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B-TECH-COMPUTER-SCIENCE-ENGINEERING-DATA-SCIENCE in General at ST. JOSEPH ENGINEERING COLLEGE
Dakshina Kannada, Karnataka
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About the Specialization
What is General at ST. JOSEPH ENGINEERING COLLEGE Dakshina Kannada?
This B.Tech Computer Science Engineering Data Science program at St Joseph Engineering College focuses on equipping students with expertise in extracting insights from complex datasets. It blends core computer science principles with advanced topics in statistics, machine learning, and big data technologies, preparing graduates for the rapidly growing data-driven economy in India. The curriculum is designed to meet the increasing industry demand for skilled data professionals.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude for mathematics, statistics, and logical reasoning, seeking entry into the booming data analytics and artificial intelligence fields. It also benefits those who are passionate about problem-solving using data, aspire to roles in data-driven companies, and are keen to understand the underlying computational and statistical methodologies of modern AI.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths, including Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Developer, and AI Engineer in leading IT firms and startups. Entry-level salaries typically range from INR 4-7 lakhs per annum, with experienced professionals earning significantly higher. The program provides a strong foundation for pursuing higher studies or professional certifications in advanced analytics and AI.
Student Success Practices
Foundation Stage
Master Programming & Mathematical Fundamentals- (Semester 1-2)
Dedicate significant time to thoroughly understand C programming and core mathematical concepts like Calculus, Linear Algebra, and Probability. These form the bedrock for all advanced data science topics. Practice coding daily on platforms and solve problems from textbooks.
Tools & Resources
GeeksforGeeks, HackerRank, Khan Academy, NPTEL online courses
Career Connection
A strong foundation ensures easier grasp of complex algorithms, efficient code development for data processing, and a better understanding of statistical models, crucial for entry-level data roles.
Develop Strong Problem-Solving Skills- (Semester 1-2)
Actively participate in problem-solving competitions and logic puzzles. Focus on breaking down complex problems into smaller, manageable parts and applying computational thinking. This skill is vital for translating real-world data challenges into analytical solutions.
Tools & Resources
CodeChef, LeetCode, TopCoder, College-level coding clubs
Career Connection
Employers highly value candidates who can approach new problems logically and develop effective solutions, directly impacting interview performance and project success.
Engage in Peer Learning and Group Projects- (Semester 1-2)
Form study groups and collaborate on assignments and mini-projects. Explaining concepts to peers solidifies your understanding, and working in teams simulates a professional environment, building essential soft skills.
Tools & Resources
College library study rooms, Online collaboration tools like Google Docs, GitHub for code sharing
Career Connection
Teamwork and communication skills are paramount in industry. Collaborative learning enhances your ability to work in data science teams and contributes to successful project delivery.
Intermediate Stage
Build a Portfolio with Data Science Projects- (Semester 3-5)
Start working on personal data science projects beyond coursework. Use real-world datasets from platforms and apply learned concepts (e.g., build a movie recommender, analyze social media trends). Document your code and findings on GitHub.
Tools & Resources
Kaggle, UCI Machine Learning Repository, GitHub, Jupyter Notebooks
Career Connection
A strong project portfolio is crucial for showcasing practical skills to recruiters, helping you stand out for internships and placements in Indian tech companies.
Acquire Proficiency in Python and SQL- (Semester 3-5)
Deepen your expertise in Python for data manipulation (Pandas, NumPy), visualization (Matplotlib, Seaborn), and machine learning (Scikit-learn). Simultaneously, master SQL for database querying and management, as both are industry staples.
Tools & Resources
DataCamp, Coursera (Python for Data Science), SQL Zoo, W3Schools
Career Connection
These are the two most demanded skills for data science roles in India. Proficiency directly translates to higher chances of securing roles as a Data Analyst or Junior Data Scientist.
Seek Early Industry Exposure through Internships- (Semester 4-5 (Summer breaks))
Actively look for summer or short-term internships in startups or small to medium-sized enterprises (SMEs) specializing in data. Even unpaid internships offer invaluable practical experience and networking opportunities.
Tools & Resources
Internshala, LinkedIn Jobs, College Placement Cell, Naukri.com
Career Connection
Practical experience before final year significantly boosts your resume, provides real-world context to theoretical knowledge, and often leads to pre-placement offers.
Advanced Stage
Specialize and Engage in Advanced Research- (Semester 6-8)
Identify a specific area within data science (e.g., Deep Learning, NLP, Big Data) that aligns with your interests and career goals. Take relevant electives, pursue online certifications, and contribute to research projects with faculty members.
