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B-TECH in Data Science Analytics Via Honors at Symbiosis International University
Pune, Maharashtra
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About the Specialization
What is Data Science & Analytics (via honors) at Symbiosis International University Pune?
This B.Tech Data Science & Analytics program at Symbiosis International University focuses on equipping students with expertise in data manipulation, analysis, and interpretation to drive informed decision-making. In the rapidly evolving Indian industry landscape, characterized by digital transformation and a data-first approach, this specialization is crucial for developing professionals capable of extracting valuable insights from complex datasets. The program''''s integrated curriculum, blending computer science fundamentals with advanced statistical and machine learning techniques, positions it as a cutting-edge pathway for aspiring data professionals.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics, statistics, and programming, who are seeking entry into high-growth data-driven roles. It also benefits working professionals from IT or related fields looking to upskill in modern data technologies and analytics. Individuals aspiring to become Data Scientists, Machine Learning Engineers, Data Analysts, or Business Intelligence Developers in various Indian sectors, from e-commerce to finance, will find this curriculum highly relevant, provided they meet the academic prerequisites.
Why Choose This Course?
Graduates of this program can expect to pursue dynamic career paths in India''''s booming data economy. Entry-level salaries for Data Scientists typically range from INR 6-10 LPA, escalating significantly with experience to INR 15-30 LPA or more for senior roles. Growth trajectories include leading data teams, specializing in AI/ML research, or becoming data architects. The curriculum aligns with the skills required for certifications from leading platforms, enhancing employability and professional credibility within Indian and global companies.
Student Success Practices
Foundation Stage
Master Programming & Mathematical Fundamentals- (Semester 1-2)
Dedicate significant time to mastering core programming languages like C/C++/Python and fundamental data structures. Simultaneously, build a strong foundation in Engineering Mathematics, Probability & Statistics, as these form the bedrock for advanced data science concepts. Utilize online platforms for practice.
Tools & Resources
Hackerrank, LeetCode, GeeksforGeeks, Khan Academy (for Math), NPTEL courses on Data Structures
Career Connection
A robust foundation ensures strong problem-solving skills, crucial for technical interviews and developing efficient data-driven solutions in early career roles.
Active Participation in Labs and Problem Solving- (Semester 1-2)
Engage actively in all laboratory sessions, treating them as opportunities for hands-on learning rather than mere task completion. Focus on understanding the ''''why'''' behind each step and explore alternative solutions. Form study groups for collaborative problem-solving.
Tools & Resources
University Labs, GitHub for collaborative coding, Online programming contests
Career Connection
Develops practical implementation skills and fosters a problem-solving mindset, highly valued by employers for entry-level engineering positions.
Develop Strong Communication Skills- (Semester 1-2)
Actively participate in communication skills workshops and courses. Practice public speaking, technical report writing, and professional email etiquette. Join college clubs focused on debates or presentations to enhance soft skills early on.
Tools & Resources
Toastmasters International (if available), Grammarly, University communication labs, College debate/public speaking clubs
Career Connection
Effective communication is critical for collaborating in teams, presenting project outcomes, and excelling in interviews, opening doors to leadership roles.
Intermediate Stage
Undertake Mini-Projects and Kaggle Competitions- (Semester 3-5)
Beyond coursework, actively seek out and complete mini-projects in Machine Learning, DBMS, and AI using real-world datasets. Participate in Kaggle competitions to apply theoretical knowledge, learn from peers, and build a demonstrable portfolio of practical skills.
Tools & Resources
Kaggle, UCI Machine Learning Repository, Google Colab, GitHub
Career Connection
Showcases practical application abilities, enhances resume, and provides talking points for interviews, making you a competitive candidate for data science internships.
Pursue Domain-Specific Electives and Certifications- (Semester 3-5)
Strategically choose professional electives that align with emerging data science trends (e.g., Deep Learning, Big Data Analytics). Consider external certifications in specific tools or areas like SQL, Python for Data Science, or cloud platforms (AWS/Azure/GCP) from platforms like Coursera, edX.
Tools & Resources
Coursera, edX, NPTEL (for specialized courses), Vendor-specific certifications (e.g., AWS Certified Cloud Practitioner)
Career Connection
Specialized knowledge and certifications make you more attractive to employers seeking specific skill sets, improving chances for targeted roles in the Indian tech industry.
Network with Industry Professionals and Alumni- (Semester 3-5)
Attend industry seminars, webinars, and workshops organized by the university or external bodies. Connect with alumni and professionals on platforms like LinkedIn to gain insights into industry trends, career paths, and potential internship opportunities in the Indian market.
