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B-SC in Computer Science Statistics Data Science at Chaitanya Degree & PG College
Hanamkonda, Telangana
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About the Specialization
What is Computer Science, Statistics, Data Science at Chaitanya Degree & PG College Hanamkonda?
This B.Sc (Mathematics, Statistics, Data Science) program at Chaitanya Degree & PG College focuses on equipping students with a robust foundation in quantitative analysis, statistical methodologies, and modern data science techniques. Designed to meet the burgeoning demand in the Indian industry, this program integrates programming skills with theoretical concepts, preparing graduates for cutting-edge roles in data-driven sectors. Its multidisciplinary approach is a key differentiator, bridging the gap between theoretical knowledge and practical application.
Who Should Apply?
This program is ideal for fresh graduates from 10+2 with a strong aptitude for mathematics and an interest in analytical problem-solving. It also suits individuals aspiring for entry-level data scientist, data analyst, or business intelligence roles in India. Enthusiastic learners keen on understanding data patterns and leveraging technology for insights, including those from diverse academic backgrounds, will find this curriculum beneficial. Strong foundational knowledge in mathematics is a specific prerequisite.
Why Choose This Course?
Graduates of this program can expect to pursue dynamic career paths in India, including Data Analyst, Business Intelligence Developer, Junior Data Scientist, Statistical Modeler, and Machine Learning Trainee. Entry-level salaries typically range from INR 3-6 lakhs per annum, with significant growth trajectories for experienced professionals reaching INR 10-20+ lakhs. The skills acquired align with industry-recognized certifications in Python, R, SQL, and various cloud platforms, boosting employability in top Indian companies.
Student Success Practices
Foundation Stage
Master Core Programming and Statistical Fundamentals- (Semester 1-2)
Dedicate significant time to thoroughly understand R and Python programming basics, data structures, and fundamental statistical concepts like descriptive statistics and probability. Consistent practice with coding exercises and problem-solving is crucial. Form study groups to discuss complex topics and clarify doubts early on.
Tools & Resources
GeeksforGeeks, HackerRank, Coursera (for ''''Introduction to R/Python''''), Khan Academy (for Statistics)
Career Connection
A strong foundation in programming and statistics is indispensable for almost all data science roles, enabling efficient data manipulation, analysis, and model building, which are entry-level job requirements.
Build a Strong Mathematical Base- (Semester 1-2)
Focus on developing a deep understanding of Differential Equations and Solid Geometry. Solve a variety of problems to strengthen analytical skills. Regularly review concepts to ensure long-term retention and prepare for advanced topics. Seek additional online resources or faculty mentorship for challenging areas.
Tools & Resources
NPTEL courses on Mathematics, MIT OpenCourseware, Schaum''''s Outlines series
Career Connection
Mathematical rigor is the backbone of advanced statistical modeling and machine learning algorithms. A solid understanding differentiates a data scientist in complex problem-solving, particularly in quantitative finance or scientific research roles.
Cultivate Effective Communication and Digital Literacy- (Semester 1-2)
Actively participate in English language and communication skill sessions. Practice public speaking, report writing, and email etiquette. Develop proficiency in basic computer skills and office tools, ensuring a comfort level with digital environments. Start building a professional online presence like a LinkedIn profile.
Tools & Resources
Grammarly, Microsoft Office tutorials, LinkedIn Learning, Toastmasters clubs
Career Connection
Effective communication is paramount for articulating data insights to non-technical stakeholders. Digital fluency is a universal requirement across all industries, boosting employability and efficiency in any professional setting.
Intermediate Stage
Apply Data Structures and DBMS for Real-world Data- (Semester 3-4)
Actively implement data structures in Python and integrate them with Database Management Systems (DBMS) concepts. Work on mini-projects involving data storage, retrieval, and manipulation using SQL. Explore real datasets to understand data modeling and normalization principles. Consider contributing to open-source projects.
Tools & Resources
MySQL/PostgreSQL, SQLZoo, LeetCode (for data structure problems), Kaggle for datasets
Career Connection
Proficiency in data structures and databases is critical for efficient data handling, large-scale data management, and building robust data pipelines, essential skills for Data Engineers and advanced Data Analysts.
