Mehta Family School of
Data Science & Artificial Intelligence

The BTech program in Data Science and Engineering aims to impart the principles of analysis and design of building large-scale data driven decision-making systems that involves humans, machines, and the environment at large. The recommendations from the National Academy of Sciences report on Undergraduate curriculum in Data Science, the ACM Data Science task force’s report on computing competencies for undergraduate data science curricula, the Data Science Education in India report from the Vaibhav Summit were considered while framing the curriculum. The curriculum is designed to impart both the theoretical and practical aspects of the discipline with the aim to build a strong foundation in

• Mathematics: A solid mathematical foundation supports critical areas such as calculus, linear algebra, and discrete mathematics, enabling students to tackle complex algorithms and data-driven models effectively.
• Probability and Statistics: Probability and statistics form the backbone of data science, enabling students to understand uncertainty, sampling, and randomness.
• Computational Thinking: Skills to design a structured approach to problem-solving that combines data abstraction, efficient use of data structures, algorithmic design, and foundational programming concepts to create scalable, effective solutions.
• Systems Engineering: Learn the the core of competencies for computing infrastructures for data science - processors, platforms, memory systems, operating systems.
• Data Management and Visualization: Effective data management skills ensure students can organize, store, and retrieve data efficiently, a critical aspect of handling large datasets. This includes database management, data cleaning, and data governance, supporting reliable, data-driven decisions.
• Data Modeling and Assessment: Modeling skills enable students to represent real-world systems and predict outcomes through data science, machine leaarning, and artificial intelligence, algorithms.
• Ethics: Ethics in data science focuses on the responsible handling and use of data, respecting privacy, transparency, and fairness. Students learn to navigate ethical challenges, fostering a sense of accountability and ensuring that their solutions positively impact society.

The BTech Data Science and Engineering (DSE) curriculum adheres to the revised institute undergraduate program guidelines, designed to deliver a broad-based education that is both student-friendly and flexible. This curriculum balances a forward-thinking approach with industry relevance, ensuring that students are well-prepared for future career landscapes. While detailed information on each course category is available in the institute's undergraduate regulations, the overall structure of the curriculum is outlined in the following table.

Category	Credits
Institute Core (IC)	42
Program Major Core (PMC)	43
Program Major Elective (PME)	20
Humanities and Social Sciences Elective (HSE)	9
Sciences and Mathematics Elective (SME)	6
Open Elective (OE)	15
Project	9
Total Credits	144

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The competencies for the BTech Data Science and Engineering (DSE) program are developed through a range of core and elective courses. The core courses are structured into three progressive levels, each categorized based on prerequisite knowledge and skills required, ensuring a coherent learning pathway. This tiered approach allows students to build foundational competencies before advancing to more complex, specialized topics in the field. These tiers are

• [Level 1] Introduction to Data Science and Engineering, Discrete Mathematics
• [Level 2] Introduction to Optimization, Data Structures and Algorithms for Data Science, Computer Systems for Data Science, Introduction to Artificial Intelligence
• [Level 3] Database Systems, Data Analytics, Introduction to Machine Learning, Introduction to Deep Learning, AI Ethics

The program major elective courses in the curriculum include a wide range of specialized topics, allowing students to pursue in-depth knowledge in areas that align with their interests and career aspirations. These electives encompass courses such as Signal Processing for Data Science, AI of Things, Applied Accelerated AI, Big Data Lab, Information Theory and Statistics, Reinforcement Learning, Probabilistic Machine Learning, Computer Vision, AI for Cybersecurity, Econometrics, Natural Language Processing, Information Retrieval, Foundations of Data Science and Machine Learning, Bioinformatics, and Responsible AI. This extensive selection empowers students to gain advanced competencies in cutting-edge domains within data science.