Tools & Resources
LinkedIn, College career fairs, Industry conferences (e.g., Data Science Summit India)
Career Connection
Building a professional network can lead to mentorship, internship offers, and invaluable career advice, giving an edge in competitive Indian job market.
Advanced Stage
Focus on Comprehensive Major Project & Internship- (Semester 6-8)
Dedicate significant effort to your Major Project, aiming for a novel solution to a real-world problem, potentially collaborating with industry. Utilize the summer internship to gain intensive practical experience, converting it into a pre-placement offer if possible.
Tools & Resources
University Research Labs, Industry partners, Advanced ML/DL frameworks, Version control systems (Git)
Career Connection
A high-quality major project and relevant internship are critical for showcasing deep technical skills and industry readiness, directly impacting placement success and starting salaries.
Intensive Placement Preparation and Mock Interviews- (Semester 6-8)
Engage in rigorous placement preparation, focusing on aptitude, logical reasoning, and domain-specific technical questions. Participate in mock interviews with faculty, alumni, or professional trainers to refine communication, problem-solving, and behavioral responses for Indian IT companies.
Tools & Resources
Placement cell workshops, InterviewBit, Glassdoor (for company-specific interview experiences), Mock interview platforms
Career Connection
Maximizes chances of clearing multiple rounds of interviews, securing desirable placements with top companies in India, and negotiating better compensation packages.
Develop Soft Skills for Leadership and Teamwork- (Semester 6-8)
Take on leadership roles in student organizations or project teams to cultivate management, delegation, and teamwork skills. Focus on presenting complex data insights clearly and persuasively, preparing for roles that require not just technical expertise but also influencing stakeholders.
Tools & Resources
Student clubs and organizations, Project management tools (Trello, Jira), Mentorship programs
Career Connection
Positions you for faster career progression into leadership and managerial roles within data science teams, essential for long-term growth in Indian corporate structures.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 with Physics and Mathematics as compulsory subjects along with one of Chemistry/Biotechnology/Biology/Technical Vocational subject/Computer Science/Information Technology/Informatics Practices/Agriculture/Engineering Graphics/Business Studies. Obtained at least 45% marks (40% marks in case of candidates belonging to reserved category) in the above subjects taken together. Appeared for SITEEE/JEE (Main)/MHT-CET.
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: Generally 30% for theory courses (Continuous Internal Evaluation - CIE), 100% for lab courses and projects., External: Generally 70% for theory courses (Semester End Examination - SEE).
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BMAT101 | Engineering Mathematics-I | Core | 4 | Differential Calculus, Integral Calculus, Matrices, Vector Algebra, Series and Sequences |
| 23BPHY102 | Engineering Physics | Core | 3 | Quantum Mechanics, Semiconductor Physics, Laser Technology, Fiber Optics, Superconductivity |
| 23BPHYL103 | Engineering Physics Lab | Lab | 1 | Optical Experiments, Electrical Measurements, Semiconductor Device Characteristics, Magnetic Field Measurements |
| 23BELE104 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Single-Phase & Three-Phase Systems, Transformers, Electrical Machines |
| 23BELEL105 | Basic Electrical Engineering Lab | Lab | 1 | Circuit Laws Verification, AC Circuit Analysis, Transformer Characteristics, DC Machine Testing |
| 23BCSE106 | Introduction to Programming | Core | 3 | Programming Fundamentals, Data Types and Operators, Control Flow Statements, Functions, Arrays and Pointers |
| 23BCSEL107 | Introduction to Programming Lab | Lab | 1 | C Language Basics, Conditional Statements, Looping Constructs, Function Implementation, Array Operations |
| 23BMEG108 | Engineering Graphics | Core | 2 | Orthographic Projections, Isometric Projections, Sections of Solids, Development of Surfaces, AutoCAD Basics |
| 23BENV109 | Environmental Studies | Core | 2 | Ecosystems, Biodiversity, Pollution Control, Natural Resources, Environmental Ethics |
| 23BSKIL110 | Skill Development | Non-Credit | 0 | Communication Skills, Logical Reasoning, Problem Solving, Teamwork, Presentation Skills |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BMAT201 | Engineering Mathematics-II | Core | 4 | Differential Equations, Laplace Transforms, Fourier Series, Vector Calculus, Complex Analysis |
