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BS-DATASCIENCEANDENGINEERING in General at Indian Institute of Science Education and Research Bhopal
Bhopal, Madhya Pradesh
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About the Specialization
What is General at Indian Institute of Science Education and Research Bhopal Bhopal?
This BS Data Science and Engineering program at IISER Bhopal focuses on integrating foundational engineering principles with advanced data science methodologies. It emphasizes a robust understanding of mathematics, statistics, and computer science crucial for analyzing and interpreting complex datasets. The curriculum is designed to equip students with interdisciplinary skills vital for emerging roles in India''''s rapidly growing data-driven industries. It uniquely blends theoretical rigor with practical application for real-world problem-solving.
Who Should Apply?
This program is ideal for high school graduates with a strong aptitude in Mathematics and Science, typically those who have excelled in competitive exams like JEE Advanced. It caters to aspiring data scientists, machine learning engineers, and data analysts eager to enter the dynamic Indian tech landscape. It is also suitable for individuals seeking a rigorous academic foundation before pursuing higher studies or research in data science. Prerequisite backgrounds typically include strong PCM in 10+2.
Why Choose This Course?
Graduates of this program can expect promising career paths in data analytics, machine learning, AI development, and business intelligence across various Indian sectors like IT, finance, healthcare, and e-commerce. Entry-level salaries in India typically range from INR 6-12 LPA, with significant growth potential up to INR 20-40 LPA or more for experienced professionals. The program aligns with industry demands for skilled professionals capable of driving data-driven innovation in leading Indian and global companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Focus rigorously on mastering core programming concepts (Python, C++) and data structures taught in early semesters. Practice coding daily using online platforms to build a strong problem-solving base.
Tools & Resources
HackerRank, LeetCode, CodeChef, GeeksforGeeks, NPTEL courses on Data Structures
Career Connection
Strong programming skills are the bedrock for any data science or engineering role, essential for cracking technical interviews and building efficient solutions in real-world scenarios.
Solidify Math and Statistical Foundations- (Semester 1-2)
Pay close attention to Calculus, Linear Algebra, Probability, and Statistics. These subjects are fundamental for understanding machine learning algorithms and data models. Attend tutorials and solve problems regularly.
Tools & Resources
Khan Academy, NPTEL courses, Essence of Linear Algebra (3Blue1Brown), textbooks by Sheldon Ross, Gilbert Strang
Career Connection
A deep understanding of these subjects is critical for advanced data science roles, enabling algorithm optimization, model interpretation, and pursuing research in the field.
Engage in Interdisciplinary Learning- (Semester 1-2)
Leverage the IISER environment by actively participating in discussions across science disciplines. Understand how data science can be applied in various scientific fields, enhancing problem-solving perspective beyond pure tech.
Tools & Resources
IISER Bhopal departmental seminars, guest lectures, interdisciplinary student clubs, science popularization events
Career Connection
Develops a holistic approach to data problems, crucial for roles in diverse industries like bioinformatics, material science, or environmental data analysis, which are prevalent in India.
Intermediate Stage
Build a Strong Project Portfolio- (Semester 3-5)
Actively seek out small projects or mini-hackathons related to data science and engineering topics. Apply learned concepts in database management, algorithms, and early machine learning to demonstrate practical skills.
Tools & Resources
Kaggle, GitHub, university project labs, mentor guidance from senior students or faculty
Career Connection
A strong project portfolio demonstrates practical skills to potential employers, especially for internships and entry-level positions in Indian tech companies, showing initiative and capability.
Seek Early Industry Exposure- (Semester 4-5)
Attend workshops, industry talks, and career fairs. Look for summer internships, even short ones, with startups or local tech companies in Bhopal or nearby cities like Indore or Pune to gain real-world insights.
Tools & Resources
LinkedIn, university career services, company websites, industry events and conferences
Career Connection
Helps in understanding real-world industry demands, networking with professionals, and aligning academic learning with practical applications, significantly improving placement chances.
Specialize in Key Data Science Tools- (Semester 4-5)
Beyond course requirements, dedicate time to master specific tools like Python libraries (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) and SQL for robust data manipulation and analysis.
Tools & Resources
Coursera, edX, DataCamp, Stack Overflow, official software documentation
Career Connection
Proficiency in industry-standard tools makes graduates highly employable for data analyst, machine learning engineer, or data engineer roles in the competitive Indian market.
Advanced Stage
Undertake Advanced Research Projects- (Semester 7-8)
Engage in a significant final year project (Project I & II) or a research internship. Focus on a niche area like Deep Learning, NLP, or Big Data, and aim for publishable quality work to showcase expertise.
Tools & Resources
Faculty mentors, research labs at IISER Bhopal, high-performance computing resources, academic databases (Scopus, Web of Science)
Career Connection
High-quality research projects differentiate candidates for top-tier roles, R&D positions, and provide a strong foundation for higher studies (MS/PhD) both in India and abroad.
