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B-SC in Computer Science Data Science Statistics at The Oxford College of Arts
Bengaluru, Karnataka
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About the Specialization
What is Computer Science, Data Science, Statistics at The Oxford College of Arts Bengaluru?
This B.Sc. program at The Oxford College of Arts, Bengaluru, offers specializations in Computer Science, Data Science, and Statistics, catering to the burgeoning tech and analytics industries in India. The curriculum is designed under NEP 2020 guidelines, providing a robust foundation and advanced skills. This multifaceted program uniquely prepares students for diverse roles in the rapidly evolving Indian digital economy.
Who Should Apply?
This program is ideal for fresh 10+2 science graduates with a strong aptitude for logical reasoning and mathematics, aspiring to build careers in technology and data-driven fields. It also suits individuals passionate about problem-solving through coding, statistical analysis, or data interpretation, seeking entry into software development, data analytics, or research roles within various Indian industries. Previous exposure to basic programming or statistics is an added advantage.
Why Choose This Course?
Graduates of this program can expect promising career paths in India as Software Developers, Data Analysts, Statisticians, Machine Learning Engineers, or Business Intelligence professionals. Entry-level salaries typically range from INR 3.5 to 6 LPA, with significant growth trajectories reaching INR 10-15+ LPA for experienced roles. The program also aligns with requirements for various professional certifications in analytics, cloud, and programming, enhancing employability in India''''s competitive job market.
Student Success Practices
Foundation Stage
Master Core Programming & Statistical Concepts- (Semester 1-2)
Dedicate significant time to understanding fundamental programming constructs in C/Python/R and foundational statistical concepts. Practice daily coding problems on platforms like HackerRank or CodeChef and apply statistical methods using tools like R-Studio to solidify your understanding. Participate in college coding clubs and basic statistics workshops.
Tools & Resources
HackerRank, CodeChef, GeeksforGeeks, R-Studio, Python IDLE
Career Connection
A strong foundation is crucial for cracking technical interviews and excelling in early career roles in software development, data analysis, and quantitative research. It ensures you have the building blocks for advanced topics.
Develop Strong Problem-Solving Skills- (Semester 1-2)
Beyond rote learning, focus on understanding the logic behind algorithms and statistical models. Engage in peer programming sessions, solve logical puzzles, and break down complex problems into smaller, manageable parts. Seek feedback from professors and seniors on your approach to problem-solving.
Tools & Resources
LeetCode, Project Euler, Problem-solving groups, Mentorship programs
Career Connection
Indian IT and data companies highly value analytical and problem-solving abilities. Mastering this skill from early on will differentiate you in internships and placements, enabling you to tackle real-world business challenges effectively.
Build a Digital Portfolio- (Semester 1-2)
Start creating a GitHub profile to showcase your coding projects, lab assignments, and any open-source contributions. Even small projects demonstrate initiative and practical application of learned concepts. For Statistics/Data Science, include R/Python notebooks demonstrating data analysis.
Tools & Resources
GitHub, Jupyter Notebooks, Google Colab, LinkedIn
Career Connection
A visible online portfolio is increasingly vital for recruiters in India, especially for freshers. It serves as a live resume, demonstrating your practical skills and passion, which can lead to better internship and job opportunities.
Intermediate Stage
Engage in Applied Projects & Mini-Research- (Semester 3-5)
Beyond curriculum, undertake small group projects that apply CS, DS, or Stats concepts to real-world data or problems. Leverage public datasets (e.g., Kaggle) for data science, build small web applications, or conduct statistical surveys. Present your findings in college events or departmental seminars.
Tools & Resources
Kaggle, UCI Machine Learning Repository, Flask/Django, R Shiny
Career Connection
Practical project experience is highly sought after by Indian employers. It helps you develop critical thinking, teamwork, and domain-specific application skills, making you more competitive for internships and specialized roles.