Engage in Statistical Inference and Applied Statistics Projects- (Semester 3-4)
Deepen your understanding of statistical inference, hypothesis testing, and applied statistics by working on data-driven projects. Collaborate with peers on analyzing socio-economic or business datasets. Focus on interpreting statistical results and drawing meaningful conclusions relevant to real-world scenarios. Use R or Python for implementations.
Tools & Resources
RStudio, Python (with SciPy, StatsModels), NSSO data, RBI data
Career Connection
Strong statistical inference skills are highly valued in roles requiring rigorous data analysis, A/B testing, and robust decision-making in sectors like market research, finance, and quality control.
Participate in Skill-building Workshops and Online Challenges- (Semester 3-4)
Actively seek out and participate in workshops or online courses on specific data science tools or techniques, especially on internet technologies. Join coding challenges or hackathons related to data analysis. Network with professionals through online platforms or college events to gain industry insights.
Tools & Resources
Kaggle competitions, LinkedIn, SkillShare/Udemy for specific tool courses, Local tech meetups
Career Connection
Continuous skill enhancement and competitive programming experience demonstrate proactivity and practical expertise to potential employers, making you a more attractive candidate for internships and entry-level positions.
Advanced Stage
Specialize in Machine Learning/Deep Learning and Big Data- (Semester 5-6)
Choose Discipline Specific Electives (DSEs) that align with your career interests, focusing on Machine Learning, Deep Learning, Big Data Analytics, or Data Visualization. Work on comprehensive projects leveraging these advanced techniques. Understand the architectural aspects of big data systems like Hadoop/Spark and cloud platforms.
Tools & Resources
TensorFlow/Keras, PyTorch, Apache Spark, AWS/Azure/GCP free tier accounts, GitHub
Career Connection
Specialized knowledge in ML/DL and Big Data is crucial for roles like Machine Learning Engineer, AI Developer, or Big Data Analyst, which are high-demand and high-paying roles in the Indian tech industry.
Undertake a Capstone Project/Internship with Industry Relevance- (Semester 5-6)
Dedicate maximum effort to your final year project or internship. Choose a topic that solves a real-world problem or has significant industry application. Focus on applying the entire data science pipeline, from data collection and cleaning to model deployment and evaluation. Document your work meticulously and build a strong portfolio.
Tools & Resources
Industry connections (through college placement cell), Personal network, Freelancing platforms (for project ideas), Portfolio websites
Career Connection
A strong project or internship is often the most impactful element of a resume for entry-level roles. It demonstrates practical skills, problem-solving abilities, and readiness for a professional data science environment.
Prepare for Placements and Professional Certifications- (Semester 5-6)
Actively engage in placement preparation activities, including mock interviews, aptitude tests, and resume building workshops. Pursue relevant professional certifications (e.g., AWS Certified Data Analytics, Microsoft Certified: Azure Data Scientist Associate, Google Cloud Professional Data Engineer) to validate your skills and boost your profile. Network aggressively with alumni and industry professionals.
