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B-E-COMPUTER-SCIENCE-ENGINEERING-DATA-SCIENCE in General at Vivekananda Institute of Technology
Bengaluru, Karnataka
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About the Specialization
What is General at Vivekananda Institute of Technology Bengaluru?
This B.E. Computer Science & Engineering (Data Science) program at Vivekananda Institute of Technology focuses on equipping students with advanced skills in data analysis, machine learning, and big data technologies. It is highly relevant in the Indian industry, which is experiencing exponential growth in data-driven decision-making across e-commerce, finance, and healthcare. The program differentiates itself by providing a robust theoretical foundation coupled with extensive practical application, addressing the burgeoning demand for skilled data scientists in the Indian market.
Who Should Apply?
This program is ideal for aspiring engineers and fresh graduates seeking entry into the high-demand field of data science. It also caters to working professionals looking to upskill in analytics, machine learning, and big data to advance their careers. Individuals with a strong aptitude for mathematics, statistics, and programming, typically with a background in science or engineering in their 10+2, will find this specialization particularly rewarding for transitioning into an analytical industry role.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including Data Scientist, Data Analyst, Machine Learning Engineer, Big Data Engineer, and Business Intelligence Developer. Entry-level salaries typically range from INR 4-8 LPA, growing significantly with experience to INR 15-30+ LPA in top-tier Indian and MNC companies. The program prepares students for roles in Bengaluru''''s vibrant tech ecosystem, contributing to India''''s digital transformation and offering opportunities for growth into leadership and specialized AI/ML positions.
Student Success Practices
Foundation Stage
Strengthen Core Programming & Math Skills- (Semester 1-2)
Dedicate extra time to master programming fundamentals in C/C++ and Java, along with discrete mathematics and linear algebra. Utilize platforms like HackerRank, LeetCode, and GeeksforGeeks for competitive programming challenges to build logical thinking and problem-solving abilities early on.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Khan Academy for Math
Career Connection
A strong foundation in programming and mathematics is critical for data science roles, forming the bedrock for understanding algorithms, data structures, and statistical models essential for placements.
Develop Effective Study Habits & Peer Learning- (Semester 1-2)
Form study groups to discuss complex topics, solve problems collaboratively, and prepare for internal assessments. Actively participate in class, take detailed notes, and seek clarifications from faculty. Focus on understanding concepts rather than rote memorization.
Tools & Resources
Collaborative whiteboards (Miro, Jamboard), Discord for study groups, College library resources
Career Connection
Effective collaboration and communication are vital in team-based project environments in the industry, while strong study habits ensure academic success which reflects positively on transcripts for placements.
Explore Basic Data Science Concepts- (Semester 1-2)
Beyond the curriculum, start exploring introductory courses on data science via online platforms. Understand the basics of Python for data analysis, data visualization tools like Tableau/Power BI, and statistical concepts. This provides an early edge and context for future courses.
Tools & Resources
Coursera (IBM Data Science Professional Certificate), Kaggle (entry-level datasets), freeCodeCamp, Python.org
Career Connection
Early exposure to data science tools and concepts helps in identifying career interests and builds a portfolio for future internships and specialized roles.
Intermediate Stage
Engage in Hands-on Data Science Projects- (Semester 3-5)
Apply theoretical knowledge from Machine Learning and Big Data Analytics courses to build mini-projects. Use real-world datasets from platforms like Kaggle or UCI Machine Learning Repository. Document your projects thoroughly on GitHub, focusing on problem statement, methodology, and results.
Tools & Resources
Kaggle, GitHub, Jupyter Notebook, Python libraries (Scikit-learn, Pandas, NumPy)
Career Connection
Practical projects demonstrate problem-solving skills and technical proficiency to recruiters, making your resume stand out for internships and entry-level data science jobs in India.
