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B-SC in Computer Science With Data Science at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Computer Science with Data Science at SRM Institute of Science and Technology Chengalpattu?
This B.Sc. Computer Science (Data Science) program at SRM Institute of Science and Technology focuses on equipping students with essential skills in data analysis, machine learning, and statistical modeling. Designed to meet the burgeoning demand for data professionals in the Indian market, the program emphasizes a blend of theoretical foundations and practical applications. It differentiates itself through a robust curriculum covering programming, databases, big data, and advanced AI techniques, preparing graduates for diverse roles in data-driven industries.
Who Should Apply?
This program is ideal for fresh graduates with a background in 10+2 with Mathematics seeking entry into the rapidly expanding field of data science. It also caters to individuals with a diploma in mathematics who are looking to kickstart a career in data analytics. Aspiring data scientists, business intelligence analysts, and machine learning engineers who are keen to develop strong analytical and programming capabilities will find this program highly beneficial.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding career paths as Data Analysts, Data Scientists, Machine Learning Engineers, or Business Intelligence Developers within India. Entry-level salaries typically range from INR 3.5 to 6 LPA, with experienced professionals earning significantly more. The program fosters growth trajectories in sectors like finance, healthcare, e-commerce, and IT, aligning with industry-recognized certifications in Python, R, and cloud platforms.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate extra time to Python and C/C++ programming. Utilize platforms like HackerRank, LeetCode, and GeeksforGeeks for daily coding practice. Build small projects to solidify understanding of data structures and algorithms. This strong base is critical for cracking technical interviews at top Indian tech companies.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python, C/C++
Career Connection
Strong programming and DSA skills are foundational for passing technical rounds in placements and for building efficient data science solutions.
Excel in Mathematics and Statistics- (Semester 1-2)
Focus on deeply understanding Calculus, Discrete Mathematics, Probability, and Statistics. These are the bedrock of data science. Attend extra tutorials, solve problems from textbooks like ''''Probability and Statistics for Engineers'''' by Miller & Freund, and apply concepts using tools like NumPy in Python. Strong analytical skills are highly valued for data scientist roles.
Tools & Resources
NumPy, Statistical software (R), Textbooks (e.g., Miller & Freund)
Career Connection
A solid grasp of math and stats is essential for understanding algorithms, interpreting models, and solving complex data problems in industry.
Engage in Peer Learning & Tech Clubs- (Semester 1-2)
Join college technical clubs focused on programming or data science. Participate in coding competitions and form study groups with peers. Collaborating on projects and discussing complex topics will enhance understanding, build a professional network, and prepare for team-based industry work.
Tools & Resources
College Tech Clubs, Coding competition platforms, Study groups
Career Connection
Develops teamwork, communication, and problem-solving skills crucial for collaborative work environments in tech companies.
Intermediate Stage
Build a Strong Data Science Portfolio- (Semester 3-5)
Beyond coursework, initiate personal projects applying Machine Learning, Big Data Analytics, and Data Visualization. Showcase these projects on GitHub and Kaggle. Participate in Kaggle competitions to hone practical skills and gain recognition, which significantly boosts resume visibility for internships and placements.
Tools & Resources
GitHub, Kaggle, Python libraries (Pandas, Scikit-learn, Matplotlib), Big Data tools (Hadoop, Spark)
Career Connection
A robust portfolio demonstrates practical application of skills to recruiters, making candidates stand out for internships and job roles.
Seek Early Industry Exposure- (Semester 3-5)
Actively pursue internships during semester breaks, even unpaid ones, to gain practical experience in data roles. Attend workshops and seminars conducted by industry experts and aim for certifications in relevant tools like Tableau, SQL, or cloud platforms (AWS/Azure/GCP Data Engineer tracks). This practical exposure is key for landing quality placements.
Tools & Resources
Internship platforms, Coursera/edX for certifications, Tableau, SQL, AWS/Azure/GCP
Career Connection
Internships provide real-world experience, build industry contacts, and often lead to pre-placement offers (PPOs) in Indian companies.