Tools & Resources
Mock interview platforms, Aptitude test preparation books/websites, Official certification training materials, LinkedIn
Career Connection
Targeted placement preparation significantly increases success rates in securing jobs. Professional certifications provide industry recognition and demonstrate commitment to skill development, giving a competitive edge in the Indian job market.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 6 semesters / 3 years
Credits: 146 Credits
Assessment: Internal: 20% (for theory papers), External: 80% (for theory papers)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC101 | Differential Equations | Core Theory (Mathematics) | 4 | Exact differential equations, Bernoulli''''s equation, Higher order linear differential equations, Cauchy-Euler equations, Systems of linear differential equations |
| BSC101P | Differential Equations Lab | Core Practical (Mathematics) | 1 | Solving first order ODEs, Solving second order ODEs, Laplace transforms, Numerical methods for ODEs |
| BSC102 | Descriptive Statistics and Probability | Core Theory (Statistics) | 4 | Measures of central tendency and dispersion, Moments, skewness, kurtosis, Correlation and Regression analysis, Basic probability theory, Random variables and their properties |
| BSC102P | Descriptive Statistics and Probability Lab | Core Practical (Statistics) | 1 | Data presentation and visualization, Calculation of statistical measures, Correlation and regression fitting, Probability experiments |
| BSC103 | Introduction to Data Science using R | Core Theory (Data Science) | 4 | Overview of Data Science concepts, R programming fundamentals, Data structures in R (vectors, lists, data frames), Data import, export and cleaning in R, Basic data visualization and statistical analysis in R |
| BSC103P | Introduction to Data Science using R Lab | Core Practical (Data Science) | 1 | R environment setup, Working with R data types and operators, Data manipulation and cleaning, Generating basic plots and graphs, Implementing simple statistical functions |
| EN104 | English Language and Communication Skills | Language | 2 | Fundamentals of communication, Grammar and vocabulary, Reading comprehension, Writing skills (essays, letters), Presentation skills |
| AECC105 | Environmental Science | Ability Enhancement Compulsory Course (AECC) | 2 | Ecosystems and biodiversity, Natural resources and their conservation, Environmental pollution and control, Global environmental issues, Sustainable development |
| OE106 | Open Elective - I | Open Elective | 2 |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC201 | Solid Geometry | Core Theory (Mathematics) | 4 | The plane and straight line, Sphere and its properties, Cones and cylinders, Equation of a conicoid, Classification of conicoids |
| BSC201P | Solid Geometry Lab | Core Practical (Mathematics) | 1 | Visualizing 3D objects, Representing planes and lines, Drawing spheres and cones, Geometric transformations, Graphing conicoids |
| BSC202 | Probability Distributions | Core Theory (Statistics) | 4 | Discrete probability distributions (Binomial, Poisson), Continuous probability distributions (Normal, Exponential), Moment Generating Functions, Chebychev''''s Inequality, Central Limit Theorem |
| BSC202P | Probability Distributions Lab | Core Practical (Statistics) | 1 | Fitting binomial and Poisson distributions, Generating random samples from distributions, Testing for normality, Applications of central limit theorem |
| BSC203 | Programming in Python for Data Science | Core Theory (Data Science) | 4 | Python programming basics, Data types, operators, control structures, Functions, modules, and packages, NumPy for numerical computing, Pandas for data manipulation and analysis, Matplotlib for data visualization |
| BSC203P | Programming in Python for Data Science Lab | Core Practical (Data Science) | 1 | Writing Python programs for data operations, Using NumPy arrays and operations, Data cleaning and transformation with Pandas, Creating various plots with Matplotlib, Applying basic statistical functions in Python |
| EN204 | English Language and Communication Skills | Language | 2 | Advanced grammar and usage, Active listening and speaking skills, Report writing and academic writing, Formal and informal communication, Group discussions and interviews |
| AECC205 | Basic Computer Skills | Ability Enhancement Compulsory Course (AECC) | 2 | Computer hardware and software fundamentals, Operating system concepts (Windows, Linux), MS Word, Excel, PowerPoint applications, Internet basics and web browsers, Email communication and online safety |
