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B-E in Computer Science Engineering Data Science at Rajarajeswari College of Engineering
Bengaluru, Karnataka
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About the Specialization
What is Computer Science & Engineering (Data Science) at Rajarajeswari College of Engineering Bengaluru?
This Computer Science & Engineering (Data Science) program at RajaRajeswari College of Engineering focuses on equipping students with advanced skills in data analysis, machine learning, and artificial intelligence. Recognizing the immense growth of data-driven decision-making in Indian industries like e-commerce, finance, and healthcare, the curriculum is designed to produce professionals capable of extracting insights from complex datasets. The program differentiates itself by integrating foundational CSE principles with specialized data science modules through professional electives, preparing graduates for cutting-edge roles.
Who Should Apply?
This program is ideal for fresh engineering graduates seeking entry into the booming data analytics and AI sectors in India. It also caters to working professionals from related IT fields looking to upskill and transition into specialized data science roles, or career changers from analytical backgrounds aiming to formalize their expertise. Applicants with a strong aptitude for mathematics, statistics, and programming, and a keen interest in problem-solving using data, will find this program particularly rewarding.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths such as Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Developer, or AI/ML Researcher. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals potentially earning INR 15-30+ LPA in top-tier Indian and MNC companies. The program aligns with professional certifications like AWS Certified Machine Learning Specialty or Google Cloud Professional Data Engineer, fostering significant growth trajectories within the vibrant Indian tech landscape.
Student Success Practices
Foundation Stage
Strengthen Programming & Math Fundamentals- (Semester 1-2)
Dedicate consistent time to mastering C and Python programming languages, along with core calculus and linear algebra concepts. Solve daily coding challenges and practice problem-solving logic. This forms the bedrock for advanced data science topics.
Tools & Resources
GeeksforGeeks, HackerRank, Coursera (for foundational courses), NPTEL lectures for Mathematics
Career Connection
A strong foundation in programming and mathematics is critical for data manipulation, algorithm implementation, and understanding machine learning models, leading to better performance in technical interviews.
Develop Effective Study Habits & Peer Learning- (Semester 1-2)
Establish a disciplined study schedule and actively participate in study groups. Discuss complex concepts, clarify doubts, and collaborate on assignments. Explaining concepts to peers solidifies your own understanding and builds teamwork skills.
Tools & Resources
College library resources, Microsoft Teams/Google Meet for group discussions, Online forums for subject-specific queries
Career Connection
Good academic performance leads to higher CGPA, opening doors for internships and placements. Teamwork skills are highly valued in corporate environments.
Engage in Early Skill Building Workshops- (Semester 1-2)
Beyond classroom learning, attend workshops or take online courses on essential software tools like Excel for data handling, or basics of data visualization. Familiarize yourself with introductory data science concepts through online resources.
Tools & Resources
YouTube tutorials for Excel, Kaggle (for beginner datasets), Datacamp (introductory tracks)
Career Connection
Early exposure to tools and basic concepts makes later specialization easier and gives you a competitive edge for subsequent internships and projects.
Intermediate Stage
Master Data Structures, Algorithms & Database Skills- (Semester 3-5)
Intensively practice Data Structures & Algorithms, as they are crucial for efficient data processing. Concurrently, build strong proficiency in SQL for database management and querying, a cornerstone of data science.
Tools & Resources
LeetCode, HackerRank (SQL challenges), CodeChef, PostgreSQL/MySQL for practice
Career Connection
These skills are fundamental for almost all software development and data science roles, heavily tested in technical interviews for product and analytics companies.
Pursue Domain-Specific Electives & Mini-Projects- (Semester 5-6)
Actively choose professional electives related to Data Science (e.g., ''''Introduction to Data Science'''', ''''Artificial Intelligence & Machine Learning''''). Complement this with mini-projects applying these concepts to real-world datasets.
Tools & Resources
Kaggle datasets, GitHub for project collaboration, Scikit-learn (Python library)
Career Connection
Specialized electives build expertise, while projects demonstrate practical application, making your profile attractive for data science internships and entry-level positions.
Engage in Industry Exposure and Networking- (Semester 3-5)
Seek out guest lectures, industry seminars, and workshops conducted by professionals. Participate in hackathons and coding competitions. Actively connect with alumni and industry mentors on platforms like LinkedIn to understand career paths and gain insights.
Tools & Resources
LinkedIn, Meetup groups (Data Science Bengaluru), College career fairs, VTU innovation cells
Career Connection
Networking opens doors to internships, mentorship, and potential job referrals. Industry exposure keeps you updated on current trends and skill requirements, crucial for a competitive job market.
