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B-TECH in Computer Science Engineering Data Science at ST. JOSEPH ENGINEERING COLLEGE
Dakshina Kannada, Karnataka
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About the Specialization
What is Computer Science & Engineering (Data Science) at ST. JOSEPH ENGINEERING COLLEGE Dakshina Kannada?
This B.Tech in Computer Science & Engineering (Data Science) program at St Joseph Engineering College focuses on equipping students with advanced skills in data analysis, machine learning, and artificial intelligence. Recognizing India''''s booming data economy, the curriculum is designed to produce professionals capable of extracting actionable insights from complex datasets. It emphasizes a blend of theoretical knowledge and practical application, crucial for the evolving data-driven industry landscape.
Who Should Apply?
This program is ideal for aspiring engineers passionate about data, statistics, and computational problem-solving. It caters to fresh 12th-grade graduates seeking entry into the high-demand field of Data Science. Graduates from diploma programs aiming for advanced degrees and individuals with a strong aptitude for mathematics and logical reasoning will find this specialization particularly rewarding, preparing them for analytical roles.
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 AI Specialists. Entry-level salaries typically range from INR 4-8 lakhs per annum, with significant growth potential up to INR 15-30 lakhs or more for experienced professionals. The curriculum fosters skills for roles in various sectors, including IT, finance, healthcare, and e-commerce, aligning with industry demand for certified data experts.
Student Success Practices
Foundation Stage
Master Programming Fundamentals & Logic- (Semester 1-2)
Focus intensely on C, C++, and Python programming concepts, data structures, and algorithm design. Participate in coding challenges regularly to strengthen problem-solving logic. Build a strong foundation in calculus and linear algebra, specifically for data science applications.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses on Data Structures and Algorithms
Career Connection
A solid programming and mathematical foundation is crucial for clearing initial technical rounds in placements and for understanding advanced data science concepts.
Engage in Mini-Projects & Peer Learning- (Semester 1-2)
Apply theoretical knowledge by undertaking small programming projects, even if self-initiated. Form study groups to discuss complex topics, clarify doubts, and collaboratively solve problems. Explain concepts to peers to solidify understanding.
Tools & Resources
GitHub for project version control, Zoom/Google Meet for online study sessions, College library resources
Career Connection
Practical experience, even from mini-projects, demonstrates initiative and application skills, enhancing resume value. Peer learning improves communication and teamwork.
Develop Strong Communication & Presentation Skills- (Semester 1-2)
Actively participate in Professional Communication Skills and Activities for Multi-disciplinary and Social Connect. Practice presenting technical topics clearly and concisely. Join college clubs like toastmasters or debate societies to build confidence.
Tools & Resources
Microsoft PowerPoint/Google Slides, Online presentation tutorials, College clubs
Career Connection
Effective communication is vital for presenting project findings, collaborating in teams, and acing interview rounds, which are critical for placements.
Intermediate Stage
Specialize in Data Science Core Technologies- (Semester 3-5)
Deep dive into Data Mining, Data Warehousing, Python for Data Science, DBMS, and Machine Learning. Get hands-on with Python libraries like NumPy, Pandas, Scikit-learn, and SQL. Understand the theoretical underpinnings as well as practical implementation.
Tools & Resources
Kaggle datasets, Jupyter Notebooks, Google Colab, Coursera/Udemy courses specific to Python DS libraries
Career Connection
These are the core skills expected for entry-level data science roles, making students highly employable.
Build a Portfolio through Projects & Competitions- (Semester 4-5)
Work on significant mini-projects (like Mini Project 1 and Mini Project 2) utilizing real-world datasets. Participate in hackathons and data science competitions on platforms like Kaggle. Document all projects comprehensively on GitHub.
Tools & Resources
Kaggle, DrivenData, GitHub, Project management tools (Trello, Asana)
Career Connection
A strong project portfolio is the best way to showcase practical skills and problem-solving abilities to potential employers during placements.
Seek Early Industry Exposure & Networking- (Semester 4-5)
Actively look for internships during summer breaks or part-time opportunities related to data analysis. Attend webinars, workshops, and industry meetups. Connect with alumni and industry professionals on platforms like LinkedIn.
Tools & Resources
LinkedIn, College placement cell, Industry events, Local tech meetups
Career Connection
Internships provide valuable real-world experience, build industry contacts, and often lead to pre-placement offers, significantly boosting career prospects.