Network and Participate in Hackathons- (Semester 3-5)
Attend local and national data science conferences, meetups, and hackathons. Connect with professionals on LinkedIn. These events offer invaluable learning, networking opportunities, and a chance to apply skills under pressure, demonstrating problem-solving capabilities to potential employers.
Tools & Resources
LinkedIn, Eventbrite/Meetup for local events, Hackathon platforms
Career Connection
Networking opens doors to hidden job opportunities, mentorship, and industry insights, while hackathons showcase problem-solving abilities.
Advanced Stage
Specialize and Deepen Expertise- (Semester 6)
Choose advanced electives in areas like Deep Learning, NLP, or Cloud Computing that align with career aspirations. Complete a significant final year project focusing on a real-world problem, potentially in collaboration with an data industry partner. This specialization sets you apart for niche and high-paying roles in the Indian data science landscape.
Tools & Resources
TensorFlow/PyTorch, NLTK/SpaCy, Cloud platforms
Career Connection
Specialized skills are highly sought after, enabling graduates to target specific high-growth areas and secure roles with better compensation.
Intensive Placement Preparation- (Semester 6)
Focus on mock interviews (technical, HR, and case studies) tailored for data science roles. Practice resume building, LinkedIn profile optimization, and cover letter writing. Prepare for aptitude tests and brush up on advanced algorithms and system design concepts relevant to data engineering roles. Utilize college placement cells extensively.
Tools & Resources
Mock interview platforms, Resume builders, LinkedIn, College placement cell resources
Career Connection
Thorough preparation ensures confidence and proficiency in interviews, significantly improving chances of securing desired placements in top companies.
Develop Communication & Business Acumen- (Semester 6)
Data scientists need to explain complex insights to non-technical stakeholders. Practice presenting project outcomes clearly and concisely. Take online courses on business intelligence, data storytelling, and ethics in AI. This blend of technical and soft skills is crucial for leadership roles and effective problem-solving in Indian companies.
Tools & Resources
Presentation software, Online courses on BI/Ethics, Case study analysis
Career Connection
Effective communication and business understanding are vital for advancing into leadership and strategic data science roles, making technical expertise more impactful.
Program Structure and Curriculum
Eligibility:
- Minimum 50% aggregate in 10+2 / HSC / CBSE / ICSE or Equivalent Examination with Mathematics as one of the subjects.
Duration: 3 years / 6 semesters
Credits: Minimum 140 credits Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21HS101L/21HS102L | Language I (Tamil/Hindi/French/German/Japanese/Sanskrit) | Core | 3 | |
| 21HS101 | English | Core | 3 | |
| 21BS101 | Calculus | Core | 4 | Differential Calculus, Integral Calculus, Differential Equations, Vector Calculus, Matrices |
| 21CS101 | Programming in C | Core | 3 | Introduction to C, Control Statements, Arrays and Strings, Functions and Pointers, Structures, Unions and Files |
| 21CS102 | Digital Computer Fundamentals | Core | 3 | Number Systems, Boolean Algebra and Logic Gates, Combinational Circuits, Sequential Circuits, Memory Devices |
| 21CS103 | Introduction to Data Science | Core | 3 | Introduction to Data Science, Data Collection & Acquisition, Data Pre-processing and Exploration, Data Visualization, Data Science Ethics and Future Trends |
| 21CS104 | Programming in C Lab | Lab | 2 | C programming exercises, Conditional statements, Loops, Arrays, Functions, Pointers, Structures and Files |
| 21CS105 | Digital Computer Fundamentals Lab | Lab | 2 | Implementation of Logic Gates, Combinational Logic Circuits, Sequential Logic Circuits, Counters and Registers, Arithmetic Circuits |
| 21CS106 | Introduction to Data Science Lab | Lab | 2 | Installation of Python/R, Data handling using NumPy/Pandas, Data cleaning and transformation, Basic data visualization, Descriptive statistics |
