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B-TECH-BACHELOR-OF-TECHNOLOGY-SIT-PUNE in Data Science at Symbiosis International University (SIU)
Pune, Maharashtra
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About the Specialization
What is Data Science at Symbiosis International University (SIU) Pune?
This B.Tech Data Science program at Symbiosis Institute of Technology focuses on equipping students with advanced computational and analytical skills vital for the rapidly growing data-driven Indian industry. It delves into machine learning, big data technologies, and deep learning, preparing graduates for complex data challenges. The program aims to create professionals who can extract actionable insights from vast datasets.
Who Should Apply?
This program is ideal for aspiring data scientists, machine learning engineers, and data analysts. It caters to fresh graduates with a strong mathematical and programming aptitude seeking entry into cutting-edge technology roles, as well as those looking to upskill for a career transition into the booming AI and data sector in India. Prerequisites typically include a 10+2 science background with PCM.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths as Data Scientists, AI/ML Engineers, Business Intelligence Analysts, and Data Architects. Entry-level salaries range from INR 6-10 LPA, growing significantly with experience. The program aligns with industry demand, fostering critical thinking and problem-solving skills for various Indian tech companies, startups, and research organizations.
Student Success Practices
Foundation Stage
Master Programming Fundamentals (C/C++/Python)- (Semester 1-2)
Dedicate time to thoroughly understand programming concepts (C/C++ in early semesters, Python for Data Science). Practice extensively on platforms like HackerRank, GeeksforGeeks, and CodeChef to build strong logical and problem-solving abilities.
Tools & Resources
HackerRank, GeeksforGeeks, CodeChef, VS Code
Career Connection
A solid foundation in programming is essential for cracking technical interviews and implementing algorithms efficiently, directly impacting placement readiness for software development and data roles.
Build a Strong Mathematical & Statistical Base- (Semester 1-3)
Pay close attention to Engineering Mathematics and Discrete Mathematics. Supplement classroom learning with online courses on linear algebra, calculus, probability, and statistics from platforms like NPTEL or Coursera, focusing on practical applications.
Tools & Resources
NPTEL courses, Coursera (Mathematics for Machine Learning), Khan Academy
Career Connection
Data Science relies heavily on mathematical principles. A strong grasp ensures better understanding of ML algorithms, enabling advanced research roles and better model interpretation, highly valued in analytics jobs.
Engage in Peer Learning and Group Projects- (Semester 1-2)
Actively participate in study groups and collaborate on class projects. Discuss challenging concepts with peers, teach others, and divide tasks in group assignments to learn teamwork and different perspectives on problem-solving.
Tools & Resources
GitHub (for collaborative coding), Discord/WhatsApp groups for discussion
Career Connection
Collaboration skills are paramount in industry. Working effectively in teams prepares you for real-world project environments and enhances your ability to communicate complex ideas, a key aspect of any tech role.
Intermediate Stage
Undertake Data Science Mini-Projects- (Semester 3-5)
Apply concepts learned in Data Structures, DBMS, and Machine Learning by undertaking small projects. Work on datasets from Kaggle, build predictive models, visualize data, and document your process. Focus on real-world Indian contexts where possible.
Tools & Resources
Kaggle, Jupyter Notebook, Python (Pandas, Scikit-learn, Matplotlib)
Career Connection
Practical projects demonstrate your ability to apply theoretical knowledge, enhancing your portfolio for internships and job applications, especially for data analyst and junior data scientist roles.
Participate in Hackathons and Coding Competitions- (Semester 4-5)
Engage in inter-college or online hackathons focused on data science and machine learning. This sharpens your problem-solving under pressure, fosters innovation, and provides exposure to diverse industry challenges. Look for India-specific challenges.
Tools & Resources
Devpost, D2C (Dare2Compete), Online coding platforms
Career Connection
Winning or actively participating in competitions stands out on resumes, showcases your competitive spirit, and can lead to networking opportunities with industry professionals and recruiters.
Explore Open-Source Contributions- (Semester 4-6)
Start contributing to open-source projects related to data science tools or libraries. Even small bug fixes or documentation improvements can build your technical credibility and expose you to professional coding standards and version control.
Tools & Resources
GitHub, Stack Overflow, Git
Career Connection
Open-source contributions provide tangible proof of your coding skills and ability to collaborate in a professional environment, making you a more attractive candidate for product-based companies.
