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B-SC-DATA-SCIENCE in General at Sri Ramachandra Institute of Higher Education and Research
Chennai, Tamil Nadu
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About the Specialization
What is General at Sri Ramachandra Institute of Higher Education and Research Chennai?
This B.Sc. Data Science program at Sri Ramachandra Institute of Higher Education and Research focuses on equipping students with a robust foundation in statistics, programming, and machine learning essential for the rapidly expanding data industry in India. The curriculum is designed to meet the growing demand for skilled data professionals, integrating theoretical knowledge with practical applications to solve real-world problems. It emphasizes a blend of core computing skills and advanced analytical techniques.
Who Should Apply?
This program is ideal for fresh graduates from science backgrounds (10+2 with Physics, Chemistry, Biology/Computer Science/Mathematics) seeking an entry into the burgeoning field of data science. It also caters to individuals with an aptitude for logical reasoning and problem-solving, looking to build a career in data analysis, machine learning engineering, or business intelligence. The program prepares students for roles demanding strong analytical and computational skills.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including Data Analyst, Business Intelligence Developer, Machine Learning Engineer, and Data Scientist in sectors like finance, healthcare, e-commerce, and IT services. Entry-level salaries typically range from INR 3.5-6 LPA, potentially rising to INR 8-15 LPA with experience. The program aligns with industry-recognized skills, enabling growth into senior analytical or management roles within Indian and global companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consolidate strong programming logic in C and Python. Regularly practice coding problems on platforms like HackerRank or LeetCode to build problem-solving muscle memory. Focus on understanding data structures and algorithms deeply.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation
Career Connection
Essential for passing technical rounds in placements, forms the base for all advanced data science topics.
Build a Strong Mathematical Core- (Semester 1-2)
Pay close attention to Calculus, Linear Algebra, Probability, and Statistics. These are the bedrock of machine learning. Solve textbook problems diligently and use online resources like Khan Academy for conceptual clarity.
Tools & Resources
Khan Academy, NPTEL courses, Standard textbooks
Career Connection
Crucial for understanding ML algorithms, model interpretation, and advanced research in data science.
Engage in Peer Learning & Study Groups- (Semester 1-2)
Form study groups with peers to discuss complex topics, solve problems together, and prepare for exams. Teaching others reinforces your own understanding and exposes you to different perspectives.
Tools & Resources
Collaborative online whiteboards, Peer-to-peer discussions, Group projects
Career Connection
Enhances teamwork, communication skills, and critical thinking, all vital for collaborative industry environments.
Intermediate Stage
Develop Project-Based Learning- (Semester 3-5)
Apply theoretical knowledge from DBMS, Java, AI, and Data Mining to small-scale projects. Build a portfolio of projects on platforms like GitHub, focusing on real-world datasets and problems.
Tools & Resources
GitHub, Kaggle datasets, Jupyter Notebooks, MySQL, MongoDB
Career Connection
Demonstrates practical skills to recruiters, provides talking points for interviews, and builds a professional online presence.
Seek Early Industry Exposure & Internships- (Semester 4-5)
Actively look for summer internships or part-time projects in startups or local companies, even if unpaid initially. This provides invaluable hands-on experience and networking opportunities.
Tools & Resources
LinkedIn, Internshala, College placement cell, Industry networking events
Career Connection
Converts theoretical knowledge into practical skills, provides professional references, and often leads to pre-placement offers.
Participate in Hackathons & Competitions- (Semester 3-5)
Test your skills by participating in data science hackathons and coding competitions on platforms like Kaggle, Analytics Vidhya, or local college events. This builds competitive spirit and problem-solving under pressure.
Tools & Resources
Kaggle, Analytics Vidhya, Local college tech fests
Career Connection
Showcases ability to work under pressure, innovate, and apply skills to novel problems; often noticed by recruiters.
Advanced Stage
Specialize and Deepen Skill Set- (Semester 6)
Focus on a chosen area within Data Science (e.g., Deep Learning, NLP, Big Data, IoT) based on electives. Pursue advanced online courses or certifications in these specialized fields to gain an edge.