Advanced Stage
Master Advanced ML/DL & Big Data Ecosystems- (Semester 6-7)
Focus on Deep Learning, Cloud Computing for Data Science, and Big Data Analytics. Gain expertise in frameworks like TensorFlow/Keras, PyTorch, and tools in the Hadoop/Spark ecosystem. Explore specialized electives like NLP or Computer Vision.
Tools & Resources
AWS/Azure/GCP free tier, Databricks Community Edition, Hugging Face, Online courses on advanced ML/DL
Career Connection
These advanced skills are critical for roles in cutting-edge AI, cloud-based data solutions, and handling large-scale data, attracting premium placement opportunities.
Undertake a Comprehensive Capstone Project- (Semester 7-8)
Dedicate significant effort to Project Work Phase I and Phase II. Choose a challenging problem, develop an end-to-end solution, and thoroughly document your process. Aim for a publishable quality outcome or a real-world deployed application.
Tools & Resources
Research papers (arXiv), University labs, Faculty mentors, Industry partners
Career Connection
The capstone project is the highlight of an engineering degree, demonstrating cumulative learning and readiness for complex industry challenges, often being a major talking point in interviews.
Prioritize Placement Preparation & Ethical Understanding- (Semester 7-8)
Actively engage with the college placement cell for resume reviews, mock interviews, and group discussion practice. Understand the Ethics and Legal Aspects in Data Science thoroughly, as ethical considerations are increasingly important in AI/ML. Prepare for technical and HR rounds.
Tools & Resources
Placement cell resources, Interview preparation platforms (e.g., InterviewBit), Ethical AI guidelines
Career Connection
Targeted preparation significantly increases the chances of securing desirable placements. Ethical awareness builds trust and opens doors to responsible data roles.
Program Structure and Curriculum
Eligibility:
- Passed 2nd PUC/12th Grade or equivalent examination with English as one of the languages and obtained a minimum of 45% of marks in aggregate in Physics and Mathematics as compulsory subjects along with Chemistry/Biotechnology/Biology/Electronics/Computer Science/Technical Vocational Subject. (40% for SC/ST/Other Backward Classes of Karnataka).
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 |
|---|---|---|---|---|
| 22MA101 | Engineering Mathematics – I | Core | 3 | Differential Calculus, Partial Differential Equations, Vector Calculus, Multiple Integrals, Differential Equations |
| 22PH102 | Engineering Physics | Core | 3 | Modern Physics, Quantum Mechanics, Lasers and Optical Fibers, Material Science, Semiconductor Physics |
| 22CP103 | C Programming for Problem Solving | Core | 3 | C Programming Fundamentals, Control Statements, Functions and Pointers, Arrays and Strings, Structures and Unions |
| 22EL104 | Basic Electrical and Electronics Engineering | Core | 3 | DC and AC Circuits, Electrical Machines, Semiconductor Devices, Digital Electronics, Transistors and Amplifiers |
| 22ME105 | Engineering Graphics | Core | 3 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, Computer-Aided Design (CAD) |
| 22PHL106 | Engineering Physics Lab | Lab | 1 | Optics Experiments, Electricity and Magnetism, Semiconductor Characteristics, Material Properties, Modern Physics Applications |
| 22CPL107 | C Programming Lab | Lab | 1 | C Program Implementation, Debugging Techniques, Algorithm Development, Problem-Solving using C, Data Handling in C |
| 22AML108 | Activities for Multi-disciplinary and Social Connect | Core | 1 | Team Building Activities, Social Responsibility Initiatives, Interdisciplinary Problem Solving, Project Formulation, Community Engagement |
| 22HS109 | Professional Communication Skills | Core | 1 | Listening and Speaking Skills, Reading and Writing Skills, Presentation Techniques, Technical Communication, Interpersonal Communication |
| 22CP110 | Universal Human Values | Core | 1 | Value Education, Harmony in Human Being, Harmony in Family and Society, Harmony in Nature, Professional Ethics |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22MA201 | Engineering Mathematics – II | Core | 3 | Laplace Transforms, Inverse Laplace Transforms, Fourier Series, Partial Differential Equations, Numerical Methods |
| 22CH202 | Engineering Chemistry | Core | 3 | Electrochemistry, Corrosion Science, Water Technology, Fuels and Combustion, Polymers and Composites |
| 22AD203 | Data Structures and Applications | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Heaps, Graphs, Searching and Sorting |