Advanced Stage
Undertake Advanced Data Science Projects & Internships- (Semester 7-8)
Focus on a capstone project (Phase 1 & 2) that addresses a complex data science problem, utilizing advanced ML/DL techniques. Secure and excel in a long-term internship, preferably in a data science role, to gain significant practical experience.
Tools & Resources
TensorFlow/PyTorch, AWS/GCP for cloud deployment, Jupyter Notebooks, Company-specific tools during internship
Career Connection
High-impact projects and relevant internships are crucial for placements, showcasing your ability to deliver solutions and adapt to industry environments, often leading to pre-placement offers.
Intensive Placement Preparation & Mock Interviews- (Semester 7-8)
Begin rigorous preparation for placement drives, including aptitude tests, technical coding rounds, and specific data science case studies. Participate in mock interviews, focusing on both technical depth and behavioral aspects, and seek feedback for improvement.
Tools & Resources
Placement cell resources, Glassdoor for interview experiences, Pramp/InterviewBit for mock interviews, Quant training apps
Career Connection
Thorough preparation directly translates into higher success rates in securing placements with leading companies in data science, analytics, and AI domains.
Build a Professional Portfolio and Personal Brand- (Semester 6-8)
Curate a strong online presence: a well-maintained GitHub profile showcasing projects, a professional LinkedIn profile highlighting skills and achievements, and potentially a personal blog. Develop strong communication and presentation skills for technical seminars and interviews.
Tools & Resources
GitHub, LinkedIn, Medium/WordPress for blogging, Canva for presentation design
Career Connection
A strong portfolio acts as a tangible proof of your abilities, attracting recruiters and demonstrating initiative beyond academic requirements, leading to better career opportunities and recognition.
Program Structure and Curriculum
Eligibility:
- Passed Karnataka 2nd PUC/12th Grade or equivalent examination with Physics and Mathematics as compulsory subjects along with Chemistry / Biotechnology / Biology / Electronics / Computer Science as one of the optional subjects and obtained a minimum of 45% of marks (40% for SC/ST/OBC candidates) from a recognized Board/University.
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MAT11 | Calculus and Linear Algebra | Core | 4 | Differential Calculus, Integral Calculus, Vector Algebra, Linear Algebra, Matrices |
| 21CHE12 | Engineering Chemistry | Core | 4 | Electrochemistry, Corrosion, Polymers, Energy Storage, Water Analysis |
| 21ELE13 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, DC Machines, AC Motors |
| 21CIV14 | Elements of Civil Engineering and Mechanics | Core | 3 | Building Materials, Surveying, Mechanics of Materials, Forces and Equilibrium, Friction |
| 21CPL15 | C Programming for Problem Solving | Core | 3 | C Language Basics, Control Flow, Functions, Arrays, Pointers |
| 21AEC16 | Communicative English | Skill Development | 1 | Grammar, Vocabulary, Reading Comprehension, Writing Skills, Oral Communication |
| 21CHEL17 | Engineering Chemistry Laboratory | Lab | 1 | Potentiometric Titration, Conductometric Titration, Viscosity Determination, Calorimetry, pH Metry |
| 21CPL18 | C Programming Laboratory | Lab | 1 | Conditional Statements, Looping Constructs, Function Implementation, Array Manipulation, String Operations |
| 21ELL19 | Basic Electrical Engineering Laboratory | Lab | 1 | Ohm''''s Law Verification, Kirchhoff''''s Laws, Series-Parallel Circuits, Transformer Tests, Motor Characteristics |
| 21TGH10 | Technical English | Mandatory Non-Credit | 0 | Report Writing, Technical Communication, Presentation Skills, Resume Building, Interview Skills |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MAT21 | Advanced Calculus and Numerical Methods | Core | 4 | Partial Differential Equations, Laplace Transforms, Fourier Series, Numerical Solutions of Equations, Numerical Integration |
| 21PHY22 | Engineering Physics | Core | 4 | Quantum Mechanics, Lasers, Optical Fibers, Nanoscience, Semiconductor Physics |
| 21ELN23 | Basic Electronics | Core | 3 | Diode Circuits, Transistors, Amplifiers, Operational Amplifiers, Digital Logic Gates |