Network with Industry Professionals- (Semester 3-5)
Attend industry webinars, guest lectures, and local tech/data meetups in Bengaluru. Connect with alumni and professionals on LinkedIn, seeking advice and potential mentorship. Actively participate in hackathons and data-a-thons organized by companies or colleges to expand your professional circle.
Tools & Resources
LinkedIn, Meetup.com, Industry conferences, College career fairs
Career Connection
Networking is paramount in the Indian job market. Strong connections can lead to internship leads, mentorship, and direct referrals, often bypassing competitive application processes and opening doors to niche opportunities.
Seek Certifications in In-Demand Technologies- (Semester 3-5)
Identify and pursue relevant certifications in areas like SQL, Cloud Computing (AWS/Azure/GCP basics), Python for Data Science, or specialized statistical software. These add tangible value to your resume and demonstrate a proactive approach to skill development.
Tools & Resources
Coursera, Udemy, NPTEL, Microsoft Learn, Google Cloud Skills Boost
Career Connection
Certifications validate your skills to Indian employers, especially in rapidly evolving tech domains. They can boost your resume, improve your chances of shortlisting, and prepare you for technical rounds during placements.
Advanced Stage
Undertake a Substantial Internship/Live Project- (Semester 6-8)
Secure a long-term internship (6 months preferred) or a capstone live project with a reputable company. Focus on applying your core specialization skills to real-world problems under professional guidance, delivering measurable impact. Document your contributions meticulously for your resume.
Tools & Resources
Internshala, Naukri.com, Company career pages, Faculty connections
Career Connection
Internships are often direct pathways to full-time employment in India. A strong performance and relevant project work are crucial for converting internships into PPOs (Pre-Placement Offers) or securing excellent final placements.
Intensive Placement Preparation & Mock Interviews- (Semester 6-8)
Engage in rigorous preparation for aptitude tests, technical rounds, and HR interviews. Form study groups, solve previous year''''s placement papers, and participate in mock interviews conducted by the college placement cell or external agencies. Focus on communication and behavioral skills.
Tools & Resources
Placement cell resources, Online aptitude tests, Glassdoor, Mock interview platforms
Career Connection
Dedicated placement preparation is non-negotiable for success in campus recruitment drives. Strong performance ensures selection in top companies, leading to desired career starts and competitive salary packages in India.
Specialize and Build a Niche Expertise- (Semester 6-8)
Towards the final semesters, delve deeper into a specific sub-field within your specialization (e.g., AI/ML in CS, Predictive Analytics in DS, Biostatistics in Stats). Take advanced electives, pursue a research project, and publish a paper if possible, establishing yourself as a subject matter expert.
Tools & Resources
Advanced textbooks, Research papers (e.g., arXiv), Specialized courses, PhD program details
Career Connection
Niche expertise makes you a highly valuable asset, especially for specialized roles and advanced research positions in Indian R&D centers or startups. It opens opportunities for higher studies (M.Sc./Ph.D.) or highly specialized industry roles.
Program Structure and Curriculum
Eligibility:
- Passed in PUC / 10 + 2 from a recognized board with Science stream with at least 45% marks in aggregate.