| 22ME204 | Elements of Mechanical Engineering | Core | 3 | Thermodynamics Basics, IC Engines, Refrigeration and Air Conditioning, Power Transmission, Material Science |
| 22CP205 | Object Oriented Programming with C++ | Core | 3 | C++ Fundamentals, Classes and Objects, Inheritance and Polymorphism, Operator Overloading, Exception Handling |
| 22CHL206 | Engineering Chemistry Lab | Lab | 1 | Volumetric Analysis, Instrumental Analysis, Water Quality Testing, Synthesis of Polymers, Corrosion Rate Measurement |
| 22ADL207 | Data Structures Lab | Lab | 1 | Implementation of Data Structures, Algorithm Efficiency Analysis, Stack and Queue Operations, Tree and Graph Traversals, Sorting and Searching Algorithms |
| 22CPL208 | Object Oriented Programming with C++ Lab | Lab | 1 | C++ Program Development, Object-Oriented Design Principles, Class and Object Implementation, Inheritance and Virtual Functions, File I/O in C++ |
| 22CP209 | Environmental Studies | Core | 1 | Ecosystems and Biodiversity, Environmental Pollution, Waste Management, Climate Change, Sustainable Development |
| 22MA210 | Calculus and Linear Algebra for Data Science | Core | 3 | Vector Spaces, Eigenvalues and Eigenvectors, Multivariable Calculus, Optimization Techniques, Probability Distributions |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS301 | Discrete Mathematics for Data Science | Core | 3 | Mathematical Logic, Set Theory and Relations, Functions and Combinatorics, Graph Theory, Trees and Recurrence Relations |
| 22DS302 | Analysis and Design of Algorithms | Core | 3 | Algorithm Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| 22DS303 | Data Mining and Data Warehousing | Core | 4 | Data Warehousing Concepts, OLAP and Data Cubes, Data Preprocessing, Association Rule Mining, Classification and Clustering |
| 22DS304 | Python Programming for Data Science | Core | 3 | Python Fundamentals, NumPy for Numerical Computing, Pandas for Data Manipulation, Data Visualization with Matplotlib, File I/O and Error Handling |
| 22HS305 | Indian Constitution | Core | 1 | Constitutional Principles, Fundamental Rights and Duties, Directive Principles of State Policy, Legislative, Executive, Judiciary, Constitutional Amendments |
| 22DS306 | Professional Skill Development Course 1 | Core | 1 | Communication Skills, Teamwork and Collaboration, Problem Solving, Presentation Skills, Interview Techniques |
| 22DS307 | Data Mining and Data Warehousing Lab | Lab | 1 | Data Preprocessing Techniques, OLAP Operations, Association Rule Implementation, Classification Algorithm Practice, Clustering Algorithm Practice |
| 22DS308 | Python Programming for Data Science Lab | Lab | 1 | NumPy Array Operations, Pandas Dataframe Manipulation, Data Visualization using Matplotlib, Exploratory Data Analysis, Basic Python Scripting for Data |
| 22DS309 | Research Methodology & IPR | Core | 1 | Research Process and Design, Data Collection and Analysis, Report Writing, Intellectual Property Rights, Patents and Copyrights |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS401 | Probability & Statistics for Data Science | Core | 4 | Probability Theory, Random Variables and Distributions, Hypothesis Testing, Regression Analysis, ANOVA and Chi-Square Tests |
| 22DS402 | Database Management Systems | Core | 3 | DBMS Architecture, Entity-Relationship Model, Relational Model and Algebra, SQL Queries, Normalization and Transaction Management |
| 22DS403 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression Algorithms, Classification Algorithms, Model Evaluation and Ensemble Methods |
| 22DS404 | Operating Systems | Core | 3 | OS Concepts and Services, Process Management, CPU Scheduling, Memory Management, File Systems and I/O |
| 22DS405 | Professional Skill Development Course 2 | Core | 1 | Advanced Communication, Critical Thinking, Leadership Skills, Group Dynamics, Conflict Resolution |
| 22DS406 | Database Management Systems Lab | Lab | 1 | SQL Querying Practice, Database Schema Design, PL/SQL Programming, Database Normalization, Transaction Control |
| 22DS407 | Machine Learning Lab | Lab | 1 | Scikit-learn Implementation, Regression Model Training, Classification Model Training, Clustering Algorithm Application, Model Evaluation and Hyperparameter Tuning |
| 22DS408 | Mini Project 1 | Project/Internship | 2 | Problem Identification, System Design, Implementation Phase, Testing and Debugging, Project Documentation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS501 | Big Data Analytics | Core | 4 | Introduction to Big Data, Hadoop Ecosystem, MapReduce Programming, HDFS and YARN, Apache Spark and NoSQL Databases |
