.png&w=1920&q=75)
B-E in Computer Science Engineering Data Science at Sahyadri College of Engineering & Management
Dakshina Kannada, Karnataka
.png&w=1920&q=75)
About the Specialization
What is Computer Science & Engineering (Data Science) at Sahyadri College of Engineering & Management Dakshina Kannada?
This B.E. Computer Science & Engineering (Data Science) program at Sahyadri College of Engineering & Management focuses on equipping students with advanced analytical and computational skills. It addresses the burgeoning demand for data professionals in the Indian market, covering crucial areas like data analysis, machine learning, and big data technologies. The program is tailored to bridge the gap between theoretical knowledge and practical industry applications within the vibrant Indian tech landscape.
Who Should Apply?
This program is ideal for aspiring engineers with a strong aptitude for mathematics, statistics, and programming, seeking entry into data-driven roles. It suits fresh graduates keen on contributing to India''''s digital transformation, individuals looking to upskill in cutting-edge data technologies, or career changers aiming for a shift into the booming data science industry. A foundational understanding of computer science concepts is beneficial.
Why Choose This Course?
Graduates of this program can expect to pursue dynamic career paths in India as Data Scientists, Machine Learning Engineers, Data Analysts, or Business Intelligence Developers. Entry-level salaries typically range from INR 4-8 LPA, with experienced professionals earning significantly more. The curriculum prepares students for roles in IT, finance, healthcare, and e-commerce sectors, aligning with industry-recognized professional certifications.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consistently practice coding problems, focusing on logic building, algorithmic thinking, and efficient data structure implementation. Understand pointers and memory management deeply, which are crucial for building robust software foundations.
Tools & Resources
HackerRank, GeeksforGeeks, CodeChef, learn-c.org
Career Connection
Essential for cracking technical interviews at top tech companies, building robust software solutions, and understanding core concepts for advanced algorithms in data science.
Excel in Engineering Mathematics- (Semester 1-2)
Pay close attention to Engineering Mathematics I & II, especially linear algebra, probability, and calculus. These subjects form the analytical and statistical bedrock for machine learning and complex data analysis. Solve diverse problems to grasp concepts thoroughly.
Tools & Resources
Khan Academy, NPTEL lectures, Standard textbooks, Peer study groups
Career Connection
Crucial for understanding the mathematical underpinnings of advanced data science algorithms, developing new models, and excelling in research-oriented roles.
Engage in Foundational Project-Based Learning- (Semester 1-2)
Start working on mini-projects that involve basic programming and data structures to apply classroom knowledge practically. For example, build a simple calculator, a to-do list application, or implement sorting algorithms. Collaborate with peers for diverse perspectives.
Tools & Resources
GitHub for version control, VS Code/IDE, Online project ideas
Career Connection
Develops critical problem-solving skills, practical application of theoretical concepts, and helps build an initial portfolio for future internships and job applications.
Intermediate Stage
Specialize in Object-Oriented Programming & Databases- (Semester 3-5)
Deepen understanding of OOP concepts with Java, applying them to create modular and scalable applications. Simultaneously, master SQL for efficient data querying, manipulation, and robust database design, which are indispensable in data engineering.
Tools & Resources
LeetCode for SQL/Java, Oracle Academy, Udemy courses, Official Java documentation
Career Connection
Fundamental for backend development, data engineering roles, and managing large datasets effectively across various industry sectors in India.
Explore Data Science Electives Early- (Semester 5 onwards)
Actively engage in Data Mining and other relevant program electives. Supplement with self-study on Python for data science, focusing on libraries like NumPy, Pandas, and Matplotlib, and grasp basic statistical concepts. Attend department workshops for exposure.
Tools & Resources
Coursera (e.g., Python for Data Science specialization), Kaggle competitions, RStudio for statistical analysis
Career Connection
Builds core domain knowledge and practical skills highly sought after for data analyst and junior data scientist roles, making you industry-ready sooner.