Duration: 4 years / 8 semesters
Credits: 176 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCUNP101 | Indian Language (e.g., Kannada, Hindi, Sanskrit) | Compulsory Language | 3 | Prose and Poetry, Grammar and Vocabulary, Functional Communication, Cultural Aspects of Language, Literary Criticism |
| BCUEG101 | English | Compulsory Language | 3 | English Grammar, Reading Comprehension, Business Communication, Creative Writing, Public Speaking |
| BCUAECC101 | Digital Fluency | Ability Enhancement Compulsory Course (AECC) | 2 | Computer Fundamentals, Operating Systems, MS Office Applications, Internet and Web Browsing, Cyber Security Basics |
| BCUSEC101 | Web Designing / Office Automation / R Programming | Skill Enhancement Course (SEC) | 2 | HTML and CSS Basics, JavaScript Fundamentals, Responsive Design, Web Page Layout, Tools for Web Development |
| BCUCSC101 | Fundamentals of Computers and Programming in C | Disciplinary Core (Computer Science Major) | 4 | Computer Organization, C Language Syntax, Control Structures, Functions and Pointers, Arrays and Strings |
| BCUCSC102P | C Programming Lab | Lab (Computer Science Major) | 2 | Algorithm Implementation, C Program Debugging, Problem Solving with C, Practical Application of Loops, Function Calls |
| BCUDAS101 | Introduction to Data Science & R Programming | Disciplinary Core (Data Science Major) | 4 | Data Science Lifecycle, R Environment Setup, Data Types in R, Data Manipulation with Dplyr, Basic Data Visualization |
| BCUDAS102P | Data Science & R Programming Lab | Lab (Data Science Major) | 2 | R Scripting Practice, Data Import/Export, Exploratory Data Analysis, Basic Statistical Tests, Report Generation |
| BCUSTS101 | Descriptive Statistics I | Disciplinary Core (Statistics Major) | 4 | Measures of Central Tendency, Measures of Dispersion, Skewness and Kurtosis, Correlation Analysis, Regression Line Fitting |
| BCUSTS102P | Descriptive Statistics I Lab using R/Python/Excel | Lab (Statistics Major) | 2 | Data Summarization, Graphical Representation, Correlation Coefficients, Regression Equation, Software Application |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCUNP201 | Indian Language (e.g., Kannada, Hindi, Sanskrit) | Compulsory Language | 3 | Literary Forms, Regional Literature, Advanced Grammar, Translation Skills, Critical Appreciation |
| BCUEG201 | English | Compulsory Language | 3 | Technical Writing, Presentation Skills, Report Writing, Critical Thinking, Group Discussion |
| BCUAECC201 | Environmental Studies | Ability Enhancement Compulsory Course (AECC) | 2 | Ecology and Ecosystems, Biodiversity Conservation, Pollution Control, Climate Change, Sustainable Development |
| BCUSEC201 | Python Programming / Data Visualization Tools | Skill Enhancement Course (SEC) | 2 | Python Basics, Data Structures in Python, Control Flow, Functions and Modules, File Handling |
| BCUCSC201 | Data Structures Using C | Disciplinary Core (Computer Science Major) | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Searching Algorithms, Sorting Algorithms |
| BCUCSC202P | Data Structures Lab | Lab (Computer Science Major) | 2 | Implementation of Data Structures, Algorithm Efficiency, Stack and Queue Operations, Tree Traversal, Graph Algorithms |
| BCUDAS201 | Data Management using SQL | Disciplinary Core (Data Science Major) | 4 | Relational Database Concepts, SQL Queries, Data Definition Language, Data Manipulation Language, Database Normalization |
| BCUDAS202P | Data Management using SQL Lab | Lab (Data Science Major) | 2 | Database Creation, Complex Queries, Data Integrity Constraints, Joins and Subqueries, Transaction Control |
| BCUSTS201 | Probability and Distribution I | Disciplinary Core (Statistics Major) | 4 | Random Variables, Probability Distributions, Binomial Distribution, Poisson Distribution, Normal Distribution |