| 22DS502 | Artificial Intelligence | Core | 3 | AI Fundamentals, Heuristic Search Techniques, Knowledge Representation, Expert Systems, Introduction to Machine Learning |
| 22DS503 | Web Technologies | Core | 3 | HTML and CSS, JavaScript Fundamentals, Client-Side Scripting, Web Servers and APIs, Responsive Web Design |
| 22DSE5XX | Professional Elective – 1 | Elective | 3 | Applied Data Science, Digital Image Processing, Operations Research, Natural Language Processing |
| 22DSO5XX | Open Elective – 1 | Elective | 3 | Interdisciplinary Topics, Management Principles, Emerging Technologies, Social Sciences, Humanities |
| 22DS506 | Big Data Analytics Lab | Lab | 1 | Hadoop Installation and Configuration, MapReduce Program Implementation, HDFS Operations, Spark Programming, NoSQL Database Operations |
| 22DS507 | Web Technologies Lab | Lab | 1 | HTML/CSS Page Design, JavaScript Interactive Elements, AJAX and JSON, Web API Integration, Frontend Framework Basics |
| 22DS508 | Professional Skill Development Course 3 | Core | 1 | Resume Building, Group Discussion Strategies, Advanced Interview Skills, Corporate Etiquette, Personal Branding |
| 22DS509 | Mini Project 2 | Project/Internship | 2 | Advanced Problem Formulation, Data Science Pipeline Implementation, Model Deployment Basics, Technical Report Writing, Project Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS601 | Deep Learning | Core | 4 | Neural Network Architectures, Backpropagation Algorithm, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs) |
| 22DS602 | Cloud Computing for Data Science | Core | 3 | Cloud Computing Models (IaaS, PaaS, SaaS), Cloud Platforms (AWS, Azure, GCP), Cloud Storage and Databases, Serverless Computing, Cloud Security |
| 22DS603 | Business Intelligence and Data Visualization | Core | 3 | Business Intelligence Concepts, Data Visualization Principles, Dashboard Design, ETL Processes, BI Tools (Tableau/Power BI) |
| 22DSE6XX | Professional Elective – 2 | Elective | 3 | Reinforcement Learning, Computer Vision, Time Series Analysis, Text Analytics |
| 22DSO6XX | Open Elective – 2 | Elective | 3 | Interdisciplinary Engineering, Sustainable Technologies, Advanced Management, Electives from other Departments, Skill-Based Electives |
| 22DS606 | Deep Learning Lab | Lab | 1 | TensorFlow/Keras Implementation, CNNs for Image Classification, RNNs for Sequence Modeling, Transfer Learning, Deep Learning Model Training |
| 22DS607 | Business Intelligence and Data Visualization Lab | Lab | 1 | Data Cleaning and Preparation, Interactive Dashboard Creation, Reporting and Storytelling, BI Tool Usage (Tableau/Power BI), Data Exploration |
| 22DS608 | Internship/Industrial Training | Project/Internship | 2 | Industry Exposure, Practical Skill Application, Real-world Project Experience, Professional Networking, Report Writing |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS701 | Ethics and Legal Aspects in Data Science | Core | 3 | Data Privacy and Security, Ethical AI Principles, Data Governance Frameworks, Legal Compliance (GDPR, Indian Laws), Responsible AI Development |
| 22DSE7XX | Professional Elective – 3 | Elective | 3 | Advanced Machine Learning, Blockchain Technology, Data Governance, Internet of Things (IoT) |
| 22DSE7YY | Professional Elective – 4 | Elective | 3 | Optimization Techniques, Social Network Analysis, Cognitive Computing, Quantum Computing Fundamentals |
| 22DS704 | Project Work Phase I | Project/Internship | 6 | Problem Definition and Literature Survey, System Design and Architecture, Initial Implementation and Prototyping, Methodology Development, Mid-term Review |
| 22DS705 | Research Seminar | Core | 2 | Literature Review, Research Paper Analysis, Technical Presentation Skills, Question and Answer Session, Academic Writing |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS801 | Entrepreneurship & Innovation | Core | 3 | Startup Ecosystem, Business Model Canvas, Innovation Management, Funding and Venture Capital, Intellectual Property Rights |
| 22DSE8XX | Professional Elective – 5 | Elective | 3 | Edge Computing, Financial Data Analytics, Health Informatics, Speech and Audio Processing |
| 22DS803 | Project Work Phase II | Project/Internship | 10 | Final System Implementation, Testing and Evaluation, Performance Analysis, Comprehensive Project Report, Viva-Voce Examination |
| 22DS804 | Internship (if not done in Sem 6) | Project/Internship | 2 | Industry Work Experience, Application of Skills, Project Submission, Professional Development, Industry Best Practices |