| 21EV101 | Environmental Studies | Core | 2 | Introduction to Environmental Studies and Ecosystems, Natural Resources, Environmental Pollution, Social Issues and the Environment, Human Population and the Environment |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21HS201L/21HS202L | Language II (Tamil/Hindi/French/German/Japanese/Sanskrit) | Core | 3 | |
| 21BS201 | Discrete Mathematics | Core | 4 | Mathematical Logic, Set Theory and Relations, Functions and Recurrence Relations, Graph Theory, Algebraic Structures |
| 21CS201 | Data Structures | Core | 3 | Introduction to Data Structures, Linear Data Structures (Arrays, Stacks, Queues), Linked Lists, Trees, Graphs and Hashing |
| 21CS202 | Object Oriented Programming with C++ | Core | 3 | Introduction to OOP and C++, Classes and Objects, Constructors and Destructors, Inheritance and Polymorphism, Templates, Exception Handling and Files |
| 21CS203 | Database Management Systems | Core | 3 | Introduction to DBMS, ER Model and Relational Model, Structured Query Language (SQL), Normalization, Transaction Management and Concurrency Control |
| 21CS204 | Python Programming | Core | 3 | Python Basics and Data Types, Control Flow and Functions, Modules and Packages, Object-Oriented Programming in Python, File Handling and Exception Handling |
| 21CS205 | Data Structures Lab | Lab | 2 | Array and Stack implementation, Queue and Linked List operations, Tree traversals, Graph algorithms, Sorting and Searching algorithms |
| 21CS206 | Object Oriented Programming with C++ Lab | Lab | 2 | Classes and Objects implementation, Operator overloading, Inheritance concepts, Polymorphism and virtual functions, File operations in C++ |
| 21CS207 | Database Management Systems Lab | Lab | 2 | DDL and DML commands, SQL functions and queries, Joins and subqueries, Views, Sequences, Indexes, PL/SQL programming |
| 21CS208 | Python Programming Lab | Lab | 2 | Basic Python programs, String and List manipulations, Functions and Modules, File handling operations, OOP concepts in Python |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21BS301 | Probability and Statistics | Core | 4 | Probability Theory, Random Variables and Distributions, Sampling Distributions, Testing of Hypothesis, Regression and Correlation |
| 21CS301 | Operating Systems | Core | 3 | Introduction to Operating Systems, Process Management, CPU Scheduling, Memory Management, File Systems and I/O Systems |
| 21CS302 | Java Programming | Core | 3 | Introduction to Java, OOP Concepts in Java, Packages, Interfaces and Exception Handling, Multithreading and I/O, Applets and AWT |
| 21CS303 | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem, MapReduce Programming, HBase and Hive, Spark and Stream Processing |
| 21CS304 | Software Engineering | Core | 3 | Introduction to Software Engineering, Software Process Models, Software Requirements, Software Design, Software Testing and Maintenance |
| 21CSE01 / 21CSE02 / 21CSE03 | Elective I (e.g., Computer Graphics, Web Technology, Mobile Application Development) | Elective | 3 | Elective specific topics, chosen from official elective list (e.g., Graphics primitives, HTML/CSS/JS, Android UI components) |
| 21CS305 | Operating Systems Lab | Lab | 2 | Linux commands and Shell scripting, Process creation and termination, CPU scheduling algorithms, Memory management techniques, File system calls |
| 21CS306 | Java Programming Lab | Lab | 2 | OOP programs in Java, Exception handling, Multithreading applications, Applet and AWT programming, JDBC connectivity |
| 21CS307 | Big Data Analytics Lab | Lab | 2 | Hadoop setup and HDFS commands, MapReduce program implementation, HiveQL queries, Pig scripts, Spark RDD operations |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21BS401 | Linear Algebra for Data Science | Core | 4 | Vector Spaces, Matrices and Determinants, Systems of Linear Equations, Eigenvalues and Eigenvectors, Linear Transformations |
| 21CS401 | Computer Networks | Core | 3 | Introduction to Computer Networks, Physical and Data Link Layer, Network Layer, Transport Layer, Application Layer and Network Security |
| 21CS402 | Artificial Intelligence | Core | 3 | Introduction to AI, Problem Solving by Search, Knowledge Representation, Machine Learning Basics, Natural Language Processing |