| BCUSTS202P | Probability and Distribution I Lab | Lab (Statistics Major) | 2 | Probability Calculation, Random Variable Generation, Distribution Fitting, Hypothesis Testing Basics, Statistical Software Application |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCUVAC301 | Indian Constitution / Human Rights / Philosophy | Value Added Course (VAC) | 2 | Constitutional Principles, Fundamental Rights, Directive Principles, Human Rights Conventions, Indian Governance Structure |
| BCUSEC301 | Cloud Computing Fundamentals / Introduction to Machine Learning | Skill Enhancement Course (SEC) | 2 | Cloud Service Models, Deployment Models, Virtualization, Cloud Security Basics, AWS/Azure Overview |
| BCUCSC301 | Object Oriented Programming using JAVA | Disciplinary Core (Computer Science Major) | 4 | OOP Concepts, Java Syntax and Basics, Classes and Objects, Inheritance and Polymorphism, Exception Handling |
| BCUCSC302P | JAVA Programming Lab | Lab (Computer Science Major) | 2 | Java Program Development, Object-Oriented Design, GUI Programming, File I/O in Java, Collection Framework |
| BCUDAS301 | Data Mining | Disciplinary Core (Data Science Major) | 4 | Data Mining Process, Classification Algorithms, Clustering Techniques, Association Rule Mining, Data Preprocessing |
| BCUDAS302P | Data Mining Lab | Lab (Data Science Major) | 2 | Weka/RapidMiner Tools, Classification Model Building, Clustering Implementation, Association Rule Discovery, Performance Evaluation |
| BCUSTS301 | Sampling Techniques | Disciplinary Core (Statistics Major) | 4 | Sampling Methods, Simple Random Sampling, Stratified Sampling, Systematic Sampling, Ratio and Regression Estimators |
| BCUSTS302P | Sampling Techniques Lab | Lab (Statistics Major) | 2 | Sample Selection, Estimation Procedures, Bias and Variance, Survey Design, Statistical Software for Sampling |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCUVAC401 | Artificial Intelligence (AI) / Cyber Security / Entrepreneurship | Value Added Course (VAC) | 2 | Introduction to AI, Types of AI, AI Applications, Ethical AI, Future of AI |
| BCUSEC401 | Advanced Excel / Data Analysis with Python | Skill Enhancement Course (SEC) | 2 | Advanced Excel Functions, Pivot Tables, Data Validation, Conditional Formatting, VBA Macros |
| BCUCSC401 | Database Management Systems | Disciplinary Core (Computer Science Major) | 4 | DBMS Architecture, Relational Model, SQL Commands, Database Design, Transaction Management |
| BCUCSC402P | DBMS Lab | Lab (Computer Science Major) | 2 | SQL Query Practice, Database Schema Creation, Data Manipulation, PL/SQL Programming, Report Generation from DB |
| BCUDAS401 | Big Data Analytics | Disciplinary Core (Data Science Major) | 4 | Introduction to Big Data, Hadoop Ecosystem, MapReduce Framework, Spark Basics, NoSQL Databases |
| BCUDAS402P | Big Data Analytics Lab | Lab (Data Science Major) | 2 | Hadoop Installation, MapReduce Programs, Spark Data Processing, Hive Queries, MongoDB Operations |
| BCUSTS401 | Statistical Inference I | Disciplinary Core (Statistics Major) | 4 | Point Estimation, Interval Estimation, Hypothesis Testing, Parametric Tests, Non-Parametric Tests |
| BCUSTS402P | Statistical Inference I Lab | Lab (Statistics Major) | 2 | Confidence Intervals, Z-tests and T-tests, Chi-square Tests, ANOVA, Software for Inference |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCUOES501 | Open Elective | Open Elective (OE) | 3 | Interdisciplinary Studies, Skill Development, Knowledge Broadening, Cross-Domain Learning, Optional Subject Choice |
| BCUCSC501 | Computer Networks | Disciplinary Core (Computer Science Major) | 4 | Network Topologies, OSI Model, TCP/IP Protocol Suite, Routing Algorithms, Network Security |
