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BE-CS-DS in Computer Science Engineering Data Science at Yenepoya Institute of Technology
Dakshina Kannada, Karnataka
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About the Specialization
What is Computer Science & Engineering (Data Science) at Yenepoya Institute of Technology Dakshina Kannada?
This Computer Science & Engineering (Data Science) program at Yenepoya Institute of Technology focuses on equipping students with advanced skills in data analysis, machine learning, artificial intelligence, and big data technologies. The curriculum is meticulously designed to meet the growing demands of the Indian industry for skilled data professionals, integrating theoretical knowledge with practical applications. This program distinguishes itself through a strong emphasis on hands-on labs and real-world projects, preparing graduates for immediate impact.
Who Should Apply?
This program is ideal for aspiring engineers and graduates with a strong aptitude for mathematics, statistics, and programming, seeking entry into the thriving data science domain in India. It also suits working professionals who wish to upskill or transition their careers into data-intensive roles, leveraging their existing engineering background. Career changers with a logical mindset and a passion for extracting insights from data will find this specialization highly rewarding, provided they meet the foundational prerequisites.
Why Choose This Course?
Graduates of this program can expect diverse and high-demand career paths such as Data Scientist, Machine Learning Engineer, Data Analyst, Big Data Engineer, and Business Intelligence Developer within India. Entry-level salaries typically range from INR 4-8 LPA, growing significantly with experience to INR 15-30+ LPA for senior roles. The program aligns with industry-recognized certifications in cloud platforms and machine learning, fostering rapid career growth within leading Indian and multinational technology companies.
Student Success Practices
Foundation Stage
Strengthen Core STEM Fundamentals- (Semester 1-2)
Focus intensely on Engineering Mathematics, Physics, and foundational C programming during the first two semesters. Regularly practice problem-solving, understand underlying concepts, and apply them in labs. Building a solid base in these areas is crucial for advanced data science topics.
Tools & Resources
NPTEL courses for Maths/Physics, GeeksforGeeks for C programming, Peer study groups
Career Connection
A strong foundation ensures understanding of algorithms, statistical models, and computational efficiency, essential for advanced roles in data science and machine learning.
Develop Foundational Programming Skills- (Semester 1-2)
Beyond classroom assignments, engage in competitive programming platforms to hone problem-solving and coding skills in C, then transition to Python. Practice data structure implementations and algorithmic thinking rigorously.
Tools & Resources
CodeChef, HackerRank, LeetCode (easy level), Python documentation
Career Connection
Proficiency in programming is the bedrock for implementing data science solutions and is a primary skill assessed during technical interviews for analyst and junior data scientist roles.
Cultivate Effective Study Habits & Networking- (Semester 1-2)
Establish a consistent study routine, participate actively in class discussions, and form study groups with peers. Leverage the Soft Skill Course to improve communication and teamwork. Engage with seniors for academic and career guidance.
Tools & Resources
Google Scholar for basic research, College library resources, Departmental events
Career Connection
Good study habits lead to academic excellence, while early networking builds a support system and exposes you to future career opportunities and collaborative projects.
Intermediate Stage
Master Data Science Core Tools and Concepts- (Semester 3-5)
Deep dive into Python for data science (Pandas, NumPy, Scikit-learn, Matplotlib), SQL for database management, and R for statistical analysis. Actively participate in labs and internal projects focusing on machine learning and big data fundamentals.
Tools & Resources
Kaggle for datasets and notebooks, DataCamp/Coursera for specialized courses, Official documentation of libraries
Career Connection
Hands-on mastery of these tools is critical for most entry to mid-level data scientist, ML engineer, and data analyst positions in the Indian market.
Engage in Practical Project Development- (Semester 3-5)
Undertake mini-projects beyond coursework, focusing on real-world datasets. Apply machine learning algorithms, perform data cleaning, and visualize results. Participate in college hackathons or technical competitions to gain practical exposure.
Tools & Resources
GitHub for version control, Google Colab/Jupyter Notebooks, Local hackathon events
Career Connection
Practical projects demonstrate application skills to potential employers and build a portfolio, which is vital for placements in data science roles.
