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BCA in Data Science at B. S. Abdur Rahman Crescent Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Data Science at B. S. Abdur Rahman Crescent Institute of Science and Technology Chengalpattu?
This Data Science program at B.S. Abdur Rahman Crescent Institute of Science and Technology focuses on equipping students with essential skills in data analysis, machine learning, and big data technologies. It aligns with the burgeoning demand for data professionals in the Indian industry, preparing graduates to extract insights from complex datasets. The program differentiates itself by integrating practical lab experiences and core theoretical concepts relevant to real-world applications and challenges.
Who Should Apply?
This program is ideal for fresh graduates with a background in mathematics or computer science seeking entry into the high-demand field of data science. It also caters to working professionals aiming to upskill their analytical capabilities or career changers transitioning to data-driven roles. Individuals passionate about statistics, programming, and problem-solving through data will find this specialization particularly rewarding and relevant for their career growth.
Why Choose This Course?
Graduates of this program can expect promising career paths as Data Analysts, Machine Learning Engineers, Business Intelligence Developers, or Big Data Specialists in India. Entry-level salaries typically range from INR 3.5 to 6 LPA, with significant growth trajectories for experienced professionals reaching INR 10-25 LPA. The curriculum prepares students for industry certifications and provides a strong foundation for advanced studies or research in data science, ensuring strong professional development.
Student Success Practices
Foundation Stage
Master Programming and Math Fundamentals- (Semester 1-2)
Dedicate significant time to understanding core programming concepts (C, C++, Data Structures) and discrete mathematics/probability. These form the bedrock for advanced data science topics. Practice coding daily on platforms and solve problems consistently.
Tools & Resources
HackerRank, GeeksforGeeks, CodeChef, NPTEL courses, Khan Academy
Career Connection
Strong fundamentals are crucial for cracking technical interviews and building efficient, robust data science solutions in professional settings.
Develop Strong Problem-Solving Skills- (Semester 1-2)
Actively participate in problem-solving sessions and coding contests. Focus on breaking down complex challenges into smaller, manageable parts. Discuss various approaches with peers and faculty to enhance analytical thinking and logical reasoning abilities.
Tools & Resources
Online competitive programming platforms, College coding clubs, Peer study groups
Career Connection
Essential for analytical roles, logical thinking, and designing effective algorithms for data processing challenges faced in the industry.
Build a Foundational Project Portfolio- (Semester 2 (end))
Start simple programming projects to apply learned concepts. Even small projects like a basic calculator, a data structure implementation, or a simple game can showcase early skill development and practical application of theoretical knowledge.
Tools & Resources
GitHub for version control, VS Code/Jupyter for development, Online tutorials for project ideas
Career Connection
Demonstrates initiative and practical application of knowledge, which is critical for securing internships and entry-level positions in tech companies.
Intermediate Stage
Gain Proficiency in Data Science Tools and Languages- (Semester 3-4)
Beyond theoretical knowledge, become highly proficient in Python (with libraries like Pandas, NumPy, Scikit-learn) and R. Master SQL for database interaction, and familiarize yourself with data visualization tools like Tableau or Power BI for effective data storytelling.
Tools & Resources
Kaggle for datasets and notebooks, DataCamp, Coursera, Official library documentation, SQLZoo
Career Connection
Directly matches the skill requirements for Data Analyst, Data Scientist, and Machine Learning Engineer roles in the Indian job market.
Engage in Real-World Data Projects and Internships- (Semester 4-5 (during breaks and alongside coursework))
Actively seek out internships in data science roles, even unpaid ones, to gain practical experience. Work on end-to-end data science projects, from data collection and cleaning to model deployment and visualization, solving real-world business problems.
Tools & Resources
LinkedIn, Internshala, College placement cell, Industry mentorship programs
Career Connection
Builds a strong portfolio, provides invaluable networking opportunities, and often leads to pre-placement offers from reputable companies.
Participate in Data Science Competitions and Workshops- (Semester 3-5)
Join Kaggle competitions, hackathons, and local data science meetups. This sharpens your skills, exposes you to diverse problems, and helps build a professional network. Attend workshops on emerging data science technologies to stay updated.
Tools & Resources
Kaggle, Analytics Vidhya, Local tech communities, College-organized events
Career Connection
Showcases initiative, problem-solving under pressure, and a commitment to continuous learning to potential employers, enhancing your profile.
Advanced Stage
Develop a Capstone Project with Industry Relevance- (Semester 6)
Focus your final year project on solving a real-world problem, ideally in collaboration with an industry partner or addressing a known industry gap. This should be a comprehensive, full-stack data science solution demonstrating your acquired expertise.
Tools & Resources
Industry contacts, Academic supervisors, Latest research papers, Advanced data science frameworks
Career Connection
A well-executed capstone project is a powerful resume booster and interview talking point, demonstrating readiness for demanding industry roles.