Build a Strong Online Presence & Network- (Semester 4-5)
Create a professional LinkedIn profile, actively participate in online tech communities, attend webinars/seminars, and start building a portfolio on GitHub with small data analysis projects. Networking with professionals is key.
Tools & Resources
LinkedIn, GitHub, Industry meetups and conferences, College career fairs
Career Connection
Opens doors to internships, mentorship opportunities, and future job prospects in the vibrant data science ecosystem, both in India and globally.
Advanced Stage
Master Machine Learning and Deep Learning- (Semester 6-7)
Gain in-depth knowledge of various ML/DL algorithms (e.g., CNNs, RNNs, NLP techniques). Implement projects using libraries like TensorFlow/PyTorch. Understand model evaluation, deployment concepts, and ethical considerations in AI.
Tools & Resources
Google Colab, AWS/Azure free tier, scikit-learn, Keras, PyTorch, Medium articles on ML
Career Connection
Prepares for specialized, high-demand roles like Machine Learning Engineer, AI Researcher, and Advanced Data Scientist in leading tech firms and startups.
Undertake Industry-Relevant Capstone Projects- (Semester 6-8)
For Project Phase-I, Project Phase-II, and the final Project Work, choose topics that address genuine industry problems in data science. Focus on end-to-end solutions from data collection, model development, to deployment and visualization.
Tools & Resources
Collaboration with faculty/industry mentors, Datasets from UCI ML Repository/Kaggle, Cloud platforms (e.g., Google Cloud, Azure)
Career Connection
Creates a tangible portfolio, demonstrates comprehensive problem-solving abilities, and often leads to pre-placement offers or strong recommendations from industry experts.
Prepare Rigorously for Placements & Internships- (Semester 7-8)
Actively participate in campus placements, prepare for technical interviews covering Data Structures, Algorithms, and ML concepts, and practice aptitude tests. Develop strong communication and presentation skills, which are crucial for team roles.
Tools & Resources
Mock interviews with faculty/alumni, Company-specific preparation groups, Career services workshops, Online interview platforms (e.g., InterviewBit)
Career Connection
Maximizes chances of securing coveted internships and full-time positions with top companies in the data science domain across India, ensuring a successful career launch.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 8 semesters / 4 years
Credits: 152 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA11 | Engineering Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Vector Algebra, Ordinary Differential Equations, Laplace Transforms |
| 22CH12 | Engineering Chemistry | Core | 3 | Water Technology, Electrochemical Energy Systems, Corrosion and its Control, Engineering Materials, Instrumental Methods of Analysis |
| 22CP13 | C Programming for Problem Solving | Core | 3 | Introduction to C, Control Structures, Arrays and Strings, Functions and Pointers, Structures and File Handling |
| 22ME14 | Elements of Mechanical Engineering | Core | 3 | Energy and its Sources, Prime Movers, Material Science, Manufacturing Processes, Engineering Thermodynamics |
| 22EL15 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Fundamentals, Three Phase Systems, Electrical Machines, Power Systems |
| 22CHL16 | Engineering Chemistry Lab | Lab | 1 | Volumetric Analysis, Instrumental Analysis, Preparation of Polymers, Determination of Properties, Surface Tension and Viscosity |
| 22CPL17 | C Programming Lab | Lab | 1 | Basic C Programs, Control Structures, Arrays and Strings, Functions, File Operations |
| 22EGH18 | Communicative English | Core | 1 | Speaking Skills, Listening Skills, Reading Comprehension, Writing Skills, Grammar |
| 22ID19 | Innovation and Design | Core | 1 | Design Thinking, Problem Identification, Ideation, Prototyping, Presentation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA21 | Engineering Mathematics-II | Core | 4 | Linear Algebra, Multiple Integrals, Vector Calculus, Numerical Methods, Complex Analysis |