Tools & Resources
Google Scholar, arXiv, Specialized MOOCs (edX, Udacity), Research labs in college
Career Connection
Specialization makes you a more attractive candidate for niche roles and high-growth areas, while research experience demonstrates analytical rigor and problem-solving at an advanced level.
Prepare Rigorously for Placements- (Semester 7-8)
Beyond technical skills, focus on aptitude tests, logical reasoning, and communication skills required for Indian recruitment processes. Participate in mock interviews, refine your resume, and practice explaining your projects clearly and concisely.
Tools & Resources
Placement training programs (internal/external), Glassdoor, AmbitionBox, PrepInsta
Career Connection
Comprehensive preparation is key to navigating the competitive Indian job market and securing lucrative placements in top-tier companies or startups.
Network Professionally and Build a Personal Brand- (Semester 6-8 and beyond)
Attend industry conferences, workshops, and webinars. Connect with professionals, alumni, and faculty on LinkedIn. Share your project work and insights to establish your online presence as an aspiring data scientist.
Tools & Resources
LinkedIn, Meetup groups, Industry conferences (e.g., Data Science Congress India), College alumni network
Career Connection
Networking opens doors to hidden job opportunities, mentorship, and collaboration, significantly enhancing your career trajectory in the long term in the Indian data ecosystem.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 with Physics, Mathematics, and one of Chemistry/Biotechnology/Biology/Electronics/Computer Science with English as one of the languages, and obtained at least 45% marks (40% for reserved category) in the aggregate of the optional subjects, along with a valid rank in competitive examinations like KCET/COMEDK/JEE Main.
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 40% (Continuous Internal Evaluation - CIE), External: 60% (Semester End Examination - SEE)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MATS11 | Calculus and Differential Equations | Basic Science Course (BSC) | 4 | Differential Calculus, Integral Calculus, Vector Calculus, Ordinary Differential Equations, Laplace Transforms |
| 21PCD12 | Programming for Problem Solving | Engineering Science Course (ESC) | 4 | Introduction to C Programming, Control Structures, Functions and Arrays, Pointers and Structures, File Handling |
| 21ELN13 | Basic Electrical and Electronics Engineering | Engineering Science Course (ESC) | 3 | DC Circuits, AC Circuits, Semiconductor Diodes, Transistors, Digital Logic Basics |
| 21PCDL14 | Programming for Problem Solving Lab | Lab | 1 | C Programming Basics, Conditional Statements, Looping Constructs, Functions and Arrays, Strings and Pointers |
| 21ELNL15 | Basic Electrical and Electronics Engineering Lab | Lab | 1 | Ohm''''s Law Verification, KCL/KVL Verification, Diode Characteristics, Transistor Amplifier, Logic Gates Implementation |
| 21EGH16 | Communicative English | Humanities and Social Sciences (HSMC) | 1 | Grammar and Vocabulary, Reading Comprehension, Written Communication, Oral Communication, Presentation Skills |
| 21FHT17 | Foundations of Health | Ability Enhancement Course (AEC) | 1 | Holistic Health, Nutritional Science, Physical Fitness, Mental Wellbeing, Preventive Health |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21MATS21 | Linear Algebra and Statistics | Basic Science Course (BSC) | 4 | Matrices and Determinants, Vector Spaces, Eigenvalues and Eigenvectors, Probability Theory, Statistical Distributions |
| 21PHY22 | Engineering Physics | Basic Science Course (BSC) | 3 | Quantum Mechanics, Lasers and Fiber Optics, Materials Science, Semiconductor Physics, Nanotechnology |
| 21CIV23 | Elements of Civil Engineering and Mechanics | Engineering Science Course (ESC) | 3 | Building Materials, Surveying, Mechanics of Solids, Fluid Mechanics, Structural Systems |
| 21CHL24 | Engineering Chemistry | Basic Science Course (BSC) | 3 | Electrochemistry, Corrosion, Fuels and Combustion, Polymers, Water Technology |
| 21CHEL25 | Engineering Chemistry Lab | Lab | 1 | Water Quality Analysis, Instrumental Methods, Synthesis of Polymers, Chemical Kinetics, Volumetric Analysis |
| 21PHYL26 | Engineering Physics Lab | Lab | 1 | Laser Experiments, Optical Fiber Communication, Semiconductor Device Studies, Magnetic Hysteresis, Planck''''s Constant Determination |
