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B-SC in Data Science at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Data Science at SRM Institute of Science and Technology Chengalpattu?
This B.Sc. Data Science program at SRM Institute of Science and Technology focuses on equipping students with core competencies in data analysis, machine learning, and artificial intelligence, crucial for the rapidly evolving Indian tech landscape. It integrates rigorous theoretical foundations with practical application, distinguishing itself through a blend of statistics, programming, and advanced analytics, addressing the significant demand for skilled data professionals across various Indian industries.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics and computing, seeking entry into high-growth data roles. It also serves as an excellent foundation for individuals looking to embark on a career in data science, including those from related fields aiming to specialize. Aspiring data analysts, machine learning engineers, and data scientists looking for a comprehensive, industry-relevant curriculum are particularly well-suited for this program.
Why Choose This Course?
Graduates of this program can expect to secure roles such as Junior Data Scientist, Data Analyst, Machine Learning Engineer, or Business Intelligence Analyst in prominent Indian and multinational companies. Entry-level salaries typically range from INR 4-7 lakhs per annum, with significant growth trajectories for experienced professionals reaching INR 15-25 lakhs or more. The curriculum also aligns with the skills required for global certifications in data science and cloud platforms.
Student Success Practices
Foundation Stage
Master Programming Fundamentals with Python- (Semester 1-2)
Dedicate consistent time to mastering Python programming syntax, data structures, and core libraries like NumPy and Pandas. Participate in weekly coding challenges and practice problems to solidify understanding and build problem-solving abilities.
Tools & Resources
HackerRank, LeetCode, DataCamp Python courses, Official Python Documentation
Career Connection
Strong Python skills are the bedrock for almost all data science roles in India, directly impacting eligibility for internships and entry-level positions requiring data manipulation and scripting.
Build a Robust Statistical and Mathematical Base- (Semester 1-2)
Focus intensely on understanding statistical concepts, linear algebra, and calculus taught in the initial semesters. Utilize online resources, solve textbook problems rigorously, and seek clarifications from faculty to build a strong theoretical foundation.
Tools & Resources
Khan Academy, MIT OpenCourseWare for Linear Algebra/Calculus, NPTEL lectures
Career Connection
A solid grasp of mathematics and statistics is critical for understanding the mechanics of machine learning algorithms and interpreting data effectively, a highly valued trait by Indian analytics firms.
Engage in Peer Learning and Collaborative Projects- (Semester 1-2)
Form study groups with classmates to discuss complex topics, share insights, and work on small projects together. Collaborative learning enhances understanding and develops teamwork skills, crucial for industry projects.
Tools & Resources
GitHub for collaborative coding, Google Docs for shared notes, Discord for group communication
Career Connection
Effective teamwork and communication are essential soft skills sought by Indian employers, particularly in project-driven data science teams, improving employability and project delivery success.
Intermediate Stage
Apply Machine Learning & Database Skills to Mini-Projects- (Semester 3-5)
Actively seek opportunities to build end-to-end mini-projects using data science and database concepts. Start with publicly available datasets on platforms like Kaggle to practice data cleaning, model building, and SQL queries.
Tools & Resources
Kaggle, Google Colab, GitHub, SQL fiddle
Career Connection
Practical project experience is highly valued in the Indian job market, demonstrating problem-solving capabilities and the ability to apply theoretical knowledge, leading to better internship and placement offers.
Develop Data Visualization & Communication Proficiency- (Semester 3-5)
Go beyond basic plotting by learning advanced visualization tools and techniques. Focus on conveying insights clearly and effectively, practicing presentation skills regularly. Explore tools like Tableau or Power BI.
Tools & Resources
Tableau Public, Power BI Desktop, Storytelling with Data by Cole Nussbaumer Knaflic
Career Connection
The ability to visually communicate complex data insights is a critical skill for Data Analysts and Business Intelligence roles in India, enabling effective stakeholder communication and impacting business decisions.
Explore Cloud and Big Data Technologies- (Semester 3-5)
Gain hands-on experience with cloud platforms (AWS, Azure, GCP) and big data tools (Hadoop, Spark). Complete online certifications or tutorials to understand deployment, storage, and processing of large datasets.