| BCUCSC502P | Computer Networks Lab | Lab (Computer Science Major) | 2 | Network Configuration, Socket Programming, Packet Analysis, Routing Protocols, Network Simulation Tools |
| BCUCSC503 | Python Programming | Disciplinary Core (Computer Science Major) | 4 | Advanced Python, Data Structures in Python, Object-Oriented Python, Modules and Packages, Web Scraping |
| BCUCSC504P | Python Programming Lab | Lab (Computer Science Major) | 2 | Python Scripting, Data Analysis with Pandas, Visualization with Matplotlib, Building Applications, API Integration |
| BCUDAS501 | Machine Learning | Disciplinary Core (Data Science Major) | 4 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Model Evaluation |
| BCUDAS502P | Machine Learning Lab | Lab (Data Science Major) | 2 | Scikit-learn Implementation, Algorithm Tuning, Data Preprocessing for ML, Cross-Validation, Predictive Model Building |
| BCUDAS503 | Exploratory Data Analysis | Disciplinary Core (Data Science Major) | 4 | Data Cleaning, Data Transformation, Univariate Analysis, Multivariate Analysis, Advanced Visualization |
| BCUDAS504P | Exploratory Data Analysis Lab | Lab (Data Science Major) | 2 | Pandas for EDA, Seaborn/Plotly, Missing Value Imputation, Outlier Detection, Feature Engineering |
| BCUSTS501 | Operations Research | Disciplinary Core (Statistics Major) | 4 | Linear Programming, Simplex Method, Transportation Problem, Assignment Problem, Network Analysis |
| BCUSTS502P | Operations Research Lab | Lab (Statistics Major) | 2 | Solver Tools, Optimization Problems, Decision Making, Simulation Models, Software for OR |
| BCUSTS503 | Time Series Analysis | Disciplinary Core (Statistics Major) | 4 | Time Series Components, Stationarity, ARIMA Models, Forecasting Techniques, Spectral Analysis |
| BCUSTS504P | Time Series Analysis Lab | Lab (Statistics Major) | 2 | Time Series Plotting, Model Identification, Forecasting using R/Python, Seasonality Decomposition, Trend Analysis |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCUOES601 | Open Elective | Open Elective (OE) | 3 | Interdisciplinary Projects, Specialized Skills, Advanced Concepts, Career-Focused Topics, Cross-Functional Learning |
| BCUCSC601 | Web Programming | Disciplinary Core (Computer Science Major) | 4 | Frontend Technologies, Backend Development, Database Integration, Web Frameworks (e.g., Flask/Django), Security in Web Applications |
| BCUCSC602P | Web Programming Lab | Lab (Computer Science Major) | 2 | Building Dynamic Websites, API Development, Database Connectivity, Deployment to Servers, Frontend Libraries (e.g., React/Angular) |
| BCUCSC603 | Operating System | Disciplinary Core (Computer Science Major) | 4 | OS Structures, Process Management, Memory Management, File Systems, Deadlocks |
| BCUCSC604P | Operating System Lab | Lab (Computer Science Major) | 2 | Shell Scripting, Process Synchronization, Memory Allocation Schemes, System Calls, OS Simulation |
| BCUDAS601 | Deep Learning | Disciplinary Core (Data Science Major) | 4 | Neural Network Architectures, Backpropagation, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), TensorFlow/Keras |
| BCUDAS602P | Deep Learning Lab | Lab (Data Science Major) | 2 | Building ANNs, Image Classification, Sequence Modeling, Hyperparameter Tuning, GPU Acceleration |
| BCUDAS603 | Natural Language Processing | Disciplinary Core (Data Science Major) | 4 | Text Preprocessing, Tokenization and Stemming, Named Entity Recognition, Sentiment Analysis, Topic Modeling |
| BCUDAS604P | Natural Language Processing Lab | Lab (Data Science Major) | 2 | NLTK/SpaCy Usage, Text Classification, Information Extraction, Chatbot Development, Text Summarization |