| 21ME24 | Elements of Mechanical Engineering | Core | 3 | Thermodynamics, Power Plants, Refrigeration, Manufacturing Processes, Automobile Engineering |
| 21PSP25 | Python Programming for Problem Solving | Core | 3 | Python Basics, Data Structures, Functions and Modules, File I/O, Object-Oriented Programming |
| 21AEC26 | Scientific Foundations of Health | Skill Development | 1 | Nutrition, Lifestyle Diseases, Mental Health, Ergonomics, Stress Management |
| 21PHYL27 | Engineering Physics Laboratory | Lab | 1 | Laser Wavelength, Diode Characteristics, Photoelectric Effect, Planck''''s Constant, Energy Gap of Semiconductor |
| 21PSPL28 | Python Programming Laboratory | Lab | 1 | List Operations, Tuple Manipulation, Dictionary Usage, Function Calls, Module Importing |
| 21ELNL29 | Basic Electronics Laboratory | Lab | 1 | Rectifier Circuits, Filter Circuits, Transistor Biasing, Amplifier Characteristics, Logic Gate Verification |
| 21KN210/21KNL210 | Vyavaharika Kannada / Balake Kannada | Mandatory Non-Credit | 0 | Basic Kannada Phrases, Grammar, Cultural Context, Conversational Skills, Reading & Writing |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MAT31 | Transforms and Numerical Methods | Core | 3 | Fourier Transforms, Z-Transforms, Difference Equations, Calculus of Variations, Finite Differences |
| 21CS32 | Data Structures and Applications | Core | 3 | Arrays, Linked Lists, Stacks and Queues, Trees, Graphs |
| 21CS33 | Analog and Digital Electronics | Core | 3 | Operational Amplifiers, Logic Gates, Combinational Logic, Sequential Logic, Analog-to-Digital Conversion |
| 21CS34 | Computer Organization and Architecture | Core | 3 | Basic Computer Structure, CPU Organization, Memory System, I/O Organization, Pipelining |
| 21CS35 | Object Oriented Programming with JAVA | Core | 3 | Classes and Objects, Inheritance, Polymorphism, Exception Handling, Multithreading |
| 21CSL36 | Data Structures Laboratory | Lab | 1 | List Implementation, Stack/Queue Operations, Tree Traversal, Graph Algorithms, Sorting and Searching |
| 21CSL37 | Analog and Digital Electronics Laboratory | Lab | 1 | Op-Amp Circuits, Logic Gate Verification, Flip-Flops, Counters, Shift Registers |
| 21CSL38 | Java Programming Laboratory | Lab | 1 | Class Design, Inheritance Applications, Interface Usage, File Handling, GUI Development |
| 21HSM39 | Universal Human Values | AEC | 1 | Human Aspirations, Relationship Values, Societal Values, Nature Values, Professional Ethics |
| 21CIP30 | Constitution of India, Professional Ethics & Cyber Law | Mandatory Non-Credit | 0 | Indian Constitution, Fundamental Rights, Professional Ethics, Cybercrimes, IT Act |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS41 | Discrete Mathematics | Core | 3 | Set Theory, Logic and Proofs, Relations and Functions, Graph Theory, Combinatorics |
| 21CS42 | Design and Analysis of Algorithms | Core | 3 | Algorithm Analysis, Sorting Algorithms, Graph Algorithms, Greedy Algorithms, Dynamic Programming |
| 21CS43 | Operating Systems | Core | 3 | Process Management, CPU Scheduling, Memory Management, File Systems, I/O Systems |
| 21CS44 | Microcontroller and Embedded Systems | Core | 3 | Microcontrollers, Embedded System Design, Interfacing Techniques, Real-Time Operating Systems, ARM Processors |
| 21CS45 | Database Management Systems | Core | 3 | Database Architecture, ER Modeling, Relational Algebra, SQL, Transaction Management |
| 21CSL46 | Design and Analysis of Algorithms Laboratory | Lab | 1 | Recursive Algorithms, Divide and Conquer, Dynamic Programming Problems, Graph Traversal, Sorting Efficiency |
| 21CSL47 | Operating Systems Laboratory | Lab | 1 | System Calls, Process Synchronization, Deadlock Detection, Memory Allocation, Shell Scripting |
| 21CSL48 | DBMS Lab with Mini Project | Lab | 1 | DDL Commands, DML Commands, SQL Queries, PL/SQL, Database Design |
| 21ENV49 | Environmental Studies | Mandatory Non-Credit | 0 | Ecosystems, Pollution, Renewable Energy, Biodiversity, Environmental Management |
| 21SDC40 | Innovation and Design Thinking | Skill Development | 1 | Design Process, Empathy Mapping, Ideation Techniques, Prototyping, User Centered Design |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS51 | Artificial Intelligence & Machine Learning | Core (Data Science Foundation) | 3 | AI Foundations, Search Algorithms, Machine Learning Basics, Supervised Learning, Unsupervised Learning |
