, Injury prevention, Stress management, Lifestyle changes
Self-DefenseValue Added2Basic self-defense techniques, Awareness, Conflict avoidance, Physical conditioning, Legal aspects
Open Elective IIElective2Specialized topic introduction, Concepts and theories, Applications, Case studies

Semester 3

Subject CodeSubject NameSubject TypeCreditsKey Topics
Probability Theory and Distributions IMajor (Core)4Basic probability, Conditional probability, Random variables, Probability distributions (Binomial, Poisson, Normal), Expectation and variance
Descriptive StatisticsMajor (Core)4Data collection, Tabulation and graphical representation, Measures of central tendency, Measures of dispersion, Skewness and Kurtosis, Correlation
Inferential StatisticsMajor (Core)4Hypothesis testing, Estimation theory, Confidence intervals, Parametric tests (t-test, ANOVA), Non-parametric tests, Chi-square tests
Minor 1Minor4Foundational concepts of minor subject, Historical overview, Key theories, Basic applications
Skill Enhancement Course 3Skill Enhancement2Specific skill development, Practical application, Tools and techniques, Case studies
Value Added Course 3Value Added2Holistic development topic, Ethical considerations, Personal growth, Social responsibility
Open Elective IIIElective2Interdisciplinary concepts, Exploring diverse fields, Broadening perspectives, Introduction to new areas

Semester 4

Subject CodeSubject NameSubject TypeCreditsKey Topics
Regression AnalysisMajor (Core)4Simple linear regression, Multiple linear regression, Assumptions of regression, Residual analysis, Model diagnostics, Variable selection
Time Series AnalysisMajor (Core)4Components of time series (trend, seasonality), Smoothing techniques, Autocorrelation and Partial Autocorrelation, ARIMA models, Forecasting methods, Stationarity testing
Sampling TheoryMajor (Core)4Sampling techniques (SRS, stratified, systematic), Estimation of population parameters, Sampling errors, Design effect, Sample size determination, Cluster sampling
Minor 2Minor4Intermediate concepts of minor subject, Advanced theories, Practical applications, Case studies and analysis
Skill Enhancement Course 4Skill Enhancement2Advanced tool usage, Problem-solving scenarios, Project-based learning, Industry-relevant skills
Value Added Course 4Value Added2Leadership development, Teamwork skills, Ethics in profession, Community engagement
Open Elective IVElective2Specialized elective topic, In-depth exploration, Current trends, Case studies

Semester 5

Subject CodeSubject NameSubject TypeCreditsKey Topics
Statistical Computing using RMajor (Core)4R programming basics, Data manipulation (dplyr, tidyr), Statistical graphics (ggplot2), Data import/export, Implementing statistical tests and models, Simulation and bootstrapping
EconometricsMajor (Core)4Classical linear regression model, Violation of assumptions (heteroscedasticity, autocorrelation), Multicollinearity, Dummy variables, Panel data regression, Limited dependent variable models
Design of ExperimentsMajor (Core)4Basic principles of experimental design, Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), Factorial designs, Analysis of Covariance (ANCOVA)
Minor 3Minor4Advanced topics in minor subject, Research methods in the field, Interdisciplinary connections, Critical discourse
Skill Enhancement Course 5Skill Enhancement2Advanced professional skills, Industry standard practices, Certification preparation, Specialized software training
Value Added Course 5Value Added2Entrepreneurial thinking, Innovation and creativity, Digital citizenship, Global awareness
Open Elective VElective2Contemporary issues, Emerging fields, Advanced interdisciplinary studies, Cross-cultural perspectives

Semester 6

Subject CodeSubject NameSubject TypeCreditsKey Topics
Machine Learning using PythonMajor (Core)4Introduction to ML concepts, Supervised learning (Linear Regression, Logistic Regression, Decision Trees), Unsupervised learning (Clustering, PCA), Model evaluation metrics, Scikit-learn library in Python, Introduction to neural networks
BiostatisticsMajor (Core)4Data types in biological and medical studies, Epidemiological study designs, Clinical trials analysis, Survival analysis, Hypothesis testing in biological context, Statistical software for biostatistics
Financial StatisticsMajor (Core)4Financial data characteristics, Returns and risk analysis, Portfolio theory (CAPM, APT), Option pricing models (Black-Scholes), Value at Risk (VaR), Time series models in finance
ProjectMajor (Project)6Problem identification and literature review, Data collection and preparation, Statistical modeling and analysis, Interpretation of results, Report writing and documentation, Presentation and defense
Minor 4Minor4Capstone experience in minor subject, Independent research or project, Advanced readings, Professional applications
Open Elective VIElective2Culminating elective topic, Advanced critical analysis, Integration of knowledge, Real-world application
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