Tools & Resources
Coursera, edX, NPTEL advanced courses, TensorFlow/PyTorch certifications
Career Connection
Positions you as an expert in a niche, making you more attractive for specialized roles and higher initial salaries.
Prioritize Major Project for Portfolio- (Semester 6)
Treat the final year major project as a capstone experience. Choose a challenging, industry-relevant problem and aim for a high-quality deliverable. Document it meticulously and be ready to present it thoroughly.
Tools & Resources
Latest frameworks (e.g., PyTorch, Spark), Cloud platforms (AWS, GCP, Azure), Research papers
Career Connection
The major project is often the most significant part of your portfolio, demonstrating complete project lifecycle management and advanced technical skills.
Intensive Placement Preparation- (Semester 6)
Begin mock interviews, aptitude test practice, and resume building well in advance. Network with alumni and industry professionals to understand current hiring trends and company expectations.
Tools & Resources
Mock interview platforms, LinkedIn, Campus placement cells, Career counseling services
Career Connection
Ensures readiness for the recruitment process, increases confidence, and maximizes chances of securing desirable job offers.
Program Structure and Curriculum
Eligibility:
- H.Sc. pass from a recognized board/council with Physics, Chemistry, Biology / Computer Science / Mathematics as major subjects.
Duration: 3 years (6 semesters)
Credits: 132 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SDSBSC101 | Professional English for Science & Technology | Core | 4 | Language skills, Technical communication, Report writing, Presentation skills, Research paper analysis |
| SDSBSC102 | Introduction to Computer Fundamentals | Core | 4 | Computer components, Operating systems, Software concepts, Internet basics, Data representation |
| SDSBSC103 | Calculus & Linear Algebra | Core | 4 | Derivatives, Integrals, Matrices, Vectors, Eigenvalues and Eigenvectors |
| SDSBSC104 | Programming in C | Core | 4 | C language basics, Control flow, Functions, Arrays, Pointers |
| SDSBSL105 | Computer Fundamentals Lab | Lab | 2 | Operating system commands, MS Office tools, Internet browsing, File management |
| SDSBSL106 | C Programming Lab | Lab | 2 | C program implementation, Data structures using C, Debugging, Algorithm development |
| SDSBSC107 | Value Education | Core | 0 | Human values, Ethics, Social responsibility, Environmental awareness, Personality development |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SDSBSC201 | Probability & Statistics | Core | 4 | Probability theory, Random variables, Distributions, Hypothesis testing, Regression |
| SDSBSC202 | Data Structures & Algorithms | Core | 4 | Arrays, Linked lists, Stacks, Queues, Trees, Graph algorithms |
| SDSBSC203 | Python Programming | Core | 4 | Python syntax, Data types, Control flow, Functions, Modules, OOP in Python |
| SDSBSC204 | Discrete Mathematics for Data Science | Core | 4 | Set theory, Logic, Relations, Functions, Graph theory, Combinatorics |
| SDSBSL205 | Data Structures & Algorithms Lab | Lab | 2 | Implementing data structures, Algorithm analysis, Sorting, Searching |
| SDSBSL206 | Python Programming Lab | Lab | 2 | Python script writing, Data manipulation, Libraries (Numpy, Pandas basics) |
| SDSBSC207 | Environmental Studies | Core | 0 | Ecosystems, Pollution, Natural resources, Biodiversity, Sustainable development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SDSBSC301 | Database Management Systems | Core | 4 | Relational model, SQL queries, Normalization, Transactions, Concurrency control |
| SDSBSC302 | Operating Systems | Core | 4 | OS functions, Process management, Memory management, File systems, Deadlocks |
| SDSBSC303 | Object Oriented Programming with Java | Core | 4 | OOP concepts, Java syntax, Classes, Objects, Inheritance, Polymorphism, Exception handling |