Build a Professional Network- (Semester 3-5)
Attend industry workshops, seminars, and guest lectures organized by the department. Connect with faculty members, alumni, and industry professionals on platforms like LinkedIn. Seek mentorship opportunities within the data science community.
Tools & Resources
LinkedIn, Department career fairs, Professional meetups
Career Connection
Networking opens doors to internship opportunities, mentorship, and insights into industry trends, significantly aiding in securing placements and future career growth.
Advanced Stage
Specialize and Execute Capstone Projects- (Semester 6-8)
Select advanced electives aligned with your career interests (e.g., Deep Learning, NLP, Big Data). Dedicate significant effort to Project Work I and II, aiming for innovative solutions to complex problems. Focus on documenting code and findings meticulously.
Tools & Resources
Advanced libraries (TensorFlow, PyTorch), Cloud platforms (AWS, Azure, GCP), Research papers on arXiv
Career Connection
A strong capstone project showcasing advanced skills is often a deciding factor in securing top data science and research positions, highlighting your specialization.
Prioritize Internship and Industry Exposure- (Semester 6-8)
Actively seek and complete meaningful internships in data science roles. Apply theoretical knowledge to practical industry challenges. Use the internship as a learning experience for real-world data pipelines and team collaboration.
Tools & Resources
Internshala, Naukri.com, College placement cell
Career Connection
Internships provide invaluable industry experience, often lead to pre-placement offers, and make you highly competitive for full-time roles in Indian companies.
Intensive Placement Preparation- (Semester 6-8)
Engage in rigorous technical interview preparation, focusing on data structures, algorithms, SQL, machine learning concepts, and behavioral questions. Practice mock interviews, participate in resume building workshops, and research target companies in India.
Tools & Resources
InterviewBit, Glassdoor for company interview experiences, College placement training programs
Career Connection
Thorough preparation directly translates into higher chances of cracking interviews and securing desirable placements in leading tech and analytics firms across India.
Program Structure and Curriculum
Eligibility:
- 10+2 or equivalent with Physics, Mathematics as compulsory subjects along with one of the Chemistry/Biotechnology/Biology/Electronics/Computer Science/Information Technology/Informatics Practices/Geology/Engineering Graphics/Vocational subjects with an aggregate of 45% (40% for reserved categories) in optional subjects.
Duration: 8 semesters / 4 years
Credits: 149 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MAT11 | Engineering Mathematics - I | Core | 4 | Matrices and their applications, Differential Calculus I (Polar curves), Differential Calculus II (Partial differentiation), Integral Calculus (Multiple integrals), Vector Algebra and functions |
| 22PHY12 | Engineering Physics | Core | 4 | Quantum Mechanics and Statistical Physics, Lasers and Holography, Optical Fibers and their applications, Electrical and Magnetic Properties of Materials, Superconductivity and Nanotechnology |
| 22ELE13 | Basic Electrical Engineering | Core | 3 | DC Circuits and Network Theorems, AC Fundamentals and Single Phase Circuits, Three Phase AC Circuits, Electrical Machines (Transformers, Motors), Electrical Safety and Measuring Instruments |
| 22CIV14 | Elements of Civil Engineering | Core | 3 | Building Materials and Construction, Surveying and Geomatics, Hydraulics and Water Resources, Environmental Engineering Fundamentals, Transportation Engineering Basics |
| 22ME15 | Elements of Mechanical Engineering | Core | 3 | Basic Concepts of Thermodynamics, IC Engines and Power Plants, Refrigeration and Air Conditioning, Power Transmission Systems, Engineering Materials and Manufacturing Processes |
| 22PHYL16 | Engineering Physics Lab | Lab | 1 | Experiment on Lasers and Optical Fibers, Study of LCR Series and Parallel Circuits, Determination of Planck''''s Constant, Hall Effect Experiment, Characteristics of a Diode |
