Curriculum

The interdisciplinary 4 year Bachelor of Science [BS] programme on Data Science & Engineering [DSE] at IISER Bhopal will enable the students in developing a wide variety of skills in Mathematics, Statistics and Computer Science that are necessary for research and development in the field of Data Analytics, and will be supplemented with interdisciplinary courses for enhancing domain knowledge in Engineering, Natural Sciences and Social Sciences.

In the 1st year, all students admitted into the BS programme in Engineering Sciences at IISER Bhopal will follow a common curriculum. The 2nd year (pre-Major) curriculum of the DSE programme would consist of 9/10 credits of compulsory courses in each semester, which will expose the students to various computational as well as statistical tools and techniques. Taking open electives can fulfill the remaining credit requirements of 2nd year.

At the end of two years, students will have the option to choose Data Science & Engineering for their Major. In the next two years, the focus will be on advanced courses and research projects specialized in Machine Learning, Artificial Intelligence, Statistical Modeling, Big Data Platforms, several other computational techniques and their applications to various areas of science and engineering. For the 3rd and 4th year, students will have 16 credits of project work and 64 credits of courses, out of which at least 32 credits must be from DSE courses. A majority of the DSE courses will have an integrated computational component through which students will be given substantial practical experience of using these tools and techniques for solving real life problems. The curriculum is designed in such a way so as to give sufficient flexibility to the students to choose courses as per their background, core competency and future career interests.

At the end of 7th semester, students can opt to continue in the programme for one more year to get a BS-MS (Dual Degree). In the 8th semester, these students will do 12 credits of DSE courses in place of project work, in order to enhance their skill set. The additional year will be spent only in research work and a 1 credit course related to intellectual property (ECO500). Students would be facilitated to do the final year research work in relevant companies so as to get practical exposure.

Detailed curriculum of the BS programme in Data Science & Engineering
Sem Course No. Course Name Credits Total
I CHM 101 General Chemistry 3 21
MTH  101 Calculus of One Variable 3
PHY 101 Mechanics 3
EES 101 Introduction to Earth Sciences 3
CHE 103 Engineering Design and Drawing 3
HSS 101 English for Communication 2
PHY 103 General Physics Laboratory-I 1
BIO 101*   or ECO101* Biology I: Biomolecules or Principles of Economics - I 3
II CHM 112 Basic Organic Chemistry-I 3 19
MTH  102 Linear Algebra 3
PHY 102 Modern Physics 3
EES 102 Introduction to Environmental Sciences 3
ECS 102 Introduction to Programming 3
CHM 114 Chemistry Laboratory- I 1
BIO 102* or ECO102* Biology II: Fundamentals of Cell Biology or Principles of Economics - II 3
Students must either do both BIO101 and BIO102 in their 1st year, or both ECO101 and ECO102
III ECS201 Discrete Mathematics – I 3 19
ECO201 Econometrics I 4
MTH201 Multivariable Calculus 3
*** *** 3 Open Electives 9
IV ECS202 Data Structures and Algorithms 3 18
ECS204 Signals and Systems 3
MTH202 Probability and Statistics 3
*** *** 3 Open Elective 9
V *** *** DSE Courses and Open Electives 20 20
VI *** *** DSE Courses and Open Electives 20 20
VII *** *** DSE Courses and Open Electives 16 20
DSE401 Project Work 4
VIII *** *** DSE Courses and Open Electives 8 20
DSE402 Project Work 12

A tentative list of DSE Courses
Algorithms Advanced Machine Learning Artificial Intelligence
Combinatorics and Graph Theory Big Data Environment Big Data Platforms
Bioinformatics Biostatistics Cloud Computing
Computational Linguistics Computer Vision Data Analysis and Statistics for Geosciences
Data Science and Machine Learning Data Visualization Deep Learning
Data Security Econometrics II Ethical Data Science
Evolutionary Intelligence Financial Data Analysis Game Theory
Knowledge Representation Pitfalls of Data Science Reinforcement Learning
Statistical Analysis using R programming Statistical Inference and Modelling