Seek Industry Internships & Workshops- (Semester 3-5)
Actively apply for internships during summer or winter breaks at local startups or established companies in Bengaluru''''s tech hub. Attend industry workshops, seminars, and hackathons organized by colleges or external organizations to gain exposure to current trends and network with professionals.
Tools & Resources
Internshala, LinkedIn, College placement cell, Local tech meetups
Career Connection
Internships provide invaluable real-world experience, build industry contacts, and often lead to pre-placement offers, significantly boosting your chances of securing a good job post-graduation.
Build a Strong Profile on Professional Platforms- (Semester 3-5)
Create and regularly update your LinkedIn profile, showcasing your projects, skills, and academic achievements. Participate in online data science competitions (e.g., Kaggle competitions) to test your skills and gain recognition. Network with alumni and professionals in your field.
Tools & Resources
LinkedIn, Kaggle, GitHub
Career Connection
A robust online professional presence attracts recruiters and demonstrates your continuous learning and passion for the field, crucial for competitive job markets in India.
Advanced Stage
Specialize and Undertake a Capstone Project- (Semester 6-8)
Choose electives that align with your career interests (e.g., NLP, Reinforcement Learning, Computer Vision). Dedicate significant effort to your final year project, aiming for an innovative solution to a complex problem, potentially collaborating with industry mentors or faculty research groups.
Tools & Resources
Advanced libraries (TensorFlow, PyTorch, Hugging Face), Research papers, Industry mentorship
Career Connection
A strong capstone project showcasing specialized skills is a powerful asset in interviews, demonstrating your ability to deliver end-to-end data science solutions and often attracting specialized roles.
Master Interview Preparation and Soft Skills- (Semester 6-8)
Practice coding interviews, particularly focusing on data structures, algorithms, and SQL. Prepare for technical discussions on machine learning concepts, project experiences, and case studies. Develop strong communication, presentation, and teamwork skills for group discussions and HR rounds.
Tools & Resources
LeetCode (Interview Prep), Glassdoor (company interviews), Mock interview sessions, Toastmasters/Public speaking clubs
Career Connection
Excellent interview performance is key to securing top placements. Strong soft skills are highly valued by Indian employers for teamwork and client interaction in a corporate setting.
Explore Higher Education or Entrepreneurship- (Semester 6-8)
For those interested in research or academia, prepare for entrance exams like GATE or GRE for M.Tech/Ph.D. programs in India or abroad. Students with entrepreneurial zeal can use their final year project as a stepping stone to develop a startup idea, seeking guidance from college incubation centers.
Tools & Resources
GATE/GRE study materials, VTU Incubation Center, Startup India resources
Career Connection
This path offers avenues for specialized research careers, academic positions, or the opportunity to build innovative data-driven ventures, contributing to India''''s startup ecosystem and R&D landscape.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Physics, Mathematics, and Chemistry/Biotechnology/Biology/Computer Science/Electronics as compulsory subjects with English as one of the languages. Obtained at least 45% marks (40% for reserved category) in the above subjects taken together. Admission through Karnataka CET or COMEDK UGET.