Intensive Placement and Interview Preparation- (Semester 7-8)
Dedicate focused time to prepare for company-specific technical interviews, coding rounds, and aptitude tests. Practice mock interviews, improve communication skills, and refine resume/CV with career services.
Tools & Resources
University placement cell, online interview platforms (Pramp), industry interview guides, alumni network for mentorship
Career Connection
Maximizes chances of securing lucrative job offers from leading Indian and multinational companies during campus placements, ensuring a smooth transition into the professional world.
Develop Professional Networking & Mentorship- (Semester 6-8)
Actively build a professional network through alumni connect programs, industry events, and LinkedIn. Seek mentorship from experienced professionals to guide career decisions and explore advanced opportunities in the field.
Tools & Resources
LinkedIn, IISER Bhopal Alumni Association, industry conferences, professional meetups and webinars
Career Connection
Opens doors to hidden job opportunities, provides insights into industry trends, and facilitates long-term career growth in India''''s competitive data science landscape by leveraging connections.
Program Structure and Curriculum
Eligibility:
- Candidates must have passed 10+2 with Physics, Chemistry, Mathematics (PCM) and obtained a minimum of 60% marks or equivalent grade in aggregate. Admission is based on JEE Advanced score.
Duration: 8 semesters / 4 years
Credits: 170 (as stated on official documents, however, sum of listed semester-wise credits is 156) Credits
Assessment: Internal: Evaluation typically based on continuous assessment (approx. 30%) and mid-semester exam (approx. 30%), External: End-semester examination (approx. 40%)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS 101 | Introduction to Programming | Core | 4 | Fundamentals of programming, Data types and variables, Control structures, Functions and modules, Object-oriented programming basics |
| DS 102 | Engineering Drawing | Core | 1 | Introduction to engineering drawing, Orthographic projections, Sectional and isometric views, Dimensioning and scaling, Computer-aided drafting basics |
| MA 101 | Calculus I | Core | 4 | Real numbers and sequences, Limits and continuity, Differentiation of functions, Applications of derivatives, Riemann integral |
| PH 101 | Physics I | Core | 4 | Mechanics of particles, Rotational dynamics, Gravitation and celestial mechanics, Oscillations and waves, Fluid mechanics |
| PH 102 | Physics Lab I | Lab | 1 | Basic experimental techniques, Error analysis and data fitting, Experiments in mechanics, Experiments in oscillations and waves, Measurement instruments |
| HS 101 | English Communication | Core | 3 | Grammar and vocabulary, Reading comprehension, Academic writing skills, Oral presentation skills, Professional communication |
| EV 101 | Environmental Studies | Core | 2 | Natural resources and management, Ecosystems and biodiversity, Environmental pollution, Climate change and global issues, Sustainable development |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS 103 | Data Structures | Core | 4 | Arrays, linked lists, stacks, queues, Trees and binary search trees, Graphs and graph traversal, Sorting and searching algorithms, Hashing techniques |
| DS 104 | Digital Logic & Design | Core | 4 | Boolean algebra and logic gates, Combinational circuits, Sequential circuits, Registers and counters, Memory elements |
| MA 102 | Calculus II | Core | 4 | Multivariable calculus, Partial derivatives and gradients, Multiple integrals, Vector calculus, Green''''s, Stoke''''s, Divergence theorems |
| CH 101 | Chemistry I | Core | 4 | Atomic structure and quantum mechanics, Chemical bonding theories, Thermodynamics principles, Electrochemistry fundamentals, Chemical kinetics |
| CH 102 | Chemistry Lab I | Lab | 1 | Volumetric analysis, Gravimetric analysis, Inorganic synthesis, Chemical instrumentation basics, Quantitative analysis |
| HS 102 | Introduction to Philosophy | Core | 3 | Epistemology and theory of knowledge, Metaphysics and reality, Ethics and moral philosophy, Logic and critical thinking, Political and social philosophy |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS 201 | Object Oriented Programming | Core | 4 | Classes, objects, and encapsulation, Inheritance and polymorphism, Abstraction and interfaces, Exception handling, Design patterns |
| DS 202 | Database Management Systems | Core | 4 | Relational model and SQL, Database design and normalization, Transaction management, Concurrency control, Database security |
| MA 201 | Linear Algebra | Core | 4 | Vector spaces and subspaces, Linear transformations, Matrices and determinants, Eigenvalues and eigenvectors, Inner product spaces |
| PH 201 | Physics II | Core | 4 | Electromagnetism and Maxwell''''s equations, Electric and magnetic fields, Electromagnetic waves, Introduction to optics, Elements of quantum mechanics |
| PH 202 | Physics Lab II | Lab | 1 | Experiments in electricity and magnetism, Optical experiments, Semiconductor device characteristics, Modern physics experiments, Circuit analysis |