| 22ELEL17 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Ohm''''s Law and KVL/KCL, Measurement of Power in AC Circuits, Load Test on DC Shunt Motor, Connection of Fluorescent Lamp, Earth Resistance Measurement |
| 22EGH18 | Technical English | Core | 1 | Grammar and Vocabulary for Technical Communication, Reading Comprehension of Technical Texts, Report Writing and Technical Documentation, Presentation Skills and Public Speaking, Resume Writing and Interview Skills |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MAT21 | Engineering Mathematics - II | Core | 4 | First Order Differential Equations, Higher Order Linear Differential Equations, Laplace Transforms, Inverse Laplace Transforms, Applications of Differential Equations |
| 22CHE22 | Engineering Chemistry | Core | 4 | Electrochemistry and Batteries, Corrosion and its Control, Water Technology and Treatment, Fuels and Combustion, Polymer Chemistry and Engineering Materials |
| 22CPS23 | C Programming for Problem Solving | Core | 3 | Introduction to C Programming, Control Structures (loops, conditionals), Functions and Modular Programming, Arrays and Strings, Pointers and Structures |
| 22EGD24 | Engineering Graphics | Core | 3 | Orthographic Projections of Points, Lines, Orthographic Projections of Planes, Solids, Sections of Solids, Development of Surfaces, Isometric Projections |
| 22BEC25 | Basic Electronics & Communication Engineering | Core | 3 | Semiconductor Diodes and Applications, Transistors (BJT, FET) and Amplifiers, Operational Amplifiers (Op-Amps), Digital Electronics (Logic Gates, Flip-Flops), Communication Systems (Modulation, Demodulation) |
| 22CHEL26 | Engineering Chemistry Lab | Lab | 1 | Volumetric Analysis (Acid-base, Redox titrations), Instrumental Methods of Analysis, Determination of Water Hardness, Estimation of Chemical Oxygen Demand (COD), Synthesis of a Polymer |
| 22CPSL27 | C Programming Lab | Lab | 1 | Programs on Control Statements, Implementation of Functions and Recursion, Array and String Manipulation Programs, Pointers and Dynamic Memory Allocation, Structures and File Handling |
| 22SKC28 | Soft Skill Course | Core | 1 | Communication Skills (Verbal and Non-verbal), Teamwork and Collaboration, Critical Thinking and Problem Solving, Professional Etiquette and Ethics, Interpersonal Skills and Leadership |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MAT31 | Transforms and Numerical Techniques | Core | 4 | Fourier Series and Transforms, Z-Transforms, Numerical Methods for Solutions of Equations, Numerical Integration and Differentiation, Partial Differential Equations |
| 22CS32 | Data Structures | Core | 4 | Introduction to Data Structures and Arrays, Stacks and Queues, Linked Lists (Singly, Doubly, Circular), Trees (Binary, BST, AVL, B-Trees), Graphs (Representations, Traversals) |
| 22CS33 | Digital Logic Design | Core | 3 | Boolean Algebra and Logic Gates, Combinational Logic Circuits (Adders, Decoders), Sequential Logic Circuits (Flip-Flops, Registers), Counters and Shift Registers, Memory and Programmable Logic Devices |
| 22CS34 | Discrete Mathematical Structures | Core | 3 | Logic and Propositional Calculus, Set Theory and Relations, Functions and Counting Techniques, Graph Theory and Trees, Algebraic Structures |
| 22CSDS35 | Foundations of Data Science | Core | 3 | Introduction to Data Science Workflow, Data Collection and Acquisition, Data Preprocessing and Cleaning, Exploratory Data Analysis (EDA), Basic Statistical Concepts for Data Science |
| 22CSL36 | Data Structures Lab | Lab | 1 | Implementation of Stacks and Queues, Operations on Linked Lists, Tree Traversals and Binary Search Trees, Graph Traversal Algorithms, Sorting and Searching Algorithms |
| 22CSDSL37 | Foundations of Data Science Lab | Lab | 1 | Python Programming for Data Science, Data Manipulation with Pandas, Data Visualization with Matplotlib/Seaborn, Basic Statistical Analysis in Python, Data Cleaning and Preprocessing Techniques |