Tools & Resources
AWS Free Tier, Microsoft Azure for Students, Coursera/edX courses on Big Data
Career Connection
Proficiency in cloud and big data ecosystems is increasingly essential for Data Scientists in Indian companies dealing with large-scale data, enhancing career prospects in advanced analytics and data engineering roles.
Advanced Stage
Undertake an Industry-Relevant Major Project- (Semester 6)
Collaborate with faculty or external organizations on a substantial major project that addresses a real-world data science problem. Focus on showcasing advanced skills, generating tangible outcomes, and preparing a professional report and presentation.
Tools & Resources
Industry partners of SRMIST, Research labs, open-source projects
Career Connection
A strong major project is often a key differentiator during placements, providing concrete evidence of advanced problem-solving, technical depth, and industry readiness to Indian recruiters.
Intensify Placement Preparation and Networking- (Semester 6)
Actively participate in placement training activities, mock interviews, and resume-building workshops organized by the institution. Network with alumni and industry professionals through LinkedIn and career fairs to explore opportunities.
Tools & Resources
SRMIST Placement Cell, LinkedIn, Glassdoor for interview prep
Career Connection
Proactive placement preparation and networking are crucial for securing desired job roles in competitive Indian job markets, leading to better offers and career starts.
Specialize in a Niche Area and Contribute to Open Source- (Semester 6)
Choose electives wisely to specialize in a specific data science domain like NLP, Computer Vision, or Reinforcement Learning. Contribute to open-source projects in your chosen niche to demonstrate expertise and learn from global communities.
Tools & Resources
GitHub, GitLab, Stack Overflow, Specialized online courses (e.g., fast.ai)
Career Connection
Specialized skills and open-source contributions make candidates highly attractive to Indian companies seeking expertise in emerging AI/ML fields, opening doors to advanced research and development roles.
Program Structure and Curriculum
Eligibility:
- A pass in 10+2 / HSC / CBSE / ICSE or equivalent examination with Physics, Chemistry and Mathematics / Computer Science / Biotechnology / Biology / Economics / Statistics / Psychology as subjects in XI and XII (Any two of them), with a minimum aggregate of 50%.
Duration: 3 years (6 semesters)
Credits: 154 (Minimum 140 credits required as per R2021 document) Credits
Assessment: Internal: 50% (Theory), 60% (Practical), 50% (Project), External: 50% (Theory), 40% (Practical), 50% (Project)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UDS21101 | Introduction to Data Science | Core | 4 | Data Science lifecycle, Types of data, Data preprocessing, Exploratory Data Analysis, Data Science applications, Basic machine learning concepts |
| UDS21102 | Python Programming for Data Science | Core | 4 | Python fundamentals, Data structures (lists, tuples, dictionaries), Functions and modules, File handling, NumPy for numerical operations, Pandas for data manipulation |
| UDS21103 | Statistics for Data Science | Core | 4 | Descriptive statistics, Probability distributions, Sampling techniques, Hypothesis testing, ANOVA, Correlation and regression |
| UDS21104 | Linear Algebra for Data Science | Core | 4 | Vectors and matrices, Matrix operations, Determinants, Eigenvalues and eigenvectors, Vector spaces, Linear transformations |
| UDS21105 | Python Programming for Data Science Lab | Lab | 2 | Python programming practice, Data manipulation with Pandas, Data visualization using libraries, Basic scripting for data tasks, Error handling, Debugging Python code |
| UDS21106 | Statistics for Data Science Lab | Lab | 2 | Statistical calculations using tools, Data analysis on real datasets, Hypothesis testing implementation, Regression analysis practice, Interpretation of statistical results, Data presentation |
| UDS21107 | English for Communication | Ability Enhancement Compulsory Course | 2 | Listening skills, Speaking for various contexts, Reading comprehension, Writing reports and essays, Vocabulary and grammar, Presentation skills |
| UDS21108 | Environmental Studies | Ability Enhancement Compulsory Course | 2 | Natural resources and their management, Ecosystems and biodiversity, Environmental pollution and control, Social issues and the environment, Human population and environment, Environmental ethics |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UDS21201 | Data Structures and Algorithms | Core | 4 | Arrays and linked lists, Stacks and queues, Trees and graphs, Sorting algorithms, Searching algorithms, Algorithm analysis (time, space complexity) |