The following is a suggested semester-wise course plan to guide the students. The detailed syllabi of the courses offered by school can be found here

Sl No.	Semester	Course Code	Course Name	Category	Credits
1	I	PH1030	Physics	Institute Core	2-1-0-3
2		MA1011A	Linear Algebra and Series	Institute Core	3-1-0-4
3		ES1010	Ecology and Environment	Institute Core	2-0-0-2
4		ME1130	Engineering Drawing	Institute Core	1-0-3-3
5		ID1050A	Engineering Design	Institute Core	1-0-3-3
6		ME1150	Mechanical Workshop	Institute Core	0-0-3-2
7		CY1130	Chemistry Lab	Institute Core	0-0-3-2
			Total Semester Credits		19
Sl No.	Semester	Course Code	Course Name	Category	Credits
1	II	MA1021	Multivariable Calculus	Institute Core	3-1-0-4
2		CY1040	Basic Chemistry for Engineers	Institute Core	2-1-0-3
3		HS1010	Technology and Society	Institute Core	2-0-0-2
4		CE1020	Engineering Mechanics	Institute Core	3-1-0-4
5		ID1110	Introduction to Programming	Institute Core	2-0-3-4
6		EE1110	Electrical Workshop	Institute Core	0-0-3-2
7		PH1130	Physics Lab	Institute Core	0-0-3-2
			Total Semester Credits		21
Sl No.	Semester	Course Code	Course Name	Category	Credits
1	III	DS1010	Introduction to Data Science and Engineering	Program Major Core	1-0-0-1
2		CS2020A	Discrete Mathematics	Program Major Core	3-1-0-4
3		DS2010	Introduction to Optimization	Program Major Core	3-0-0-3
4		DS2030	Data Structures and Algorithms for Data Science	Program Major Core	3-0-3-5
5		BT2010	Life Sciences	Institute Core	2-0-2-2
6			SME 1	Science and Mathematics Elective	3
7			HSE 1	Humanities and Social Sciences Elective	0-0-3-2
			Total Semester Credits		21
Sl No.	Semester	Course Code	Course Name	Category	Credits
1	IV	DS2020	Introduction to Artificial Intelligence	Program Major Core	3-0-2-4
2		DS2040	Computer Systems for Data Science	Program Major Core	3-0-2-5
3		DS3020	Database Systems	Program Major Core	3-0-2-5
4			SME 2 (Probability and Statistics)	Science and Mathematics Elective	3-0-0-3
5			OE 1	Open Elective	3
			Total Semester Credits		20
Sl No.	Semester	Course Code	Course Name	Category	Credits
1	V	DS3010	Introduction to Machine Learning	Program Major Core	3-0-3-5
2		DS3030	Data Analytics	Program Major Core	2-0-3-4
3			PME 1	Program Major Elective	3
4			HSE 2	Humanities and Social Sciences Elective	3
5			OE 2	Open Elective	3
			Total Semester Credits		18
Sl No.	Semester	Course Code	Course Name	Category	Credits
1	VI	DS3010	Introduction to Deep Learning	Program Major Core	3-0-3-5
2		DS3030	Artificial Intelligence Ethics	Program Major Core	2-0-0-2
3			PME 2	Program Major Elective	3
4			PME 3	Program Major Elective	3
5			HSE 3	Humanities and Social Sciences Elective	3
6			Honors PME 1	Program Major Elective	3
			Total Semester/Honors Credits		15/18
Sl No.	Semester	Course Code	Course Name	Category	Credits
1	VII		PME 4	Program Major Elective	3
2			PME 5	Program Major Elective	3
3			OE 3	Open Elective	3
4			OE 4	Open Elective	3
5			Project I	Project	3
6			Honors PME 2	Program Major Elective	3
			Total Semester/Honors Credits		15/18
Sl No.	Semester	Course Code	Course Name	Category	Credits
1	VIII		PME 6	Program Major Elective	3
2			PME 7	Program Major Elective	3
3			OE 5	Open Elective	3
4			Project II	Project	3
5			Honors PME 3	Program Major Elective	3
6			Honors PME 4	Program Major Elective	3
			Total Semester/Honors Credits		15/21
			Total Credits		144