| 21CPL27 | Computer Aided Engineering Graphics | Engineering Science Course (ESC) | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Assembly Drawings, Computer Aided Drafting |
| 21SFH28 | Scientific Foundations of Health | Ability Enhancement Course (AEC) | 1 | Basic Anatomy, Physiological Systems, Disease Mechanisms, Diagnostic Tools, Public Health Principles |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS31 | Data Structures and Applications | Professional Core Course (PCC) | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Searching and Sorting, Hashing Techniques |
| 21CS32 | Analog and Digital Electronics | Professional Core Course (PCC) | 3 | Operational Amplifiers, Logic Gates, Combinational Circuits, Sequential Circuits, Analog to Digital Conversion |
| 21CS33 | Computer Organization and Architecture | Professional Core Course (PCC) | 3 | Basic Computer Functions, Instruction Set Architecture, CPU Organization, Memory System, Input/Output Organization |
| 21CS34 | Object Oriented Programming with JAVA | Professional Core Course (PCC) | 4 | OOP Concepts, Java Fundamentals, Inheritance and Polymorphism, Interfaces and Packages, Exception Handling and Multithreading |
| 21DS35 | Probability and Statistics for Data Science | Professional Core Course (PCC) - Data Science Specific | 3 | Probability Foundations, Random Variables, Descriptive Statistics, Inferential Statistics, Hypothesis Testing |
| 21CSL36 | Data Structures Lab | Lab | 1 | Linked List Operations, Stack and Queue Implementation, Tree Traversal, Graph Algorithms, Sorting Algorithms |
| 21CSL37 | Analog and Digital Electronics Lab | Lab | 1 | Logic Gate Verification, Adders and Subtractors, Flip-Flops, Counters and Registers, Op-Amp Circuits |
| 21DS38 | JAVA Programming Lab | Lab | 1 | Classes and Objects in Java, Inheritance and Interfaces, Exception Handling, Multithreading, GUI Programming Basics |
| 21HSM39 | Universal Human Values | Humanities and Social Sciences (HSMC) | 1 | Self-Exploration, Human Values, Harmony in Family, Harmony in Society, Harmony in Nature |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21CS41 | Design and Analysis of Algorithms | Professional Core Course (PCC) | 4 | Algorithm Analysis, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| 21CS42 | Operating Systems | Professional Core Course (PCC) | 3 | Process Management, CPU Scheduling, Memory Management, Virtual Memory, File Systems |
| 21CS43 | Microcontrollers and Embedded Systems | Professional Core Course (PCC) | 3 | 8051 Microcontroller Architecture, Assembly Language Programming, Interfacing with Peripherals, Embedded System Design, Real-time Operating Systems |
| 21DS44 | Database Management Systems | Professional Core Course (PCC) - Data Science Specific | 4 | Relational Model, SQL Queries, Database Design, Normalization, Transaction Management |
| 21DS45 | Foundations of Data Science | Professional Core Course (PCC) - Data Science Specific | 3 | Data Science Lifecycle, Data Collection and Cleaning, Exploratory Data Analysis, Introduction to Machine Learning, Data Visualization Basics |
| 21CSL46 | Operating Systems Lab | Lab | 1 | Shell Programming, Process Creation, CPU Scheduling Algorithms, Deadlock Avoidance, Memory Allocation |
| 21DSL47 | DBMS Lab with Mini Project | Lab | 1 | DDL and DML Commands, SQL Joins and Subqueries, PL/SQL Programming, Database Project Implementation, Data Integrity Constraints |
| 21DSL48 | Foundations of Data Science Lab | Lab | 1 | Python for Data Science, Numpy and Pandas, Matplotlib for Visualization, Data Preprocessing Techniques, Basic Machine Learning Models |
| 21CIV49 | Environmental Studies | Ability Enhancement Course (AEC) | 1 | Ecology and Ecosystems, Environmental Pollution, Biodiversity Conservation, Renewable Energy, Sustainable Development |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21DS51 | Computer Networks | Professional Core Course (PCC) | 4 | Network Topologies, OSI and TCP/IP Models, Data Link Layer, Network Layer, Transport and Application Layers |
| 21DS52 | Python Programming for Data Science | Professional Core Course (PCC) - Data Science Specific | 3 | Advanced Python Concepts, NumPy for numerical computing, Pandas for data manipulation, Matplotlib and Seaborn for visualization, Scikit-learn for basic ML |
| 21DS53 | Machine Learning | Professional Core Course (PCC) - Data Science Specific | 4 | Supervised Learning, Unsupervised Learning, Reinforcement Learning, Model Evaluation, Feature Engineering |