Advanced Stage
Secure Relevant Internships- (Semester 6-7 (Summer breaks))
Seek internships in data science, machine learning, or analytics roles, preferably in Indian startups or established companies. Focus on gaining hands-on experience with real-world data, tools, and project methodologies. Utilize university placement cells and online portals.
Tools & Resources
LinkedIn, Internshala, University Placement Portal
Career Connection
Internships are critical for practical experience, industry networking, and often convert into full-time employment offers, significantly boosting your career launch in the Indian job market.
Specialize and Build a Portfolio- (Semester 7-8)
Leverage electives to specialize in areas like NLP, Computer Vision, or Reinforcement Learning. Develop a comprehensive online portfolio (GitHub, personal website) showcasing your major projects, hackathon achievements, and analytical insights, focusing on impactful solutions.
Tools & Resources
GitHub Pages, Hashnode, Medium, personal domain
Career Connection
A strong, specialized portfolio is your resume in the data science field, directly demonstrating your expertise and problem-solving capabilities to potential employers, leading to high-quality placements.
Prepare for Placements Strategically- (Semester 7-8)
Engage in rigorous placement preparation, focusing on aptitude, data structures and algorithms, core data science concepts, and HR interviews. Participate in mock interviews, refine your resume, and tailor your application to specific company requirements. Practice case studies relevant to Indian market scenarios.
Tools & Resources
LeetCode, Interviewer.ai, Glassdoor, career services
Career Connection
Proactive and strategic placement preparation significantly increases your chances of securing desirable job offers from top companies, ensuring a successful transition from academia to industry.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 with Physics and Mathematics as compulsory subjects along with one of the Chemistry/Biotechnology/Biology/Technical Vocational subject. Obtained at least 45% marks (40% in case of candidates belonging to SC/ST category) in the above subjects taken together. Valid score in SITEEE/JEE (Main)/any State Level Engineering Entrance Examination.
Duration: 4 years / 8 semesters
Credits: 166 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22EN0101 | Engineering Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Matrices and Determinants, Vector Calculus, Applications in Engineering |
| 22PC0102 | Engineering Physics | Core | 3 | Quantum Physics, Optics and Lasers, Semiconductor Physics, Electromagnetism, Nano Technology |
| 22PC0103 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Single Phase Transformers, DC Machines, Three Phase Systems |
| 22PC0104 | C Programming for Problem Solving | Core | 3 | Introduction to Programming, Control Structures, Functions and Arrays, Pointers and Strings, Structures and Files |
| 22HS0105 | Communication Skills | Core | 2 | Verbal Communication, Non-verbal Communication, Presentation Skills, Report Writing, Group Discussions |
| 22PC0106 | Engineering Physics Lab | Lab | 1 | Lasers and Fiber Optics Experiments, Semiconductor Device Characteristics, Magnetic Field Measurements, Optical Phenomena, Error Analysis |
| 22PC0107 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Circuit Laws, Resonance in AC Circuits, Transformer Load Test, DC Motor Characteristics, Three-Phase Power Measurement |
| 22PC0108 | C Programming Lab | Lab | 1 | Conditional Statements and Loops, Array and String Operations, Function Implementation, Pointer Arithmetic, File Handling Programs |
| 22HS0109 | Communication Skills Lab | Lab | 1 | Public Speaking Practice, Interview Skills Simulation, Technical Report Writing Exercises, Effective Listening Drills, Resume Building Workshop |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22EN0201 | Engineering Mathematics-II | Core | 4 | Differential Equations, Laplace Transforms, Fourier Series, Partial Differential Equations, Complex Numbers |
| 22PC0202 | Engineering Chemistry | Core | 3 | Water Treatment, Corrosion and its Control, Electrochemistry, Polymers, Fuels and Combustion |
| 22PC0203 | Basic Electronics Engineering | Core | 3 | Diode Circuits, Transistors (BJT & FET), Operational Amplifiers, Digital Logic Gates, Power Supplies |