| SDSBSC304 | Data Warehousing & Data Mining | Core | 4 | Data warehousing concepts, OLAP, Data mining tasks, Association rules, Classification, Clustering |
| SDSBSL305 | DBMS Lab | Lab | 2 | SQL queries, Database design, PL/SQL, Report generation |
| SDSBSL306 | Java Programming Lab | Lab | 2 | Java program development, GUI programming, Database connectivity |
| SDSBSC307 | Foreign Language (French/German/Japanese) | Elective | 3 | Basic grammar, Conversational skills, Reading comprehension, Writing practice, Cultural aspects |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SDSBSC401 | Computer Networks | Core | 4 | Network models (OSI, TCP/IP), Data link layer, Network layer, Transport layer, Application layer |
| SDSBSC402 | Research Methodology & IPR | Core | 4 | Research design, Data collection, Statistical analysis, Report writing, Intellectual property rights |
| SDSBSC403 | Artificial Intelligence | Core | 4 | AI concepts, Problem solving, Search algorithms, Knowledge representation, Machine learning basics |
| SDSBSC404 | Web Technology | Core | 4 | HTML, CSS, JavaScript, Web servers, Front-end frameworks, Backend scripting |
| SDSBSL405 | AI Lab | Lab | 2 | Implementing search algorithms, AI problem solving, Logic programming |
| SDSBSL406 | Web Technology Lab | Lab | 2 | Web page design, Dynamic content, Client-side scripting, Server-side integration |
| SDSBSC407 | Soft Skills for Professionals | Core | 3 | Communication skills, Teamwork, Leadership, Time management, Interview skills |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SDSBSC501 | Big Data Analytics | Core | 4 | Big Data concepts, Hadoop ecosystem, MapReduce, Spark, NoSQL databases, Data streaming |
| SDSBSC502 | Machine Learning | Core | 4 | Supervised learning, Unsupervised learning, Regression, Classification, Clustering, Model evaluation |
| SDSBSC503 | Natural Language Processing | Core | 4 | Text processing, Tokenization, Part-of-speech tagging, Semantic analysis, Machine translation |
| SDSBSC504 | Elective I (Computer Graphics / Optimization Techniques / Cloud Computing) | Elective | 3 | Cloud models, Virtualization, Cloud security, AWS/Azure basics, Cloud storage |
| SDSBSC505 | Elective II (E-commerce / Data Visualization / Information Security) | Elective | 3 | Visualization principles, Chart types, Tableau/Power BI basics, Interactive dashboards, Storytelling with data |
| SDSBSL506 | Big Data Analytics Lab | Lab | 2 | Hadoop implementation, MapReduce programming, Spark applications, NoSQL operations |
| SDSBSL507 | Machine Learning Lab | Lab | 2 | Implementing ML algorithms, Model training, Scikit-learn, TensorFlow/Keras basics |
| SDSBSP508 | Mini Project | Project | 2 | Problem identification, Literature review, Design and Implementation, Testing and Evaluation, Project Reporting |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| SDSBSC601 | Deep Learning | Core | 4 | Neural networks, Activation functions, CNNs, RNNs, LSTMs, Transfer learning |
| SDSBSC602 | IoT & Edge Computing | Core | 4 | IoT architecture, Sensors, Actuators, Communication protocols, Edge computing, IoT security |
| SDSBSC603 | Elective III (Augmented Reality/Virtual Reality / Financial Technology / Geospatial Data Science) | Elective | 3 | GIS concepts, Spatial data types, Geocoding and Geoprocessing, Mapping tools, Remote sensing |
| SDSBSC604 | Elective IV (Bio Informatics / Blockchain Technology / Robotics Process Automation) | Elective | 3 | Blockchain fundamentals, Cryptography, Consensus mechanisms, Smart contracts, Bitcoin/Ethereum |
| SDSBSL605 | Deep Learning Lab | Lab | 2 | Implementing CNNs/RNNs, Frameworks (TensorFlow/PyTorch), Model optimization, Image/Text analysis, Generative models |
| SDSBSP606 | Major Project | Project | 6 | Project planning, System design, Implementation and Testing, Documentation, Presentation and Viva |