| 23BCHE202 | Engineering Chemistry | Core | 3 | Water Technology, Corrosion, Polymers, Electrochemistry, Spectroscopy |
| 23BCHEL203 | Engineering Chemistry Lab | Lab | 1 | Water Hardness Determination, Viscosity Measurements, Titrations, Calorimetry, Corrosion Experiments |
| 23BCIE204 | Basic Civil Engineering | Core | 3 | Building Materials, Surveying, Structural Mechanics, Hydraulics, Transportation Engineering |
| 23BMEE205 | Basic Mechanical Engineering | Core | 3 | Thermodynamics, Fluid Mechanics, Power Plants, Manufacturing Processes, Automobiles |
| 23BWORL206 | Workshop Practices | Lab | 1 | Fitting, Carpentry, Welding, Foundry, Sheet Metal Work |
| 23BCSE207 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Hashing, Sorting and Searching Algorithms |
| 23BCSEL208 | Data Structures Lab | Lab | 1 | Implementation of Lists, Stack and Queue Operations, Tree Traversals, Graph Algorithms, Sorting Algorithm Analysis |
| 23BCOM209 | Communication Skills | Core | 2 | Verbal Communication, Non-Verbal Communication, Presentation Skills, Report Writing, Active Listening |
| 23BUHV210 | Universal Human Values | Core | 2 | Understanding Harmony, Self-Exploration, Relationship Values, Societal Values, Professional Ethics |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BMAT301 | Discrete Mathematics | Core | 4 | Set Theory, Logic and Proof Techniques, Relations and Functions, Graph Theory, Combinatorics |
| 23BEXTC302 | Digital Electronics | Core | 3 | Boolean Algebra, Logic Gates, Combinational Circuits, Sequential Circuits, Memory Devices |
| 23BEXTCL303 | Digital Electronics Lab | Lab | 1 | Logic Gate Implementation, Flip-Flop Design, Counter Circuits, Multiplexers and Demultiplexers, Adder/Subtractor Circuits |
| 23BCSE304 | Object Oriented Programming | Core | 3 | Classes and Objects, Inheritance, Polymorphism, Abstraction, Exception Handling |
| 23BCSEL305 | Object Oriented Programming Lab | Lab | 1 | Class Implementation, Inheritance Hierarchies, Polymorphic Functions, File I/O, Templates |
| 23BCSE306 | Computer Organization & Architecture | Core | 3 | Processor Organization, Memory Hierarchy, Input/Output Organization, Instruction Set Architecture, Pipelining |
| 23BCSE307 | Database Management Systems | Core | 3 | Database Architecture, ER Modeling, Relational Algebra, SQL Queries, Normalization |
| 23BCSEL308 | Database Management Systems Lab | Lab | 1 | SQL DDL/DML, Join Operations, Subqueries, Views and Stored Procedures, Transaction Management |
| 23BBCO309 | Business Communication Skills | Core | 2 | Professional Writing, Email Etiquette, Meeting Management, Negotiation Skills, Cross-Cultural Communication |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BMAT401 | Probability & Statistics | Core | 4 | Probability Theory, Random Variables, Probability Distributions, Hypothesis Testing, Regression Analysis |
| 23BCSE402 | Operating Systems | Core | 3 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks |
| 23BCSEL403 | Operating Systems Lab | Lab | 1 | Shell Scripting, Process Creation, IPC Mechanisms, CPU Scheduling, Memory Allocation |
| 23BCSE404 | Design & Analysis of Algorithms | Core | 3 | Algorithm Complexity, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| 23BCSE405 | Computer Networks | Core | 3 | Network Topologies, OSI Model, TCP/IP Protocol Suite, Routing Algorithms, Network Security Basics |
| 23BCSEL406 | Computer Networks Lab | Lab | 1 | Socket Programming, Packet Sniffing, Network Configuration, Client-Server Communication, Routing Protocols Implementation |
| 23BCSEDS407 | Artificial Intelligence | Core | 3 | Problem Solving Agents, Search Algorithms, Knowledge Representation, Logical Reasoning, Machine Learning Fundamentals |
| 23BCSEDSL408 | Artificial Intelligence Lab | Lab | 1 | Python for AI, Search Algorithms Implementation, Knowledge Representation with Prolog, AI Library Usage (e.g., NumPy, Pandas), Simple AI Agent Development |
| 23BLSK409 | Life Skills | Core | 2 | Emotional Intelligence, Time Management, Stress Management, Decision Making, Leadership Skills |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BCSE501 | Compiler Design | Core | 3 | Lexical Analysis, Syntax Analysis, Semantic Analysis, Intermediate Code Generation, Code Optimization |
| 23BCSEL502 | Compiler Design Lab | Lab | 1 | Lexical Analyzer Implementation, Parser Development, Symbol Table Management, Intermediate Code Generation, Simple Compiler Front-End |