Prepare for Placements and Professional Certifications- (Semester 5 (end) - Semester 6)
Thoroughly prepare for placement interviews, focusing on data science concepts, case studies, and coding challenges. Consider pursuing relevant professional certifications (e.g., AWS, Google Cloud, Microsoft Azure data specialties) to validate your specialized skills.
Tools & Resources
Mock interviews, Resume workshops, Online certification courses (Coursera, edX), Interview prep books
Career Connection
Increases employability, validates specialized skills, and provides a significant competitive edge in the Indian and global job markets.
Build and Showcase a Public Data Science Portfolio- (Throughout program, finalized in Semester 6)
Create an online portfolio (e.g., personal website, GitHub repository, Kaggle profile) showcasing your best projects, including clean code, insightful visualizations, and detailed explanations of methodologies. This is your digital resume.
Tools & Resources
GitHub Pages, Medium/Towards Data Science for project write-ups, LinkedIn profile optimization
Career Connection
Serves as a tangible demonstration of your skills and experience to recruiters and hiring managers, greatly increasing your visibility and appeal.
Program Structure and Curriculum
Eligibility:
- A pass in 10+2 (HSC) or equivalent examination from a recognized Board with Mathematics/Business Mathematics/Computer Science/Statistics as one of the subjects.
Duration: 3 years / 6 semesters
Credits: 133 Credits
Assessment: Internal: 40% (for theory courses) / 50% (for practical and project courses), External: 60% (for theory courses) / 50% (for practical and project courses)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HSB 1181 | Professional English I | Core | 3 | Language and Communication, Reading Comprehension, Writing Skills, Presentation Skills, Vocabulary Building |
| MAB 1181 | Discrete Mathematics | Core | 4 | Logic and Proofs, Set Theory, Relations and Functions, Graph Theory, Algebraic Structures |
| CAB 1101 | C Programming | Core | 3 | C Fundamentals, Control Structures, Functions, Arrays and Strings, Pointers, Structures and Unions |
| CAB 1102 | Digital Principles | Core | 3 | Number Systems, Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits |
| CAB 1103 | C Programming Lab | Lab | 2 | Basic C Programs, Conditional Statements, Looping Constructs, Function Implementation, Array and String Operations |
| CAB 1104 | Digital Principles Lab | Lab | 2 | Logic Gate Verification, Boolean Function Implementation, Adders and Subtractors, Flip-Flops, Counters and Registers |
| CAB 1105 | Office Automation Lab | Lab | 2 | Word Processing, Spreadsheet Applications, Presentation Software, Database Management Basics, Internet and Email Usage |
| XCB 1101 | Value Education | Core (Mandatory Non-Credit) | 0 | Human Values, Professional Ethics, Social Responsibility, Environmental Awareness, Stress Management |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HSB 1282 | Professional English II | Core | 3 | Advanced Reading Skills, Technical Writing, Public Speaking, Group Discussion, Professional Communication Strategies |
| MAB 1282 | Probability and Statistics | Core | 4 | Basic Probability Theory, Random Variables, Probability Distributions, Sampling Theory, Hypothesis Testing |
| CAB 1201 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees, Graphs, Sorting and Searching Algorithms |
| CAB 1202 | Object Oriented Programming using C++ | Core | 3 | OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Exception Handling, File Handling |
| CAB 1203 | Operating Systems | Core | 3 | OS Functions and Types, Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| CAB 1204 | Data Structures Lab | Lab | 2 | Array and Linked List Operations, Stack and Queue Implementation, Tree Traversal Algorithms, Graph Representations, Sorting and Searching Programs |
| CAB 1205 | Object Oriented Programming using C++ Lab | Lab | 2 | Class and Object Creation, Constructor and Destructor, Operator Overloading, Function Overloading, Inheritance Implementation |
| XCB 1202 | Environmental Science | Core (Mandatory Non-Credit) | 0 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Waste Management, Sustainable Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HSB 2181 | Islamic Studies / Ethics and Culture | Core | 2 | Islamic Teachings, Moral Principles, Cultural Diversity, Human Rights, Ethical Dilemmas |
| CAB 2101 | Computer Networks | Core | 3 | Network Topologies, OSI and TCP/IP Models, Data Link Layer, Network Layer, Transport Layer, Application Layer Protocols |
| CAB 2102 | Database Management Systems | Core | 3 | DBMS Architecture, ER Model, Relational Model, SQL Queries, Normalization, Transaction Management |
| CAB 2103 | Python Programming | Core | 3 | Python Basics, Data Types and Structures, Functions and Modules, File I/O, Object Oriented Programming, Exception Handling |