| OE206 | Open Elective - II | Open Elective | 2 |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC301 | Real Analysis | Core Theory (Mathematics) | 4 | Sequences and series of real numbers, Limits, continuity and uniform continuity, Differentiation, Mean Value Theorems, Riemann integration, Improper integrals |
| BSC301P | Real Analysis Lab | Core Practical (Mathematics) | 1 | Graphing sequences and series convergence, Illustrating continuity and differentiability, Numerical integration techniques, Exploring properties of functions, Visualizing mean value theorems |
| BSC302 | Statistical Methods and Inference | Core Theory (Statistics) | 4 | Sampling distributions (t, Chi-square, F), Point estimation and Interval estimation, Hypothesis testing (large and small samples), Analysis of Variance (ANOVA), Non-parametric tests |
| BSC302P | Statistical Methods and Inference Lab | Core Practical (Statistics) | 1 | Implementing various statistical tests, Constructing confidence intervals, Performing ANOVA in R/Python, Calculating p-values, Drawing conclusions from hypothesis tests |
| BSC303 | Data Structures for Data Science | Core Theory (Data Science) | 4 | Arrays, Linked Lists (single, double, circular), Stacks and Queues, Trees (Binary Trees, BST, AVL Trees), Graphs (representations, traversals), Searching and Sorting algorithms (with complexity analysis), Hashing and collision resolution |
| BSC303P | Data Structures for Data Science Lab | Core Practical (Data Science) | 1 | Implementing basic data structures, Writing code for searching and sorting, Tree traversals, Graph algorithms (BFS, DFS), Hashing implementations |
| SEC304 | Digital Fluency | Skill Enhancement Course (SEC) | 2 | Fundamentals of computer hardware and software, Operating system concepts, MS Office suite (Word, Excel, PowerPoint) for productivity, Internet applications and services, Cyber hygiene and digital security |
| OE305 | Open Elective - III | Open Elective | 2 |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC401 | Abstract Algebra | Core Theory (Mathematics) | 4 | Groups and subgroups, Normal subgroups and quotient groups, Homomorphisms and isomorphisms, Rings and Integral Domains, Fields and their properties |
| BSC401P | Abstract Algebra Lab | Core Practical (Mathematics) | 1 | Exploring group properties with examples, Constructing quotient groups, Illustrating homomorphisms, Working with modular arithmetic, Implementing algebraic structures |
| BSC402 | Applied Statistics | Core Theory (Statistics) | 4 | Sampling techniques (SRS, Stratified, Systematic), Index Numbers (Laspeyres, Paasche, Fisher), Time Series Analysis (components, forecasting), Vital Statistics (mortality, fertility rates), Statistical Quality Control (control charts for variables and attributes) |
| BSC402P | Applied Statistics Lab | Core Practical (Statistics) | 1 | Implementing various sampling methods, Calculating and interpreting index numbers, Time series forecasting using software, Computing vital rates, Constructing and analyzing control charts |
| BSC403 | Database Management Systems for Data Science | Core Theory (Data Science) | 4 | Introduction to DBMS and its architecture, Entity-Relationship (ER) model, Relational Model and Relational Algebra, Structured Query Language (SQL): DDL, DML, DCL, Normalization (1NF, 2NF, 3NF, BCNF), Transaction management and concurrency control |
| BSC403P | Database Management Systems for Data Science Lab | Core Practical (Data Science) | 1 | Designing ER diagrams, Creating tables and defining constraints in SQL, Writing complex queries using DML commands, Implementing stored procedures and triggers, Applying normalization techniques |
| SEC404 | Internet Technologies | Skill Enhancement Course (SEC) | 2 | Internet basics and web protocols, HTML and CSS fundamentals, Web browsers and search engines, E-commerce concepts, Cyber security and ethical hacking basics |
| OE405 | Open Elective - IV | Open Elective | 2 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC501A | Advanced Calculus | Discipline Specific Elective Theory (Mathematics Choice) | 4 | Functions of several variables, Limits, continuity, and differentiability in higher dimensions, Partial derivatives and chain rule, Taylor''''s theorem for functions of several variables, Extrema of functions of several variables |