| 21DS54X | Professional Elective - I (e.g., Big Data Analytics, Cloud Computing) | Professional Elective Course (PEC) | 3 | Specific topics depend on elective choice. E.g., for Big Data Analytics: Hadoop Ecosystem, MapReduce, Spark |
| 21DS55X | Open Elective - I (e.g., IoT, Image Processing) | Open Elective Course (OEC) | 3 | Specific topics depend on elective choice. E.g., for IoT: Sensor Networks, IoT Protocols, Cloud Integration |
| 21DSL56 | Computer Networks Lab | Lab | 1 | Network Simulation Tools, Socket Programming, Packet Analysis, Routing Protocols, Network Security Basics |
| 21DSL57 | Machine Learning Lab | Lab | 1 | Implementing Regression Models, Classification Algorithms, Clustering Techniques, Dimensionality Reduction, Model Hyperparameter Tuning |
| 21DSP58 | Mini Project with Python | Project Work (PWP) | 2 | Problem Identification, Data Collection and Preprocessing, Model Selection and Implementation, Evaluation and Reporting, Software Development Lifecycle |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21DS61 | Data Warehousing and Data Mining | Professional Core Course (PCC) - Data Science Specific | 4 | Data Warehouse Architecture, OLAP Operations, Association Rule Mining, Classification and Prediction, Cluster Analysis |
| 21DS62 | Big Data Analytics | Professional Core Course (PCC) - Data Science Specific | 4 | Big Data Concepts, Hadoop Ecosystem, MapReduce Programming, Spark Framework, NoSQL Databases |
| 21DS63 | Deep Learning | Professional Core Course (PCC) - Data Science Specific | 4 | Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Transfer Learning, Deep Learning Frameworks (TensorFlow/PyTorch) |
| 21DS64X | Professional Elective - II (e.g., Natural Language Processing, Computer Vision) | Professional Elective Course (PEC) | 3 | Specific topics depend on elective choice. E.g., for NLP: Text Preprocessing, Word Embeddings, Sentiment Analysis |
| 21DS65X | Open Elective - II (e.g., Web Technologies, Mobile Application Development) | Open Elective Course (OEC) | 3 | Specific topics depend on elective choice. E.g., for Web Technologies: HTML, CSS, JavaScript, Server-side Scripting |
| 21DSL66 | Big Data Analytics Lab | Lab | 1 | Hadoop Installation and Configuration, MapReduce Programs, Hive and Pig Queries, Spark Applications, Data Streaming with Kafka |
| 21DSL67 | Deep Learning Lab | Lab | 1 | Neural Network Implementation, CNN for Image Classification, RNN for Sequence Data, Hyperparameter Tuning, Model Deployment Basics |
| 21DS68 | Internship / Mini-Project | Project Work (PWP) | 2 | Industry Problem Solving, Team Collaboration, Report Writing, Presentation Skills, Real-world Data Application |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21DS71 | Reinforcement Learning | Professional Core Course (PCC) - Data Science Specific | 3 | Markov Decision Processes, Dynamic Programming in RL, Monte Carlo Methods, Temporal Difference Learning, Policy Gradient Methods |
| 21DS72 | Natural Language Processing | Professional Core Course (PCC) - Data Science Specific | 4 | Text Preprocessing, Word Embeddings, Sequence Models, Sentiment Analysis, Machine Translation |
| 21DS73X | Professional Elective - III (e.g., Data Privacy and Security, Blockchain Technologies) | Professional Elective Course (PEC) | 3 | Specific topics depend on elective choice. E.g., for Data Privacy: Data Anonymization, Differential Privacy, Homomorphic Encryption |
| 21DS74X | Professional Elective - IV (e.g., Computer Vision, Recommender Systems) | Professional Elective Course (PEC) | 3 | Specific topics depend on elective choice. E.g., for Computer Vision: Image Features, Object Detection, Image Segmentation |
| 21DSP75 | Project Work Phase - I | Project Work (PWP) | 4 | Literature Survey, Problem Definition, System Design, Methodology Planning, Initial Prototyping |
| 21DSS76 | Technical Seminar | Seminar | 1 | Research Skill Development, Technical Paper Analysis, Presentation Techniques, Public Speaking, Current Technology Trends |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21DS81 | Professional Practice | Professional Core Course (PCC) | 3 | Ethical Hacking, Intellectual Property Rights, Entrepreneurship, Cyber Law, Professional Ethics |
| 21DS82X | Professional Elective - V (e.g., Internet of Things, Advanced Data Mining) | Professional Elective Course (PEC) | 3 | Specific topics depend on elective choice. E.g., for IoT: Sensor Networks, IoT Security, Edge Computing |
| 21DSP83 | Project Work Phase - II | Project Work (PWP) | 10 | System Implementation, Testing and Debugging, Performance Evaluation, Report Generation, Final Presentation |