| 22IC38 | Indian Constitution & Professional Ethics | Mandatory Non-Credit | 0 | Preamble, Fundamental Rights and Duties, Structure and Functioning of Union Government, State Government and Local Administration, Professional Ethics in Engineering, Cyber Law and Intellectual Property Rights |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MAT41 | Probability & Statistics for Data Science | Core | 4 | Probability Theory and Random Variables, Probability Distributions (Discrete and Continuous), Sampling Distributions and Central Limit Theorem, Hypothesis Testing and Confidence Intervals, Correlation and Regression Analysis |
| 22CS42 | Design and Analysis of Algorithms | Core | 4 | Algorithm Analysis and Asymptotic Notations, Divide and Conquer Strategy, Greedy Algorithms, Dynamic Programming, Graph Algorithms (BFS, DFS, Shortest Paths) |
| 22CS43 | Operating Systems | Core | 3 | Process Management and CPU Scheduling, Process Synchronization and Deadlocks, Memory Management (Paging, Segmentation), Virtual Memory, File Systems and I/O Management |
| 22CS44 | Database Management Systems | Core | 3 | Introduction to DBMS and ER Model, Relational Model and Algebra, SQL (Structured Query Language), Database Design and Normalization, Transaction Management and Concurrency Control |
| 22CSDS45 | Machine Learning Fundamentals | Core | 3 | Introduction to Machine Learning Paradigms, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Model Evaluation and Validation, Ensemble Methods and Dimensionality Reduction |
| 22CSL46 | Database Management Systems Lab | Lab | 1 | SQL DDL and DML Commands, Advanced SQL Queries (Joins, Subqueries), PL/SQL Programming (Procedures, Functions, Triggers), Database Schema Design and Implementation, User Management and Security |
| 22CSDSL47 | Machine Learning Lab | Lab | 1 | Implementing Linear and Logistic Regression, Decision Trees and Random Forests, K-Means Clustering and PCA, Model Training and Hyperparameter Tuning, Evaluating Classification and Regression Models |
| 22IP48 | Intellectual Property Rights & Cyber Law | Mandatory Non-Credit | 0 | Introduction to IPR and Patents, Copyrights, Trademarks, and Industrial Designs, Cyber Law and IT Act 2000, Digital Signatures and Cyber Crimes, Data Protection and Privacy |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS51 | Computer Networks | Core | 4 | Network Models (OSI, TCP/IP), Physical and Data Link Layer Protocols, Network Layer (IP Addressing, Routing), Transport Layer (TCP, UDP), Application Layer Protocols (HTTP, DNS, FTP) |
| 22CSDS52 | Big Data Analytics | Core | 4 | Introduction to Big Data and its Challenges, Hadoop Ecosystem (HDFS, MapReduce), Spark and In-memory Processing, NoSQL Databases (Cassandra, MongoDB), Big Data Visualization and Tools |
| 22CSDS53 | Deep Learning | Core | 3 | Introduction to Neural Networks, Feedforward Networks and Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and LSTMs, Deep Learning Frameworks (TensorFlow, PyTorch) |
| 22CSDS54 | Elective-I (Data Mining) | Elective | 3 | Data Preprocessing and Data Warehousing, Association Rule Mining (Apriori, FP-growth), Classification Techniques (Decision Trees, Naive Bayes), Clustering Algorithms (K-Means, Hierarchical), Outlier Detection and Data Mining Applications |
| 22CSDS55 | Elective-II (Cloud Computing) | Elective | 3 | Introduction to Cloud Computing Concepts, Service Models (IaaS, PaaS, SaaS), Deployment Models (Public, Private, Hybrid), Virtualization Technologies, Cloud Security and Data Privacy |
| 22CSL56 | Computer Networks Lab | Lab | 1 | Network Configuration and Troubleshooting, Socket Programming (TCP/UDP), Packet Sniffing and Analysis (Wireshark), Implementation of Routing Protocols, Network Security Concepts |
| 22CSDSL57 | Big Data & Deep Learning Lab | Lab | 1 | HDFS Operations and MapReduce Programming, Spark RDDs and DataFrames, Building and Training Simple Neural Networks, Image Classification with CNNs, Text Processing using RNNs/LSTMs |
| 22ES58 | Environmental Studies | Mandatory Non-Credit | 0 | Ecosystems and Biodiversity, Environmental Pollution and Control, Natural Resources and Conservation, Climate Change and Sustainable Development, Environmental Acts and Policies |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS61 | Software Engineering | Core | 4 | Software Development Life Cycle Models, Requirements Engineering, Software Design Principles and Patterns, Software Testing Techniques and Strategies, Software Project Management and Metrics |