| BSC501B | Numerical Methods | Discipline Specific Elective Theory (Mathematics Choice) | 4 | Solutions of algebraic and transcendental equations, Interpolation techniques, Numerical differentiation and integration, Numerical solutions of ordinary differential equations, Curve fitting and regression |
| BSC501AP | Advanced Calculus Lab | Discipline Specific Elective Practical (Mathematics Choice) | 1 | Visualizing multivariate functions, Computing partial derivatives, Finding critical points, Implementing integration methods, Solving multivariable optimization problems |
| BSC501BP | Numerical Methods Lab | Discipline Specific Elective Practical (Mathematics Choice) | 1 | Implementing root-finding algorithms, Lagrange and Newton''''s interpolation, Numerical differentiation techniques, Numerical integration (Trapezoidal, Simpson''''s), Solving ODEs numerically |
| BSC502A | Linear Algebra | Discipline Specific Elective Theory (Mathematics Choice) | 4 | Vector spaces and subspaces, Basis and dimension, Linear transformations, Eigenvalues and eigenvectors, Cayley-Hamilton theorem |
| BSC502B | Complex Analysis | Discipline Specific Elective Theory (Mathematics Choice) | 4 | Complex numbers and functions, Analytic functions and Cauchy-Riemann equations, Complex integration and Cauchy''''s theorem, Taylor and Laurent series, Residue theorem and applications |
| BSC502AP | Linear Algebra Lab | Discipline Specific Elective Practical (Mathematics Choice) | 1 | Vector and matrix operations, Solving systems of linear equations, Finding eigenvalues and eigenvectors, Implementing linear transformations, Matrix decompositions |
| BSC502BP | Complex Analysis Lab | Discipline Specific Elective Practical (Mathematics Choice) | 1 | Visualizing complex functions, Plotting complex transformations, Numerical complex integration, Approximating series expansions, Calculating residues |
| BSC503A | Linear Models and Regression Analysis | Discipline Specific Elective Theory (Statistics Choice) | 4 | Simple and multiple linear regression, Assumptions of linear regression, Parameter estimation and hypothesis testing, Model adequacy checking, Introduction to ANOVA and ANCOVA |
| BSC503B | Design of Experiments | Discipline Specific Elective Theory (Statistics Choice) | 4 | Basic principles of experimental design, Completely Randomized Design (CRD), Randomized Block Design (RBD), Latin Square Design (LSD), Factorial experiments |
| BSC503AP | Linear Models and Regression Analysis Lab | Discipline Specific Elective Practical (Statistics Choice) | 1 | Fitting simple and multiple regression models, Residual analysis and diagnostics, Hypothesis testing in regression, Using statistical software for regression, ANOVA table interpretation |
| BSC503BP | Design of Experiments Lab | Discipline Specific Elective Practical (Statistics Choice) | 1 | Analyzing CRD, RBD, LSD data, ANOVA for experimental designs, Calculating treatment effects, Interpreting results of factorial experiments, Using statistical software for DOE |
| BSC504A | Actuarial Statistics | Discipline Specific Elective Theory (Statistics Choice) | 4 | Life tables and survival models, Principles of insurance and annuities, Premium calculation methods, Risk theory and solvency, Pension funds and social security |
| BSC504B | Official Statistics | Discipline Specific Elective Theory (Statistics Choice) | 4 | Role of national statistical systems, Sources of official statistics in India, Population census and surveys, Agricultural, Industrial, Labour statistics, National income accounting and economic indicators |
| BSC504AP | Actuarial Statistics Lab | Discipline Specific Elective Practical (Statistics Choice) | 1 | Constructing life tables, Calculating premiums and annuities, Modeling survival data, Risk assessment exercises, Using actuarial tables |
| BSC504BP | Official Statistics Lab | Discipline Specific Elective Practical (Statistics Choice) | 1 | Analyzing census data, Working with NSSO data, Calculating economic indicators, Interpreting government reports, Data presentation of official statistics |
| BSC505A | Machine Learning Essentials | Discipline Specific Elective Theory (Data Science Choice) | 4 | Introduction to Machine Learning, Supervised Learning: Linear and Logistic Regression, SVM, Unsupervised Learning: K-Means Clustering, PCA, Decision Trees and Ensemble Methods (Random Forests), Model evaluation metrics and cross-validation |
| BSC505B | Data Mining Fundamentals | Discipline Specific Elective Theory (Data Science Choice) | 4 | Data Mining processes and tasks, Data preprocessing techniques, Association Rule Mining (Apriori algorithm), Classification methods (Naïve Bayes, k-NN), Clustering techniques (Hierarchical, Density-based) |