| UDS21202 | Database Management Systems | Core | 4 | Relational model, SQL queries and operations, Database design (ER model), Normalization, Transaction management, Database security |
| UDS21203 | Calculus for Data Science | Core | 4 | Limits and continuity, Differentiation techniques, Applications of derivatives, Integration techniques, Multivariable calculus, Optimization in machine learning |
| UDS21204 | Machine Learning | Core | 4 | Introduction to machine learning, Supervised learning algorithms (Regression, Classification), Unsupervised learning (Clustering), Model evaluation metrics, Bias-variance tradeoff, Ensemble methods |
| UDS21205 | Data Structures and Algorithms Lab | Lab | 2 | Implementation of data structures, Algorithm design and analysis, Problem-solving using algorithms, Comparative study of algorithms, Debugging algorithms, Performance measurement |
| UDS21206 | Database Management Systems Lab | Lab | 2 | SQL query writing, Database creation and manipulation, Report generation using SQL, Stored procedures and functions, Database connectivity with programming languages, Mini-project on database design |
| UDS21207 | Professional Communication | Ability Enhancement Compulsory Course | 2 | Effective communication strategies, Public speaking and presentations, Interview skills, Group discussions, Resume and cover letter writing, Email etiquette |
| UDS21208 | Universal Human Values | Ability Enhancement Compulsory Course | 2 | Understanding human values, Harmony in self and family, Harmony in society, Professional ethics, Ethical dilemmas and solutions, Holistic development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UDS21301 | Object Oriented Programming | Core | 4 | Concepts of OOP (Encapsulation, Inheritance), Polymorphism and Abstraction, Classes and objects, Constructors and destructors, Exception handling, Object-oriented design principles |
| UDS21302 | Data Visualization | Core | 4 | Principles of effective visualization, Types of charts and graphs, Matplotlib and Seaborn libraries, Interactive visualization with Plotly, Dashboard design principles, Data storytelling |
| UDS21303 | Operating Systems | Core | 4 | Introduction to operating systems, Process management, CPU scheduling, Memory management techniques, File systems, I/O management |
| UDS21304 | Optimization Techniques | Core | 4 | Linear programming, Simplex method, Transportation and assignment problems, Non-linear programming, Gradient descent algorithms, Applications in machine learning |
| UDS21305 | Object Oriented Programming Lab | Lab | 2 | Implementation of OOP concepts, Building classes and objects, Inheritance and polymorphism exercises, File I/O in OOP context, Debugging OOP programs, Small OOP project development |
| UDS21306 | Data Visualization Lab | Lab | 2 | Creating static and interactive plots, Using Matplotlib and Seaborn, Developing dashboards, Visualizing complex datasets, Choosing appropriate visualizations, Tools like Tableau/PowerBI introduction |
| UDS213E1 | Program Elective I (Data Mining) | Elective | 4 | Data preprocessing for mining, Association rule mining, Classification techniques, Clustering algorithms, Outlier detection, Web mining and text mining |
| UDS21307 | General Elective I (Entrepreneurship Development) | Elective | 2 | Concept of entrepreneurship, Business idea generation, Market research, Business plan creation, Funding sources for startups, Legal aspects of business |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UDS21401 | Artificial Intelligence | Core | 4 | History and scope of AI, Problem-solving agents, Search algorithms (DFS, BFS, A*), Knowledge representation and reasoning, Expert systems, Machine learning as part of AI |
| UDS21402 | Cloud Computing | Core | 4 | Cloud service models (IaaS, PaaS, SaaS), Cloud deployment models, Virtualization technologies, Cloud storage and networking, AWS/Azure/GCP overview, Cloud security and management |
| UDS21403 | Big Data Analytics | Core | 4 | Introduction to Big Data, Hadoop ecosystem (HDFS, MapReduce), Spark for distributed processing, NoSQL databases (MongoDB, Cassandra), Data streaming concepts, Big Data tools and technologies |
| UDS21404 | Deep Learning | Core | 4 | Introduction to neural networks, Perceptrons and activation functions, Backpropagation algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep learning frameworks (TensorFlow, Keras) |