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MAT11 | Calculus and Differential Equations | Core | 4 | Differential Calculus, Integral Calculus, Differential Equations, Partial Differential Equations, Vector Calculus |
| 21PHY12 | Engineering Physics | Core | 4 | Quantum Mechanics, Solid State Physics, Lasers and Optic Fibers, Electrical Properties of Materials, Semiconductor Physics |
| 21ELE13 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Fundamentals, Electrical Machines, Power Systems, Electrical Safety |
| 21CIV14 | Elements of Civil Engineering | Core | 3 | Surveying, Building Materials, Hydraulics, Transportation Engineering, Environmental Engineering |
| 21FCT15 | Foundations of Computer Science | Core | 3 | Introduction to Computers, Problem Solving, Programming Constructs, Data Structures Basics, Algorithm Analysis |
| 21PHY16 | Engineering Physics Lab | Lab | 1 | Young''''s Modulus, Planck''''s Constant, Diode Characteristics, Transistor Characteristics, Resistivity of Semiconductor |
| 21FCL17 | Foundations of Computer Science Lab | Lab | 1 | Programming in C, Data input/output, Conditional statements, Loops and Arrays, Functions and Pointers |
| 21EGH18 | Communicative English | Core | 1 | Listening Skills, Speaking Skills, Reading Comprehension, Writing Skills, Presentation Skills |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MAT21 | Linear Algebra, Transforms and Numerical Methods | Core | 4 | Matrices and Determinants, Eigenvalues and Eigenvectors, Laplace Transforms, Fourier Series, Numerical Methods |
| 21CHE22 | Engineering Chemistry | Core | 4 | Electrochemistry, Corrosion, Fuels and Combustion, Polymer Chemistry, Water Technology |
| 21CME23 | Elements of Mechanical Engineering | Core | 3 | Thermodynamics, Power Plants, IC Engines, Refrigeration and Air Conditioning, Material Science |
| 21EVN24 | Environmental Studies | Core | 3 | Ecosystems, Biodiversity, Environmental Pollution, Waste Management, Sustainable Development |
| 21DTC25 | Data Structures using C++ | Core | 3 | Arrays and Pointers, Linked Lists, Stacks and Queues, Trees and Graphs, Sorting and Searching |
| 21CHE26 | Engineering Chemistry Lab | Lab | 1 | pH meter experiments, Conductivity experiments, Viscosity measurements, Water analysis, Estimation of metal ions |
| 21DTL27 | Data Structures using C++ Lab | Lab | 1 | Implementation of Linked Lists, Stack operations, Queue operations, Tree traversals, Graph algorithms |
| 21CIP28 | Constitution of India and Professional Ethics | Core | 1 | Indian Constitution, Fundamental Rights, Directive Principles, Professional Ethics, Cyber Law |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS31 | Discrete Mathematics | Core | 3 | Set Theory, Logic and Proofs, Graph Theory, Combinatorics, Recurrence Relations |
| 21CS32 | Data Analysis and Visualization | Core | 3 | Statistical Concepts, Data Cleaning, Exploratory Data Analysis, Data Visualization Techniques, Statistical Tools |
| 21CS33 | Object Oriented Programming with Java | Core | 3 | OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Exception Handling |
| 21CS34 | Computer Organization and Architecture | Core | 3 | Computer Basics, CPU Organization, Memory System, I/O Organization, Pipelining |
| 21CS35 | Database Management Systems | Core | 3 | Database Concepts, ER Model, Relational Model, SQL Queries, Normalization |
| 21CSL36 | Data Analysis and Visualization Lab | Lab | 1 | Python for Data Analysis, Pandas and NumPy, Matplotlib and Seaborn, Data preprocessing, Statistical plots |
| 21CSL37 | Object Oriented Programming with Java Lab | Lab | 1 | Java program development, Class and object implementation, Inheritance scenarios, Polymorphism examples, File handling |
| 21KSK38 | Kannada Language | Ability Enhancement | 1 | Basic Kannada grammar, Conversational Kannada, Reading comprehension, Writing simple sentences, Kannada culture |
| 21CST39 | Technical Communication | Ability Enhancement | 1 | Report writing, Technical documentation, Effective presentations, Meeting etiquette, Email communication |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS41 | Advanced Data Structures and Algorithms | Core | 3 | AVL Trees, B-Trees, Hashing Techniques, Dynamic Programming, Greedy Algorithms |
| 21CS42 | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks |
| 21CS43 | Probability and Statistics for Data Science | Core | 3 | Probability Distributions, Hypothesis Testing, Regression Analysis, ANOVA, Bayesian Statistics |
| 21CS44 | Design and Analysis of Algorithms | Core | 3 | Asymptotic Notations, Divide and Conquer, Greedy Method, Dynamic Programming, Backtracking and Branch & Bound |
| 21CS45 | Formal Automata Theory and Computability | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Pushdown Automata, Turing Machines |
| 21CSL46 | Advanced Data Structures and Algorithms Lab | Lab | 1 | Graph algorithms, Hashing implementation, Tree structures implementation, Dynamic programming problems, Advanced sorting techniques |
| 21CSL47 | Operating Systems Lab | Lab | 1 | Process scheduling algorithms, Memory allocation strategies, Banker''''s algorithm, Page replacement algorithms, File system operations |
| 21SC48 | Scientific Foundations of Data Science | Ability Enhancement | 1 | Fundamentals of Scientific Methods, Mathematical modeling, Computational thinking, Ethical considerations in Data Science, Reproducibility |
| 21RMI49 | Research Methodology and IPR | Ability Enhancement | 1 | Research problem formulation, Literature review, Data collection methods, Report writing, Intellectual Property Rights |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS51 | Software Engineering | Core | 3 | Software Life Cycle Models, Requirements Engineering, Design Concepts, Software Testing, Project Management |
| 21CS52 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation, Bias-Variance Tradeoff |
| 21CS53 | Big Data Analytics | Core | 3 | Hadoop Ecosystem, MapReduce, Spark, NoSQL Databases, Data Stream Mining |
| 21CS54X | Professional Elective - I | Elective | 3 | Depending on chosen elective |
| 21CS55X | Open Elective - I | Elective | 3 | Depending on chosen elective |
| 21CSL56 | Machine Learning Lab | Lab | 1 | Linear Regression implementation, Decision Tree algorithms, Clustering techniques, Neural network basics, Model performance metrics |
| 21CSL57 | Big Data Analytics Lab | Lab | 1 | Hadoop setup and commands, MapReduce programming, Spark RDD operations, Hive queries, Cassandra operations |
| 21INT58 | Internship / Technical Seminar | Project | 2 | Industry problem solving, Report writing, Presentation skills, Teamwork, Professional development |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS61 | Cloud Computing | Core | 3 | Cloud Architecture, Virtualization, Cloud Services (IaaS, PaaS, SaaS), Cloud Security, Cloud Deployment Models |
| 21CS62 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Deep Learning Frameworks |
| 21CS63 | Data Security and Privacy | Core | 3 | Cryptography, Network Security, Data Privacy Principles, Access Control, Legal and Ethical Aspects |
| 21CS64X | Professional Elective - II | Elective | 3 | Depending on chosen elective |
| 21CS65X | Open Elective - II | Elective | 3 | Depending on chosen elective |
| 21CSL66 | Deep Learning Lab | Lab | 1 | TensorFlow/PyTorch basics, Image classification with CNNs, Sequence modeling with RNNs, Generative model implementation, Transfer learning |
| 21CSL67 | Cloud Computing Lab | Lab | 1 | Virtual machine deployment, Cloud storage services, Serverless computing, Load balancing, Containerization with Docker |
| 21CSI68 | Project Work Phase 1 / Internship | Project | 2 | Problem identification, Literature review, Methodology design, Partial implementation, Team coordination |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS71 | Natural Language Processing | Core | 3 | Text Preprocessing, Language Models, Text Classification, Machine Translation, Sentiment Analysis |
| 21CS72 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Policy Gradient Methods |
| 21CS73X | Professional Elective - III | Elective | 3 | Depending on chosen elective |
| 21CS74X | Professional Elective - IV | Elective | 3 | Depending on chosen elective |
| 21CS75 | Project Work Phase 2 | Project | 6 | Full system implementation, Testing and validation, Result analysis, Technical report writing, Project defense |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS81 | Internship / Technical Seminar | Project | 17 | Advanced industry problem solving, Innovation and research, Professional communication, Independent learning, Project delivery |
| 21CS82 | Major Project Work | Project | 17 | Full scale product development, Solution deployment, Performance evaluation, Comprehensive technical report, Public presentation |