| 22PH22 | Engineering Physics | Core | 3 | Quantum Mechanics, Lasers, Optical Fibers, Material Science, Nanotechnology |
| 22EE23 | Basic Electronics | Core | 3 | Semiconductor Diodes, Transistors, Rectifiers, Amplifiers, Digital Electronics |
| 22CS24 | Data Structures | Core | 3 | Arrays, Linked Lists, Stacks, Queues, Trees, Graphs |
| 22ME25 | Computer Aided Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Solid Modeling |
| 22PHL26 | Engineering Physics Lab | Lab | 1 | Lasers and Optical Fibers, Semiconductor Devices, Elasticity, Spectroscopy, Electrical Measurements |
| 22CSL27 | Data Structures Lab | Lab | 1 | Implementations of Stacks, Queues, Linked Lists, Sorting Algorithms, Searching Algorithms |
| 22SK28 | Scientific Foundations of Health | Core | 1 | Basic Anatomy, Physiology, Nutrition, Lifestyle Diseases, Mental Health |
| 22CV29 | Constitution of India and Professional Ethics | Core | 1 | Indian Constitution, Fundamental Rights, Directive Principles, Professional Ethics, Cyber Laws |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA31 | Engineering Mathematics-III | Core | 4 | Transforms (Fourier, Z), Partial Differential Equations, Probability & Statistics, Numerical Solution of ODEs, Calculus of Variations |
| 22CS32 | Object Oriented Programming with Java | Core | 3 | Introduction to Java, Classes and Objects, Inheritance, Polymorphism, Exception Handling, Collections |
| 22CS33 | Digital Logic Design | Core | 3 | Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Memories |
| 22CS34 | Computer Organization and Architecture | Core | 3 | CPU Organization, Memory Hierarchy, I/O Organization, Instruction Sets, Pipelining |
| 22CS35 | Discrete Mathematical Structures | Core | 3 | Set Theory, Relations, Functions, Graph Theory, Combinatorics, Logic |
| 22CSL36 | Object Oriented Programming Lab | Lab | 1 | Java Programs for Classes, Inheritance, Interfaces, Exceptions, Collections |
| 22CSL37 | Digital Logic Design Lab | Lab | 1 | Logic Gates Implementation, Combinational Circuits Design, Sequential Circuits Design, Counters |
| 22ENV38 | Environmental Studies | Core | 1 | Ecosystems, Biodiversity, Environmental Pollution, Waste Management, Sustainable Development |
| 22SD39 | Samskrutika Kannada / Balake Kannada | Core | 1 | Kannada Language Skills, Kannada Culture, Prose and Poetry, Grammar, Communication |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS41 | Design and Analysis of Algorithms | Core | 4 | Algorithm Paradigms, Sorting and Searching, Graph Algorithms, Dynamic Programming, Greedy Algorithms |
| 22CS42 | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks |
| 22CS43 | Database Management Systems | Core | 3 | ER Model, Relational Model, SQL, Normalization, Transaction Management |
| 22CS44 | Automata Theory and Computability | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Turing Machines, Undecidability |
| 22CS45 | Microcontrollers and Embedded Systems | Core | 3 | 8051 Microcontroller, ARM Processors, Interfacing, Embedded C, RTOS Concepts |
| 22CSL46 | Operating Systems Lab | Lab | 1 | Linux Commands, Shell Scripting, Process Management, Memory Management, Synchronization |
| 22CSL47 | Database Management Systems Lab | Lab | 1 | SQL Queries, PL/SQL, Database Design, Triggers, Views |
| 22ADD48 | Additive Manufacturing | Core | 1 | 3D Printing Technologies, Materials, Design for Additive Manufacturing, Applications, Future Trends |
| 22SD49 | Vyavaharika Kannada / Balake Kannada | Core | 1 | Practical Kannada Communication, Vocabulary, Basic Grammar, Dialogue Writing, Cultural Context |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS51 | Computer Networks | Core | 4 | Network Models, Physical Layer, Data Link Layer, Network Layer, Transport Layer, Application Layer |
| 22CS52 | Web Technologies | Core | 3 | HTML, CSS, JavaScript, Web Servers, PHP/Node.js, Databases for Web, Web Security |