| 21CS52 | Computer Networks | Core | 3 | Network Topologies, OSI Model, TCP/IP Protocol Suite, Routing Algorithms, Network Security |
| 21CS53 | Automata Theory and Computability | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Turing Machines, Decidability |
| 21CS541 | Introduction to Data Science | Professional Elective (Data Science) | 3 | Data Science Pipeline, Statistical Foundations, Data Preprocessing, Exploratory Data Analysis, Introduction to Machine Learning |
| 21CS55 | Open Elective - 1 (e.g., Python Programming for Analytics) | Open Elective | 3 | Data Manipulation, Numerical Computing, Data Visualization, Statistical Analysis, Basic ML Libraries |
| 21CSL56 | AIML Lab with Mini Project | Lab (Data Science Foundation) | 1 | Linear Regression, Classification Models, Clustering, Neural Networks Basics, Tool Usage (e.g., Python/R) |
| 21CSL57 | Computer Networks Laboratory | Lab | 1 | Network Configuration, Socket Programming, Packet Tracing, Routing Protocols, Network Security Tools |
| 21SDC58 | Skill Development Course - II (Industry Specific) | Skill Development | 1 | Cloud Fundamentals, Web Development Frameworks, Cybersecurity Essentials, UI/UX Design, Competitive Programming |
| 21INT59 | Internship (Activity) | Internship | 2 | Industry Exposure, Project Implementation, Teamwork, Problem Solving, Report Writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS61 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design, Testing Strategies, Software Project Management |
| 21CS62 | Cloud Computing | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security, Cloud Platforms (AWS, Azure) |
| 21CS63 | Web Technologies | Core | 3 | HTML5, CSS3, JavaScript, Client-Side Scripting, Server-Side Scripting, Web Frameworks |
| 21CS642 | Big Data Analytics | Professional Elective (Data Science) | 3 | Big Data Concepts, Hadoop Ecosystem, MapReduce, Spark, NoSQL Databases |
| 21CS65 | Open Elective - 2 (e.g., Introduction to Cyber Security) | Open Elective | 3 | Cyber Threats, Cryptography, Network Security, Web Security, Digital Forensics |
| 21CSL66 | Software Engineering Lab with Mini Project | Lab | 1 | UML Diagrams, Agile Methodologies, Version Control, Test Case Generation, Project Documentation |
| 21CSL67 | Web Technologies Laboratory | Lab | 1 | Responsive Design, DOM Manipulation, AJAX, Database Integration, RESTful APIs |
| 21SDC68 | Skill Development Course - III (Industry Specific) | Skill Development | 1 | Data Visualization Tools (Tableau, PowerBI), DevOps Tools (Docker, Kubernetes), Ethical Hacking Basics, Mobile App Development, Entrepreneurship Skills |
| 21INT69 | Internship (Activity) | Internship | 2 | Practical Skill Application, Problem-solving, Communication Skills, Professional Networking, Mentorship |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS71 | Data Mining & Data Warehousing | Core (Data Science) | 3 | Data Warehousing Concepts, OLAP, Association Rule Mining, Classification Algorithms, Clustering Techniques |
| 21CS721 | Natural Language Processing | Professional Elective (Data Science) | 3 | Text Preprocessing, Linguistic Models, Word Embeddings, Sentiment Analysis, Machine Translation |
| 21CS731 | Deep Learning | Professional Elective (Data Science) | 3 | Neural Network Architectures, Backpropagation, Convolutional Neural Networks, Recurrent Neural Networks, Transformers |
| 21CS74 | Project Work Phase 1 / Internship | Project / Internship | 3 | Problem Formulation, Literature Survey, Methodology Design, Preliminary Implementation, Report Writing |
| 21SDC75 | Technical Seminar | Skill Development | 1 | Research Topic Selection, Literature Review, Presentation Skills, Technical Writing, Q&A Handling |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS81 | Project Work Phase 2 | Project | 10 | System Development, Testing and Validation, Deployment Strategies, Optimization, Final Report and Viva |
| 21CS821 | Business Intelligence | Professional Elective (Data Science) | 3 | BI Concepts, Data Modeling, ETL Processes, Dashboards and Reporting, Decision Support Systems |
| 21CS83 | Open Elective - 3 (e.g., Supply Chain Management) | Open Elective | 3 | Logistics, Inventory Management, Demand Forecasting, Global Supply Chains, E-commerce Operations |