| BSC505AP | Machine Learning Essentials Lab | Discipline Specific Elective Practical (Data Science Choice) | 1 | Implementing regression models in Python, Applying classification algorithms, Performing clustering on datasets, Hyperparameter tuning, Model evaluation and selection |
| BSC505BP | Data Mining Fundamentals Lab | Discipline Specific Elective Practical (Data Science Choice) | 1 | Data preprocessing tasks in Python, Implementing association rule mining, Applying various classification algorithms, Performing clustering analysis, Evaluating data mining models |
| BSC506A | Big Data Analytics | Discipline Specific Elective Theory (Data Science Choice) | 4 | Introduction to Big Data concepts, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark for big data processing, NoSQL databases (MongoDB, Cassandra), Data ingestion and streaming analytics, Big data visualization |
| BSC506B | Data Visualization Techniques | Discipline Specific Elective Theory (Data Science Choice) | 4 | Principles of effective data visualization, Types of charts and graphs, Visualization tools (Tableau, Power BI, D3.js), Interactive dashboards and storytelling with data, Geospatial and network visualizations |
| BSC506AP | Big Data Analytics Lab | Discipline Specific Elective Practical (Data Science Choice) | 1 | Working with HDFS commands, Writing MapReduce programs, Implementing Spark applications, Interacting with NoSQL databases, Processing large datasets |
| BSC506BP | Data Visualization Techniques Lab | Discipline Specific Elective Practical (Data Science Choice) | 1 | Creating various plots using Matplotlib/Seaborn, Building interactive dashboards, Using Tableau/Power BI for data visualization, Designing infographics, Exploring advanced visualization libraries |
| GE507 | Generic Elective | Generic Elective | 2 |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BSC601A | Ring Theory | Discipline Specific Elective Theory (Mathematics Choice) | 4 | Rings, subrings, and ideals, Quotient rings and homomorphisms, Integral domains and fields, Polynomial rings, Factorization in integral domains |
| BSC601B | Operations Research | Discipline Specific Elective Theory (Mathematics Choice) | 4 | Linear Programming Problems (LPP): graphical and simplex method, Transportation Problem, Assignment Problem, Game Theory, Queuing Theory |
| BSC601AP | Ring Theory Lab | Discipline Specific Elective Practical (Mathematics Choice) | 1 | Examples of rings and ideals, Constructing quotient rings, Testing for integral domains, Working with polynomial operations, Exploring factorization properties |
| BSC601BP | Operations Research Lab | Discipline Specific Elective Practical (Mathematics Choice) | 1 | Solving LPPs using software, Implementing transportation algorithms, Solving assignment problems, Game theory problem solving, Simulating queuing systems |
| BSC602A | Graph Theory | Discipline Specific Elective Theory (Mathematics Choice) | 4 | Basic concepts of graphs, paths, cycles, Trees and spanning trees, Planar graphs and graph coloring, Eulerian and Hamiltonian graphs, Shortest path algorithms (Dijkstra''''s, Floyd-Warshall) |
| BSC602B | Metric Spaces | Discipline Specific Elective Theory (Mathematics Choice) | 4 | Metric spaces and examples, Open and closed sets, Convergence of sequences and completeness, Continuous functions on metric spaces, Compactness and connectedness |
| BSC602AP | Graph Theory Lab | Discipline Specific Elective Practical (Mathematics Choice) | 1 | Graph representation in code, Implementing BFS and DFS, Finding minimum spanning trees, Applying shortest path algorithms, Graph visualization |
| BSC602BP | Metric Spaces Lab | Discipline Specific Elective Practical (Mathematics Choice) | 1 | Exploring different metrics, Identifying open and closed sets, Analyzing convergent sequences, Visualizing continuous functions, Properties of compact sets |
| BSC603A | Econometrics | Discipline Specific Elective Theory (Statistics Choice) | 4 | Introduction to econometric models, Classical Linear Regression Model (CLRM), Violations of CLRM assumptions (multicollinearity, heteroskedasticity), Time series econometrics (stationarity, ARIMA models), Simultaneous equation models |
| BSC603B | Demographic Methods | Discipline Specific Elective Theory (Statistics Choice) | 4 | Sources of demographic data, Measures of mortality (CDR, SDR, IMR), Measures of fertility (CBR, GFR, TFR), Population growth models, Population projection methods |