| 22CS53 | Professional Practice & Ethics | Core | 3 | Professionalism, Ethics in Engineering, Intellectual Property Rights, Cyber Security, Societal Impact of Technology |
| 22CS541 | Data Mining | Elective (Program Elective-I for Data Science) | 3 | Data Preprocessing, Association Rule Mining, Classification Algorithms, Clustering Algorithms, Outlier Analysis |
| 22XX55 | Open Elective-I | Elective (Open Elective) | 3 | |
| 22CSL56 | Computer Networks Lab | Lab | 1 | Network Commands, Socket Programming, Network Traffic Analysis, Router Configuration, Client-Server Applications |
| 22CSL57 | Web Technologies Lab | Lab | 1 | HTML/CSS Projects, JavaScript Interactive Pages, Server-side Scripting, Database Integration |
| 22CSL58 | Data Mining Lab | Lab | 1 | Implementation of Classification, Clustering Algorithms, Association Rules, Data Preprocessing using Tools |
| 22HS59 | Universal Human Values | Core | 1 | Self-exploration, Human Relationships, Society, Nature, Ethics |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS61 | Software Engineering | Core | 4 | Software Life Cycle, Requirements Engineering, Design Principles, Software Testing, Project Management |
| 22CS62 | Cryptography and Network Security | Core | 3 | Symmetric Key Cryptography, Asymmetric Key Cryptography, Hashing and Digital Signatures, Network Security Protocols, Firewalls and VPNs |
| 22CS63 | Cloud Computing | Core | 3 | Cloud Paradigms, Service Models (IaaS, PaaS, SaaS), Deployment Models, Virtualization, Cloud Security |
| 22CS642 | Machine Learning | Elective (Program Elective-II for Data Science) | 3 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation Metrics, Ensemble Methods |
| 22XX65 | Open Elective-II | Elective (Open Elective) | 3 | |
| 22CSL66 | Cryptography and Network Security Lab | Lab | 1 | Encryption/Decryption Algorithms, Digital Signatures Implementation, Network Security Tools, Firewall Configuration |
| 22CSL67 | Machine Learning Lab | Lab | 1 | Implementation of ML Algorithms, Feature Engineering, Model Training and Evaluation, Data Visualization for ML |
| 22CSL68 | Project Phase-I | Project | 2 | Problem Identification, Literature Survey, Project Planning, Initial Design, Report Writing |
| 22NC69 | National Service Scheme / NSS | Core | 1 | Community Service, Social Awareness Programs, Leadership Development, Skill Enhancement, Environmental Activities |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS71 | Professional Practice & Ethics for Engineers | Core | 3 | Engineering Ethics, Professional Responsibility, Intellectual Property Rights, Cyber Law, Societal Impact of Technology |
| 22CS741 | Deep Learning | Elective (Program Elective-III for Data Science) | 3 | Neural Networks Basics, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs and Transformers, Deep Learning Frameworks (TensorFlow/PyTorch) |
| 22CS742 | Data Warehousing and Business Intelligence | Elective (Program Elective-IV for Data Science) | 3 | Data Warehousing Concepts, ETL Process, OLAP and Data Cubes, Business Intelligence Tools, Data Visualization for BI |
| 22XX74 | Open Elective-III | Elective (Open Elective) | 3 | |
| 22CSL75 | Project Phase-II | Project | 3 | Project Development and Implementation, Module Integration, Testing and Debugging, Documentation, Interim Presentation |
| 22CSL76 | Internship / Industry Training | Internship | 2 | Industrial Experience, Practical Skill Application, Professional Development, Report Submission, Presentation of Internship Learnings |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22CS841 | Web Mining | Elective (Program Elective-V for Data Science) | 3 | Web Content Mining, Web Structure Mining, Web Usage Mining, Search Engines, Recommendation Systems |
| 22CS82 | Project Work | Project | 10 | Full Project Implementation, Advanced Development, Comprehensive Testing, Final Report Writing, Viva-Voce Examination |
| 22CS83 | Technical Seminar | Seminar | 1 | Research on Current Technologies, Technical Presentation Skills, Report Writing, Question and Answer Session, In-depth Topic Analysis |