| BCUSTS601 | Econometrics | Disciplinary Core (Statistics Major) | 4 | Linear Regression Models, Violations of Assumptions, Time Series Econometrics, Panel Data Models, Forecasting in Economics |
| BCUSTS602P | Econometrics Lab | Lab (Statistics Major) | 2 | Regression Analysis with R/Python, Hypothesis Testing in Econometrics, Model Diagnostics, Forecasting Economic Data, Software for Econometrics |
| BCUSTS603 | Actuarial Statistics | Disciplinary Core (Statistics Major) | 4 | Life Contingencies, Survival Models, Risk Theory, Pension Fund Mathematics, Insurance Premium Calculation |
| BCUSTS604P | Actuarial Statistics Lab | Lab (Statistics Major) | 2 | Mortality Table Analysis, Life Insurance Calculations, Annuity Valuations, Risk Measurement, Actuarial Software |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCUHON701 | Research Methodology / Dissertation Part-I | Honours Core (Common for all Specializations) | 5 | Research Design, Data Collection Methods, Literature Review, Academic Writing, Ethics in Research |
| BCUCSC701 | Cloud Computing | Disciplinary Core (Computer Science Major) | 4 | Cloud Architecture, Virtualization Technologies, Cloud Security, AWS/Azure Services, Cloud Deployment Strategies |
| BCUCSC702P | Cloud Computing Lab | Lab (Computer Science Major) | 2 | Cloud Service Configuration, Virtual Machine Deployment, Containerization (Docker), Cloud Storage, Serverless Computing |
| BCUCSC703 | Software Engineering | Disciplinary Core (Computer Science Major) | 4 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Testing and Quality Assurance, Project Management |
| BCUCSC704P | Software Engineering Lab | Lab (Computer Science Major) | 2 | UML Diagrams, Software Project Planning, Testing Frameworks, Version Control (Git), Agile Methodologies |
| BCUCSC705 | Cryptography and Network Security / Internet of Things (IoT) | Discipline Specific Elective (Computer Science Major) | 4 | Symmetric Key Cryptography, Asymmetric Key Cryptography, Digital Signatures, Network Security Protocols, IoT Architecture |
| BCUCSC706 | Mobile Application Development / Big Data Analytics with Hadoop | Discipline Specific Elective (Computer Science Major) | 4 | Android/iOS Development, UI/UX Design for Mobile, Mobile App Security, Hadoop Ecosystem, Data Storage in HDFS |
| BCUDAS701 | Predictive Modeling | Disciplinary Core (Data Science Major) | 4 | Advanced Regression, Ensemble Methods, Forecasting Techniques, Survival Analysis, Model Interpretability |
| BCUDAS702P | Predictive Modeling Lab | Lab (Data Science Major) | 2 | Advanced Sklearn, Custom Model Building, Model Deployment, A/B Testing, Feature Selection |
| BCUDAS703 | Data Visualization Techniques | Disciplinary Core (Data Science Major) | 4 | Principles of Visualization, Interactive Dashboards, Geospatial Visualization, Time Series Visualization, Big Data Visualization Tools |
| BCUDAS704P | Data Visualization Techniques Lab | Lab (Data Science Major) | 2 | Tableau/Power BI, D3.js Basics, Advanced Matplotlib/Seaborn, Infographic Design, Dashboard Creation |
| BCUDAS705 | Social Network Analysis / Reinforcement Learning | Discipline Specific Elective (Data Science Major) | 4 | Network Metrics, Community Detection, Link Prediction, Markov Decision Processes, Q-Learning |
| BCUDAS706 | Time Series Forecasting / Bayesian Methods | Discipline Specific Elective (Data Science Major) | 4 | ARIMA and SARIMA, Exponential Smoothing, Bayes'''' Theorem, Prior and Posterior Distributions, MCMC Methods |
| BCUSTS701 | Design of Experiments | Disciplinary Core (Statistics Major) | 4 | Basic Principles of DOE, Completely Randomized Design, Randomized Block Design, Factorial Experiments, Analysis of Variance |
| BCUSTS702P | Design of Experiments Lab | Lab (Statistics Major) | 2 | Experimental Design Software, ANOVA Calculation, Treatment Comparison, Effect Size Estimation, Interpretation of Results |