| 22PC0204 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Techniques |
| 22PC0205 | Engineering Graphics & Design | Core | 2 | Orthographic Projections, Sectional Views, Isometric Projections, CAD Software Basics, Dimensioning and Tolerancing |
| 22PC0206 | Engineering Chemistry Lab | Lab | 1 | Water Hardness Determination, pH Metry and Potentiometry, Viscosity and Surface Tension, Conductometry Experiments, Titration Techniques |
| 22PC0207 | Basic Electronics Engineering Lab | Lab | 1 | Diode Rectifier Circuits, Transistor Amplifier Design, OP-AMP Applications, Logic Gate Verification, Voltage Regulator Circuits |
| 22PC0208 | Data Structures Lab | Lab | 1 | Array and Linked List Implementations, Stack and Queue Operations, Tree Traversal Algorithms, Graph Representation and Traversal, Sorting and Searching Program |
| 22PC0209 | Workshop/Manufacturing Practice | Lab | 1 | Fitting Shop, Carpentry Shop, Welding Shop, Foundry Shop, Sheet Metal Working |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22EN0301 | Engineering Mathematics-III | Core | 4 | Linear Algebra, Probability and Statistics, Numerical Methods, Transform Techniques, Optimization |
| 22PC0302 | Discrete Mathematics | Core | 3 | Set Theory and Logic, Relations and Functions, Graph Theory, Counting and Probability, Algebraic Structures |
| 22PC0303 | Object Oriented Programming with C++ | Core | 3 | Classes and Objects, Inheritance and Polymorphism, Encapsulation and Abstraction, Constructors and Destructors, File I/O and Exception Handling |
| 22PC0304 | Database Management Systems | Core | 3 | ER Model, Relational Algebra, SQL Queries, Normalization, Transaction Management |
| 22PC0305 | Introduction to Data Science | Core | 3 | Data Science Lifecycle, Data Collection and Cleaning, Exploratory Data Analysis, Introduction to Machine Learning, Data Visualization Basics |
| 22PC0306 | Object Oriented Programming Lab | Lab | 1 | Class and Object Implementation, Inheritance and Virtual Functions, Operator Overloading, Templates and STL, Exception Handling Programs |
| 22PC0307 | Database Management Systems Lab | Lab | 1 | DDL and DML Commands, SQL Joins and Subqueries, Stored Procedures and Triggers, Database Design Exercises, Data Manipulation with SQL |
| 22PC0308 | Introduction to Data Science Lab | Lab | 1 | Python Basics for Data Science, Numpy and Pandas for Data Manipulation, Matplotlib for Visualization, Scikit-learn for Basic ML, Data Preprocessing Techniques |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22PC0401 | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, Deadlocks, I/O Systems |
| 22PC0402 | Computer Networks | Core | 3 | OSI and TCP/IP Models, Data Link Layer, Network Layer, Transport Layer, Application Layer Protocols |
| 22PC0403 | Design and Analysis of Algorithms | Core | 3 | Algorithm Complexity, Greedy Algorithms, Dynamic Programming, Divide and Conquer, Graph Algorithms |
| 22DS0404 | Statistical Methods for Data Science | Core | 3 | Descriptive Statistics, Probability Distributions, Hypothesis Testing, Regression Analysis, ANOVA |
| 22DS0405 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Model Evaluation, Bias-Variance Tradeoff, Ensemble Methods |
| 22PC0406 | Operating Systems Lab | Lab | 1 | Shell Programming, Process Creation and Management, CPU Scheduling Algorithms, Memory Allocation Strategies, Deadlock Avoidance Simulation |
| 22PC0407 | Computer Networks Lab | Lab | 1 | Socket Programming, Network Configuration Commands, Packet Sniffing and Analysis, Routing Protocols Implementation, Network Security Tools |
| 22DS0408 | Machine Learning Lab | Lab | 1 | Linear Regression Implementation, Classification Algorithms (e.g., SVM, Decision Tree), Clustering Techniques (K-Means), Feature Engineering, Model Performance Metrics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS0501 | Big Data Technologies | Core | 3 | Hadoop Ecosystem, MapReduce Framework, Spark Architecture, NoSQL Databases, Data Warehousing |
| 22DS0502 | Deep Learning | Core | 3 | Neural Network Architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning, Generative Adversarial Networks (GANs) |
| 22HS0503 | Universal Human Values | Core | 3 | Understanding Harmony, Relationship and Trust, Professional Ethics, Holistic Development, Societal Values |