| 23BCSE503 | Theory of Computation | Core | 3 | Finite Automata, Regular Expressions, Context-Free Grammars, Turing Machines, Undecidability |
| 23BCSEDS504 | Machine Learning | Core | 3 | Supervised Learning, Unsupervised Learning, Reinforcement Learning Basics, Model Evaluation, Feature Engineering |
| 23BCSEDSL505 | Machine Learning Lab | Lab | 1 | Scikit-learn, Linear Regression, Classification Algorithms, Clustering Techniques, Data Preprocessing |
| 23BCSEDS506 | Data Warehousing & Data Mining | Core | 3 | Data Warehouse Architecture, OLAP Operations, Association Rule Mining, Classification and Prediction, Clustering Algorithms |
| 23BPELXXX | Professional Elective-I | Elective | 3 | Software Engineering, Network Security, Image Processing, Advanced Database Management Systems |
| 23BOELXXX | Open Elective-I | Elective | 3 | Internet of Things (IoT), Entrepreneurship Development, Foreign Language (German/Japanese/French/Chinese), Indian Constitution and Traditional Knowledge |
| 23BCSEMPI509 | Minor Project-I | Project | 2 | Problem Definition, Literature Survey, Design and Implementation, Testing and Evaluation, Project Report Writing |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BCSEDS601 | Deep Learning | Core | 3 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Learning Frameworks (TensorFlow/PyTorch) |
| 23BCSEDSL602 | Deep Learning Lab | Lab | 1 | CNNs for Image Classification, RNNs for Sequence Data, Transfer Learning, Hyperparameter Tuning, Neural Network Implementation |
| 23BCSEDS603 | Big Data Analytics | Core | 3 | Big Data Technologies (Hadoop, Spark), Distributed File Systems, MapReduce Programming, NoSQL Databases, Stream Processing |
| 23BCSEDSL604 | Big Data Analytics Lab | Lab | 1 | Hadoop Ecosystem Setup, Spark Programming, Hive Queries, HBase Operations, Data Ingestion with Kafka/Flume |
| 23BCSEDS605 | Natural Language Processing | Core | 3 | Text Preprocessing, Language Models, Syntactic Analysis, Semantic Analysis, Machine Translation |
| 23BPELXXX | Professional Elective-II | Elective | 3 | Cloud Computing, Mobile Application Development, Distributed Systems, Computer Graphics |
| 23BOELXXX | Open Elective-II | Elective | 3 | Cyber Security, Augmented Reality & Virtual Reality, Research Methodology, Robotics |
| 23BCSEMPII608 | Minor Project-II | Project | 2 | Advanced Problem Definition, System Design, Implementation with Modern Tools, Performance Evaluation, Technical Report and Presentation |
| 23BCSEINT609 | Summer Internship (Industrial Training) | Project | 2 | Industry Exposure, Practical Skill Application, Professional Networking, Project Documentation, Presentation of Work |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BCSEDS701 | Reinforcement Learning | Core | 3 | Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Deep Reinforcement Learning |
| 23BCSEDSL702 | Reinforcement Learning Lab | Lab | 1 | OpenAI Gym Environments, Q-Learning Implementation, SARSA Algorithm, Policy Gradient Methods, Model-Free Control |
| 23BCSEDS703 | Data Visualization | Core | 3 | Principles of Visualization, Exploratory Data Analysis, Statistical Graphics, Interactive Visualizations, Tools like Tableau/Power BI/D3.js |
| 23BCSEDSL704 | Data Visualization Lab | Lab | 1 | Python Plotting Libraries (Matplotlib, Seaborn), Dashboard Creation, Visualizing Geospatial Data, Interactive Web Visualizations, Storytelling with Data |
| 23BPELXXX | Professional Elective-III | Elective | 3 | Ethical Hacking, Game Development, Web Technologies, Information Retrieval |
| 23BPELXXX | Professional Elective-IV | Elective | 3 | Block Chain Technology, Quantum Computing, Human Computer Interaction, Software Project Management |
| 23BCSEMPJ707 | Major Project-I | Project | 6 | Advanced Problem Identification, Comprehensive Literature Review, System Architecture Design, Module-wise Implementation, Interim Report and Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 23BPELXXX | Professional Elective-V | Elective | 3 | Pattern Recognition, Digital Forensics, High Performance Computing, Bioinformatics |
| 23BOELXXX | Open Elective-III | Elective | 3 | Financial Engineering, Operation Research, Intellectual Property Rights, Professional Ethics and Corporate Social Responsibility |
| 23BCSEMPJ803 | Major Project-II | Project | 10 | Full System Integration, Thorough Testing and Validation, Performance Optimization, Comprehensive Documentation, Final Presentation and Demonstration |
| 23BCSEPR804 | Professional Practice | Core | 1 | Professionalism in Workplace, Ethical Hacking, Teamwork and Collaboration, Career Development, Industry Best Practices |