| DAB 2101 | Introduction to Data Science | Core (Specialization) | 4 | Data Science Life Cycle, Data Collection and Cleaning, Exploratory Data Analysis, Data Visualization Fundamentals, Introduction to Machine Learning, Statistical Thinking for Data Science |
| CAB 2104 | Database Management Systems Lab | Lab | 2 | SQL DDL Commands, SQL DML Commands, Joins and Subqueries, Stored Procedures, Triggers and Cursors |
| CAB 2105 | Python Programming Lab | Lab | 2 | Basic Python Scripts, List, Tuple, Dictionary Operations, Function Definition and Call, File Handling, Classes and Objects in Python |
| DAB 2102 | Data Science Lab I (R Programming) | Lab (Specialization) | 2 | R Basics and Data Types, Data Structures in R, Data Import and Export, Data Manipulation with Dplyr, Data Visualization with Ggplot2, Basic Statistical Analysis in R |
| GEC 1 | Basic Electrical and Electronics Engineering (Example Generic Elective) | Generic Elective | 2 | Basic Circuits, Semiconductor Devices, Diodes and Rectifiers, Transistors, Amplifiers, Digital Logic |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| HSB 2282 | Professional Communication | Core | 3 | Verbal Communication, Non-Verbal Communication, Business Correspondence, Report Writing, Presentation Skills, Interpersonal Skills |
| CAB 2201 | Web Programming | Core | 3 | HTML and CSS, JavaScript Fundamentals, DOM Manipulation, Web Servers, PHP Basics, Database Connectivity |
| DAB 2201 | Machine Learning | Core (Specialization) | 4 | Supervised Learning, Unsupervised Learning, Regression Algorithms, Classification Algorithms, Clustering Techniques, Model Evaluation and Selection |
| DAB 2202 | Big Data Analytics | Core (Specialization) | 4 | Big Data Characteristics, Hadoop Ecosystem, HDFS, MapReduce, Spark, NoSQL Databases |
| CAB 2202 | Web Programming Lab | Lab | 2 | HTML Page Design, CSS Styling and Layouts, JavaScript Interactive Elements, PHP Form Processing, Database Integration with PHP |
| DAB 2203 | Data Science Lab II (Python for Data Science) | Lab (Specialization) | 2 | Numpy for Numerical Operations, Pandas for Data Manipulation, Matplotlib and Seaborn for Visualization, Scikit-learn for Machine Learning, Data Preprocessing Techniques |
| EEC 1 | Quantitative Aptitude (Example Employability Enhancement Course) | Employability Enhancement Course | 2 | Numerical Ability, Logical Reasoning, Data Interpretation, Verbal Ability, Problem Solving Strategies |
| XCB 2203 | Foreign Language / Professional Certification / Internship / Skill Development | Mandatory Non-Credit | 0 | Foundational Language Skills, Industry-Specific Skill Training, Workplace Experience, Certification Exam Preparation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DAB 3101 | Deep Learning | Core (Specialization) | 4 | Neural Network Architectures, Backpropagation Algorithm, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Learning Frameworks (TensorFlow/Keras), Applications of Deep Learning |
| DAB 3102 | Data Visualization | Core (Specialization) | 4 | Principles of Data Visualization, Chart Types and Selection, Dashboard Design, Storytelling with Data, Interactive Visualizations, Data Visualization Tools (Tableau/Power BI) |
| PE 1 | Natural Language Processing (Example Programme Elective I) | Programme Elective (Specialization) | 3 | Text Preprocessing, Tokenization and Stemming, Word Embeddings (Word2Vec, GloVe), Sentiment Analysis, Text Classification, Sequence Models |
| PE 2 | Cloud Computing (Example Programme Elective II) | Programme Elective (Specialization) | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Virtualization Technology, Cloud Security, AWS/Azure Services Overview, Serverless Computing |
| DAB 3103 | Deep Learning Lab | Lab (Specialization) | 2 | Building ANNs with Keras, Implementing CNNs for Image Classification, Working with RNNs for Sequence Data, Model Training and Hyperparameter Tuning, Transfer Learning Applications |
| DAB 3104 | Data Visualization Lab | Lab (Specialization) | 2 | Creating Basic Charts (Bar, Line, Scatter), Designing Interactive Dashboards, Using Tableau/Power BI for Data Exploration, Advanced Charting Techniques, Visualizing Geospatial Data |
| EEC 2 | Campus to Corporate (Example Employability Enhancement Course) | Employability Enhancement Course | 2 | Resume Building, Interview Preparation, Group Discussion Techniques, Corporate Etiquette, Professional Networking |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DAB 3201 | Data Mining Techniques | Core (Specialization) | 4 | Data Preprocessing for Mining, Classification Algorithms, Clustering Algorithms, Association Rule Mining, Anomaly Detection, Data Warehousing Concepts |
| DAB 3202 | Project Work | Project | 16 | Problem Identification and Scope Definition, Literature Review, System Design and Architecture, Implementation and Testing, Data Analysis and Interpretation, Project Report and Presentation |