| 21CS403 | Machine Learning | Core | 3 | Introduction to Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Validation, Ensemble Methods and Dimensionality Reduction |
| 21CS404 | Data Visualization Techniques | Core | 3 | Introduction to Data Visualization, Principles of Visual Perception, Data Visualization Techniques, Interactive Data Visualization, Tools and Applications (Tableau/PowerBI/Matplotlib) |
| 21CSE04 / 21CSE05 / 21CSE06 | Elective II (e.g., Cloud Computing, Internet of Things, Cyber Security) | Elective | 3 | Elective specific topics, chosen from official elective list (e.g., Cloud service models, IoT architecture, Cryptography) |
| 21CS405 | Artificial Intelligence Lab | Lab | 2 | Implementation of Search Algorithms, Constraint Satisfaction Problems, Knowledge representation using Prolog/Python, Mini-AI projects, Game playing algorithms |
| 21CS406 | Machine Learning Lab | Lab | 2 | Data Preprocessing using Python, Implementation of Regression models, Implementation of Classification models, Clustering algorithms (K-Means), Model evaluation metrics |
| 21CS407 | Data Visualization Techniques Lab | Lab | 2 | Basic plotting with Matplotlib/Seaborn, Creating interactive dashboards, Visualizing multivariate data, Storytelling with data, Using Tableau/Power BI for dashboards |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS501 | Deep Learning | Core | 3 | Introduction to Neural Networks, Deep Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow/PyTorch) |
| 21CS502 | Web Analytics | Core | 3 | Introduction to Web Analytics, Data Collection and Metrics, Web Analytics Tools (Google Analytics), Traffic Analysis and Reporting, Conversion Optimization and A/B Testing |
| 21CS503 | Natural Language Processing | Core | 3 | Introduction to NLP, Text Preprocessing and Tokenization, Part-of-Speech Tagging and Named Entity Recognition, Text Classification and Clustering, Sentiment Analysis and Language Models |
| 21CS504 | Optimization Techniques | Core | 3 | Introduction to Optimization, Linear Programming, Non-Linear Programming, Metaheuristic Algorithms, Dynamic Programming |
| 21CSE07 / 21CSE08 / 21CSE09 | Elective III (e.g., Blockchain Technology, Digital Marketing, Human Computer Interaction) | Elective | 3 | Elective specific topics, chosen from official elective list (e.g., Cryptographic hash, SEO/SEM, UX principles) |
| 21CS505 | Deep Learning Lab | Lab | 2 | Building Feedforward Neural Networks, Implementing CNNs for image classification, Implementing RNNs for sequence data, Transfer learning techniques, Hyperparameter tuning |
| 21CS506 | Web Analytics Lab | Lab | 2 | Setting up Google Analytics, Tracking website traffic, Analyzing user behavior, Creating custom reports, Implementing A/B tests |
| 21CS507 | Natural Language Processing Lab | Lab | 2 | Text preprocessing using NLTK, Implementing POS tagging, Named Entity Recognition, Sentiment analysis, Word embeddings |
| 21CS508 | Mini Project | Project | 2 | Problem identification and literature survey, System design and implementation, Testing and debugging, Project report preparation, Presentation and demonstration |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS601 | Cloud and Edge Computing for Data Science | Core | 3 | Introduction to Cloud Computing, Cloud Service Models (IaaS, PaaS, SaaS), Introduction to Edge Computing, Data Processing at the Edge, Cloud and Edge Integration for Data Science |
| 21CS602 | Business Intelligence | Core | 3 | Introduction to Business Intelligence, Data Warehousing and ETL, OLAP and Data Mining for BI, Reporting and Dashboarding, Decision Support Systems |
| 21CS603 | Ethics and Governance in Data Science | Core | 3 | Ethical Principles in Data Science, Data Privacy and Security, Algorithmic Bias and Fairness, Data Governance and Regulations, Societal Impact of AI and Data |
| 21CSP01 | Project Work / Internship | Project | 6 | Project proposal and literature review, Methodology and system design, Implementation and experimentation, Results analysis and interpretation, Project report and presentation |
| 21CSE10 / 21CSE11 / 21CSE12 | Elective IV (e.g., Augmented and Virtual Reality, Quantum Computing, Digital Forensics) | Elective | 3 | Elective specific topics, chosen from official elective list (e.g., VR/AR devices, Qubits/Quantum gates, Cybercrime investigation) |