| BCUSTS703 | Multivariate Analysis | Disciplinary Core (Statistics Major) | 4 | Multivariate Normal Distribution, Principal Component Analysis, Factor Analysis, Cluster Analysis, Discriminant Analysis |
| BCUSTS704P | Multivariate Analysis Lab | Lab (Statistics Major) | 2 | PCA Implementation, Factor Analysis Software, Clustering Algorithms, Manova Analysis, Multivariate Data Visualization |
| BCUSTS705 | Survival Analysis / Generalized Linear Models | Discipline Specific Elective (Statistics Major) | 4 | Censoring and Truncation, Kaplan-Meier Estimator, Cox Proportional Hazards Model, Exponential Family, Link Functions |
| BCUSTS706 | Data Mining for Statisticians / Statistical Quality Control | Discipline Specific Elective (Statistics Major) | 4 | Regression Trees, Random Forests, Control Charts, Acceptance Sampling, Process Capability |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| BCUHON801 | Project Work / Dissertation Part-II | Honours Core (Common for all Specializations) | 10 | Research Project Execution, Data Analysis and Interpretation, Dissertation Writing, Presentation Skills, Independent Research |
| BCUHON802 | Internship / Elective | Honours Elective (Common for all Specializations) | 5 | Industry Exposure, Practical Skill Application, Professional Networking, Workplace Ethics, Specialized Skill Development |
| BCUCSC801 | Artificial Intelligence | Disciplinary Core (Computer Science Major) | 4 | Intelligent Agents, Search Algorithms, Knowledge Representation, Machine Learning Principles, Expert Systems |
| BCUCSC802P | Artificial Intelligence Lab | Lab (Computer Science Major) | 2 | Prolog/Python AI Tools, Game Playing AI, Constraint Satisfaction Problems, Logical Reasoning Implementation, Pathfinding Algorithms |
| BCUCSC803 | Distributed Computing | Disciplinary Core (Computer Science Major) | 4 | Distributed System Models, Inter-process Communication, Distributed File Systems, Consistency and Replication, Fault Tolerance |
| BCUCSC804P | Distributed Computing Lab | Lab (Computer Science Major) | 2 | RPC/RMI Implementation, Message Passing, Distributed Transaction, Cloud-based Distributed Apps, Concurrency Control |
| BCUDAS801 | Advanced Analytics | Disciplinary Core (Data Science Major) | 4 | Causal Inference, A/B Testing Advanced, Survival Analysis, Network Analytics, Optimization Techniques |
| BCUDAS802P | Advanced Analytics Lab | Lab (Data Science Major) | 2 | Advanced Statistical Software, Simulation Modeling, Causal Model Building, Experiment Design, Analytics Project |
| BCUDAS803 | Big Data Management | Disciplinary Core (Data Science Major) | 4 | Data Lake Architecture, Data Governance, Stream Processing, NoSQL Databases Advanced, Data Security in Big Data |
| BCUDAS804P | Big Data Management Lab | Lab (Data Science Major) | 2 | Kafka/Spark Streaming, Cloud Big Data Services, Data Warehousing Concepts, Data Quality Checks, Distributed File Systems |
| BCUSTS801 | Demography | Disciplinary Core (Statistics Major) | 4 | Measures of Fertility, Measures of Mortality, Life Tables, Population Projection, Migration Analysis |
| BCUSTS802P | Demography Lab | Lab (Statistics Major) | 2 | Demographic Data Analysis, Population Pyramid, Fertility Rate Calculation, Mortality Rate Estimation, Demographic Software |
| BCUSTS803 | Clinical Trials | Disciplinary Core (Statistics Major) | 4 | Phases of Clinical Trials, Randomization Methods, Blinding Techniques, Sample Size Calculation, Ethical Considerations |
| BCUSTS804P | Clinical Trials Lab | Lab (Statistics Major) | 2 | Clinical Data Management, Statistical Analysis of Trial Data, Survival Analysis in Trials, Reporting Guidelines, Software for Clinical Statistics |