| 22DS0504 | Elective I (Data Science Stream) | Elective | 3 | Choice from advanced topics like Reinforcement Learning, Natural Language Processing, Computer Vision, etc. |
| 22DS0505 | Elective II (Open Elective) | Elective | 3 | Choice from interdisciplinary subjects across engineering streams or management |
| 22DS0506 | Big Data Technologies Lab | Lab | 1 | HDFS Operations, MapReduce Programming, Spark Data Processing, Hive and Pig Queries, NoSQL Database Operations (e.g., MongoDB) |
| 22DS0507 | Deep Learning Lab | Lab | 1 | Building ANNs with Keras/TensorFlow, CNN for Image Classification, RNN for Sequence Data, Hyperparameter Tuning, Pre-trained Model Usage |
| 22DS0508 | Minor Project-I | Project | 2 | Problem Identification, Literature Review, System Design, Implementation, Report Writing and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS0601 | Data Visualization | Core | 3 | Principles of Visualization, Types of Charts and Graphs, Interactive Visualizations, Data Storytelling, Tools like Tableau/PowerBI |
| 22DS0602 | Cloud Computing for Data Science | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Platforms (AWS, Azure, GCP), Cloud Storage Solutions, Serverless Computing, Data Analytics Services in Cloud |
| 22DS0603 | Elective III (Data Science Stream) | Elective | 3 | Advanced topics like Recommendation Systems, Time Series Analysis, IoT Data Analytics, etc. |
| 22DS0604 | Elective IV (Open Elective) | Elective | 3 | Choice from interdisciplinary subjects or subjects from other engineering streams |
| 22DS0605 | Professional Ethics & Intellectual Property Rights | Core | 3 | Ethical Theories, Cyber Ethics, IPR Laws in India, Patents, Copyrights, Trademarks, Ethical Hacking and Privacy |
| 22DS0606 | Data Visualization Lab | Lab | 1 | Tableau/PowerBI Dashboard Creation, Python Libraries (Seaborn, Plotly), Geospatial Data Visualization, Time Series Visualizations, Interactive Report Generation |
| 22DS0607 | Cloud Computing for Data Science Lab | Lab | 1 | AWS/Azure/GCP VM Instances, Cloud Storage (S3, Blob Storage), Setting up Data Lakes, Using Cloud-based ML Services, Containerization with Docker |
| 22DS0608 | Minor Project-II | Project | 2 | Advanced Problem Solving, Team Collaboration, System Integration, Testing and Debugging, Project Documentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS0701 | Data Mining and Warehousing | Core | 3 | Data Preprocessing, Association Rule Mining, Classification Techniques, Clustering Algorithms, Data Cube and OLAP |
| 22DS0702 | Natural Language Processing | Core | 3 | Text Preprocessing, Word Embeddings, Sentiment Analysis, Named Entity Recognition, Machine Translation |
| 22HS0703 | Constitution of India | Core | 2 | Preamble and Basic Structure, Fundamental Rights and Duties, Directive Principles, Union and State Government, Judiciary and Elections |
| 22DS0704 | Elective V (Data Science Stream) | Elective | 3 | Specialized topics like Reinforcement Learning, Computer Vision, Geospatial Data Analytics, etc. |
| 22DS0705 | Elective VI (Open Elective) | Elective | 3 | Advanced topics from other domains or a project-based elective |
| 22DS0706 | Data Mining and Warehousing Lab | Lab | 1 | Data Cleaning and Transformation, Implementing Association Rules, Classification Model Building, Clustering Analysis, OLAP Cube Operations |
| 22DS0707 | Natural Language Processing Lab | Lab | 1 | Text Preprocessing using NLTK/SpaCy, Building Language Models, Sentiment Analysis Implementation, Chatbot Development Basics, Word Embedding Generation |
| 22DS0708 | Major Project-I | Project | 4 | Problem Formulation, System Design and Architecture, Technology Stack Selection, Initial Implementation, Progress Reporting |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 22DS0801 | Data Governance and Ethics | Core | 3 | Data Privacy Regulations (GDPR, India''''s Data Protection Bill), Ethical AI Principles, Data Security Best Practices, Bias and Fairness in AI, Data Quality Management |
| 22HS0802 | Project Management & Entrepreneurship | Core | 3 | Project Life Cycle, Risk Management, Budgeting and Scheduling, Business Plan Development, Startup Ecosystem in India |
| 22DS0803 | Major Project-II | Project | 12 | Advanced System Development, Extensive Testing and Validation, Performance Optimization, Comprehensive Documentation, Final Presentation and Viva Voce |