| BSC603AP | Econometrics Lab | Discipline Specific Elective Practical (Statistics Choice) | 1 | Estimating regression models with economic data, Testing for CLRM violations, Time series analysis in R/Python, Forecasting economic variables, Using econometric software |
| BSC603BP | Demographic Methods Lab | Discipline Specific Elective Practical (Statistics Choice) | 1 | Analyzing census and survey data, Calculating various demographic rates, Constructing life tables, Performing population projections, Visualizing demographic trends |
| BSC604A | Reliability Theory | Discipline Specific Elective Theory (Statistics Choice) | 4 | Basic concepts of reliability, Failure rate and mean time to failure, Reliability of systems (series, parallel), Life distributions (Exponential, Weibull), Reliability estimation and testing |
| BSC604B | Bayesian Inference | Discipline Specific Elective Theory (Statistics Choice) | 4 | Foundations of Bayesian statistics, Prior, likelihood, and posterior distributions, Bayesian estimation and hypothesis testing, Conjugate priors, Markov Chain Monte Carlo (MCMC) methods |
| BSC604AP | Reliability Theory Lab | Discipline Specific Elective Practical (Statistics Choice) | 1 | Calculating reliability metrics, Analyzing system reliability, Fitting life distributions to data, Estimating failure rates, Simulating reliability scenarios |
| BSC604BP | Bayesian Inference Lab | Discipline Specific Elective Practical (Statistics Choice) | 1 | Implementing Bayes'''' theorem for various problems, Working with prior and posterior distributions, Bayesian estimation in R/Python, MCMC simulations, Comparing Bayesian and frequentist approaches |
| BSC605A | Deep Learning Basics | Discipline Specific Elective Theory (Data Science Choice) | 4 | Introduction to Artificial Neural Networks (ANNs), Perceptrons and multi-layer perceptrons, Backpropagation algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Activation functions, optimizers, regularization |
| BSC605B | Natural Language Processing | Discipline Specific Elective Theory (Data Science Choice) | 4 | Fundamentals of NLP, Text preprocessing (tokenization, stemming, lemmatization), Language models (N-grams), Part-of-Speech tagging and Named Entity Recognition, Sentiment analysis and text classification, Introduction to word embeddings |
| BSC605AP | Deep Learning Basics Lab | Discipline Specific Elective Practical (Data Science Choice) | 1 | Building ANNs using TensorFlow/Keras, Implementing CNNs for image classification, Working with RNNs for sequence data, Experimenting with different activation functions, Fine-tuning deep learning models |
| BSC605BP | Natural Language Processing Lab | Discipline Specific Elective Practical (Data Science Choice) | 1 | Text preprocessing using NLTK, Implementing language models, Developing sentiment analysis tools, Performing named entity recognition, Building text classifiers |
| BSC606A | Cloud Computing for Data Science | Discipline Specific Elective Theory (Data Science Choice) | 4 | Introduction to Cloud Computing concepts, Service Models (IaaS, PaaS, SaaS), Deployment Models (Public, Private, Hybrid), AWS/Azure/GCP basic services for data science, Big data processing on cloud, Cloud security and compliance |
| BSC606B | Social Network Analysis | Discipline Specific Elective Theory (Data Science Choice) | 4 | Introduction to Social Network Analysis (SNA), Network representations (adjacency matrices, graphs), Measures of centrality (degree, betweenness, closeness), Community detection algorithms, Network visualization and dynamics |
| BSC606AP | Cloud Computing for Data Science Lab | Discipline Specific Elective Practical (Data Science Choice) | 1 | Setting up cloud instances (EC2, Azure VMs), Working with cloud storage (S3, Azure Blob), Deploying data science applications to cloud, Using cloud-based data processing services, Implementing basic cloud security features |
| BSC606BP | Social Network Analysis Lab | Discipline Specific Elective Practical (Data Science Choice) | 1 | Creating and visualizing networks with NetworkX, Calculating centrality measures, Implementing community detection algorithms, Analyzing real-world social network data, Interpreting network structures and patterns |
| PRJ607 | Project / Internship | Project | 4 | Project proposal and planning, Data collection and preprocessing, Application of data science techniques, Report writing and documentation, Presentation and defense |