| UDS21405 | Big Data Analytics Lab | Lab | 2 | Hadoop installation and configuration, MapReduce programming, Spark programming, Hive and Pig for data warehousing, NoSQL database operations, Real-time data processing |
| UDS21406 | Deep Learning Lab | Lab | 2 | Building neural networks with Keras/TensorFlow, Image classification using CNNs, Text generation using RNNs, Model training and fine-tuning, Hyperparameter optimization, Deployment of deep learning models |
| UDS214E2 | Program Elective II (Natural Language Processing) | Elective | 4 | Text preprocessing techniques, Tokenization and stemming, Word embeddings (Word2Vec, GloVe), Sentiment analysis, Named Entity Recognition, Introduction to chatbots and language models |
| UDS21407 | General Elective II (Digital Marketing) | Elective | 2 | Search Engine Optimization (SEO), Search Engine Marketing (SEM), Social media marketing, Content marketing, Email marketing, Analytics and reporting in digital marketing |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UDS21501 | Internet of Things | Core | 4 | IoT architecture and components, Sensors and actuators, Communication protocols (MQTT, CoAP), IoT platforms (AWS IoT, Azure IoT Hub), Edge computing, IoT security and privacy |
| UDS21502 | Data Security and Privacy | Core | 4 | Fundamentals of cryptography, Network security principles, Access control mechanisms, Data privacy regulations (GDPR, CCPA), Privacy-preserving data analysis, Ethical hacking and security audits |
| UDS21503 | Data Warehousing and Mining | Core | 4 | Data warehouse architecture, ETL processes, OLAP operations, Data cube technology, Data mining concepts, Data mining functionalities |
| UDS21504 | Predictive Modeling | Core | 4 | Regression models (Linear, Logistic), Time series forecasting, Ensemble methods (Random Forest, Boosting), Model deployment strategies, A/B testing, Feature engineering and selection |
| UDS21505 | Data Security Lab | Lab | 2 | Implementing cryptographic algorithms, Network security tools, Vulnerability assessment, Intrusion detection systems, Data anonymization techniques, Access control policy enforcement |
| UDS21506 | Minor Project | Project | 2 | Project proposal writing, Literature review, Data collection and preparation, System design and implementation, Testing and validation, Project report and presentation |
| UDS215E3 | Program Elective III (Business Intelligence) | Elective | 4 | BI architecture and tools, Data governance, Reporting and dashboards, OLAP cubes, Predictive analytics for business, Data-driven decision making |
| UDS215SK1 | Skill Enhancement Course I (Mobile Application Development) | Skill Enhancement | 2 | Introduction to mobile platforms (Android/iOS), UI/UX design principles, Development environments (Android Studio/Xcode), Front-end development (XML/SwiftUI), Back-end integration, App deployment process |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| UDS21601 | Blockchain Technologies | Core | 4 | Cryptographic primitives (Hashing, Digital Signatures), Distributed ledger technology, Consensus mechanisms (PoW, PoS), Smart contracts and DApps, Cryptocurrencies and tokens, Blockchain applications in data science |
| UDS21602 | Robotic Process Automation | Core | 4 | Introduction to RPA, RPA development life cycle, Bots and automation, RPA tools (UiPath, Automation Anywhere), Process mapping and optimization, Intelligent automation concepts |
| UDS216E4 | Program Elective IV (Computer Vision) | Elective | 4 | Image processing fundamentals, Feature detection and extraction, Object recognition and detection, Image segmentation, Deep learning for computer vision (CNNs), Applications (face recognition, autonomous vehicles) |
| UDS216E5 | Program Elective V (Reinforcement Learning) | Elective | 4 | Markov Decision Processes (MDPs), Bellman equations, Q-learning algorithm, Policy gradient methods, Deep Reinforcement Learning, Applications in robotics and game playing |
| UDS216PRJ | Major Project | Project | 8 | Advanced project management, Problem identification and scope definition, System architecture and design, Implementation and testing, Comprehensive report writing, Public presentation and defense |
| UDS216SK2 | Skill Enhancement Course II (Soft Skills) | Skill Enhancement | 2 | Effective communication, Teamwork and collaboration, Leadership skills, Problem-solving and critical thinking, Time management and productivity, Interpersonal skills and emotional intelligence |