| HS 2XX | Humanities/Social Science Elective I | Elective | 3 | Arts and culture studies, Introduction to psychology, Sociology of modern society, Elements of history, Language and communication |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS 203 | Design & Analysis of Algorithms | Core | 4 | Asymptotic analysis and complexity, Divide and conquer algorithms, Dynamic programming, Greedy algorithms, Graph algorithms (BFS, DFS, shortest path) |
| DS 204 | Computer Organization and Architecture | Core | 4 | CPU organization and instruction sets, Data path and control unit, Memory hierarchy and cache, Input/Output organization, Pipelining and parallel processing |
| MA 202 | Differential Equations | Core | 4 | First order ordinary differential equations, Second order linear ODEs, Series solutions of ODEs, Laplace transforms, Partial differential equations basics |
| CH 201 | Chemistry II | Core | 4 | Organic chemistry fundamentals, Reaction mechanisms, Stereochemistry, Spectroscopy (NMR, IR, Mass), Polymer chemistry |
| CH 202 | Chemistry Lab II | Lab | 1 | Organic synthesis experiments, Spectroscopic characterization, Chromatographic techniques, Physical chemistry measurements, Computational chemistry applications |
| HS 2XX | Humanities/Social Science Elective II | Elective | 3 | Introduction to economics, Political science fundamentals, Literary studies, Environmental sociology, Critical thinking and ethics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS 301 | Operating Systems | Core | 4 | Process management and CPU scheduling, Memory management and virtual memory, File systems and I/O systems, Deadlocks, Operating system security |
| DS 302 | Introduction to Machine Learning | Core | 4 | Supervised and unsupervised learning, Regression and classification algorithms, Clustering techniques, Model evaluation and selection, Introduction to neural networks |
| DS 303 | Probability and Statistics | Core | 4 | Probability theory and random variables, Probability distributions (discrete and continuous), Sampling distributions, Hypothesis testing and confidence intervals, Regression and correlation analysis |
| DS 304 | Introduction to Data Science | Core | 4 | Data science lifecycle, Data acquisition and cleaning, Exploratory data analysis, Data visualization techniques, Big Data concepts and tools |
| DE 3XX | Department Elective I | Elective | 4 | Advanced programming paradigms, Distributed systems architecture, Optimization techniques, Numerical methods in data science, Data visualization principles |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS 305 | Computer Networks | Core | 4 | Network models (OSI, TCP/IP), Physical and data link layers, Network layer and routing protocols, Transport layer and congestion control, Application layer protocols and security |
| DS 306 | Artificial Intelligence | Core | 4 | Problem-solving and search algorithms, Knowledge representation and reasoning, Expert systems, Introduction to natural language processing, Machine learning overview |
| DS 307 | Software Engineering | Core | 4 | Software development life cycle, Requirements engineering, Software design principles and patterns, Software testing and quality assurance, Project management and agile methodologies |
| DE 3XX | Department Elective II | Elective | 4 | Cloud computing fundamentals, Cyber security principles, Deep learning architectures, Natural language processing advanced topics, Computer vision applications |
| OE 3XX | Open Elective I | Elective | 3 | Interdisciplinary scientific concepts, Societal impact of technology, Entrepreneurship and innovation, Introduction to foreign languages, Advanced communication skills |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS 401 | Data Mining and Warehousing | Core | 4 | Data preprocessing and cleaning, Association rule mining, Classification and prediction, Clustering algorithms, Data warehousing and OLAP |
| DE 4XX | Department Elective III | Elective | 4 | Time series analysis, Reinforcement learning, Blockchain technology, Advanced database systems, Parallel and distributed computing |
| DE 4XX | Department Elective IV | Elective | 4 | Robotics and autonomous systems, Internet of Things (IoT), Game theory and AI, Scientific computing methods, Human-computer interaction |
| OE 4XX | Open Elective II | Elective | 3 | Entrepreneurial strategies, Public policy and governance, Introduction to arts and aesthetics, Advanced statistical methods, Intercultural communication |
| DS 499 | Project I | Project | 3 | Problem identification and definition, Literature survey, Methodology development, Initial implementation and results, Technical report writing |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DE 4XX | Department Elective V | Elective | 4 | Big data analytics and tools, Quantum computing basics, Bioinformatics and computational biology, Financial technology (FinTech) applications, Data ethics and privacy |
| DE 4XX | Department Elective VI | Elective | 4 | Advanced machine learning models, Distributed databases and NoSQL, Wireless sensor networks, Computer graphics and visualization, Virtual and augmented reality |
| DS 498 | Project II | Project | 6 | Advanced research and development, System design and implementation, Large-scale data analysis, Comprehensive thesis writing, Project defense and presentation |
| DS 497 | Internship/Industrial Training | Internship | 6 | Real-world industry experience, Application of academic knowledge, Professional skill development, Teamwork and communication in industry, Internship report and presentation |