| 22CSDS62 | Data Warehousing & Business Intelligence | Core | 4 | Data Warehousing Concepts and Architecture, ETL Process (Extraction, Transformation, Loading), OLAP (Online Analytical Processing), Data Marts and Dimensional Modeling, Business Intelligence Tools and Dashboards |
| 22CSDS63 | Time Series Analysis | Core | 3 | Introduction to Time Series Data, Components of Time Series (Trend, Seasonality), ARIMA and SARIMA Models, Exponential Smoothing Methods, Forecasting Techniques and Model Evaluation |
| 22CSDS64 | Elective-III (Image Processing) | Elective | 3 | Image Fundamentals and Transforms, Image Enhancement (Spatial and Frequency Domain), Image Restoration and Filtering, Image Segmentation Techniques, Feature Extraction and Object Recognition |
| 22CSDS65 | Elective-IV (Internet of Things) | Elective | 3 | IoT Architecture and Paradigms, Sensors, Actuators, and Microcontrollers, IoT Communication Protocols (MQTT, CoAP), Cloud Platforms for IoT, IoT Security and Applications |
| 22CSL66 | Software Engineering Lab | Lab | 1 | UML Diagrams for Software Design, Requirements Gathering and Documentation, Software Testing using Automated Tools, Version Control Systems (Git), Project Planning and Management Tools |
| 22CSDSL67 | Data Warehousing & Time Series Analysis Lab | Lab | 1 | Designing and Implementing a Data Warehouse Schema, ETL Process Implementation using Tools, Building BI Dashboards with Tableau/Power BI, Time Series Data Preprocessing in R/Python, Implementing ARIMA Models for Forecasting |
| 22INT68 | Internship | Project/Internship | 2 | Industry Exposure and Practical Skill Development, Project Report Writing, Presentation of Internship Work, Professional Networking, Real-world Problem Solving |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CSDS71 | Predictive Modeling | Core | 4 | Linear and Logistic Regression, Decision Trees and Random Forests, Gradient Boosting Machines (XGBoost, LightGBM), Support Vector Machines (SVMs), Model Selection and Hyperparameter Tuning |
| 22CSDS72 | Data Visualization | Core | 3 | Principles of Effective Data Visualization, Tools for Data Visualization (Tableau, Power BI), Creating Interactive Dashboards, Storytelling with Data, Advanced Visualization Techniques (D3.js basics) |
| 22CSDS73 | Elective-V (Recommender Systems) | Elective | 3 | Introduction to Recommender Systems, Collaborative Filtering (User-based, Item-based), Content-Based Recommender Systems, Hybrid Recommender Systems, Evaluation Metrics for Recommender Systems |
| 22CSDS74 | Project Work - I | Project | 4 | Problem Identification and Literature Survey, Project Design and Planning, Initial Implementation and Module Development, Progress Reporting and Presentation, Teamwork and Collaboration |
| 22CSDSL75 | Predictive Modeling & Data Visualization Lab | Lab | 1 | Implementing various Regression Models, Implementing Classification Algorithms, Building Interactive Dashboards with Tableau/Power BI, Creating Custom Visualizations in Python, Model Deployment Fundamentals |
| 22AEC76 | Audit Course | Audit | 0 | Professional Communication, Research Methodology, Financial Literacy, Entrepreneurship Development, Stress Management |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CSDS81 | Social Network Analysis | Core | 4 | Introduction to Social Network Analysis, Network Measures (Centrality, Density), Community Detection in Networks, Network Visualization and Graph Algorithms, Influence and Diffusion in Social Networks |
| 22CSDS82 | Elective-VI (Distributed Systems) | Elective | 3 | Introduction to Distributed Systems, Client-Server and Peer-to-Peer Architectures, Distributed Consensus and Coordination, RPC, Message Queues, and Middleware, Fault Tolerance and Replication |
| 22CSDS83 | Project Work - II | Project | 8 | Complete Project Implementation and Testing, Data Collection and Analysis for Project, Report Writing and Documentation, Final Project Presentation and Viva-Voce, Integration of Advanced Data Science Concepts |
| 22SEM84 | Technical Seminar | Project/Seminar | 1 | In-depth Research on Advanced Topics, Critical Analysis and Literature Review, Technical Presentation Skills, Question and Answer Handling, Development of Technical Writing Skills |
