| I Semester | II Semester | ||||
| IC: MTH 101A Mathematics I |
3-1-0-0 | 11 | IC: MTH 102A Mathematics II |
3-1-0-0 | 11 |
| IC: PHY 103A Physics II |
3-1-0-0 | 11 | IC: PHY 102A Physics I |
3-1-0-0 | 11 |
| IC: CHM 101A Chemistry Laboratory |
0-0-3-0 | 03 | IC: PHY 101A Physics Laboratory |
0-0-3-0 | 03 |
| IC: ESC 101A Fundamental of Computing |
3-1-3-0 | 14 | IC: LIF 101A Introduction to Biology |
2-0-0-0 | 06 |
| HSS: ENG 112A/HSS-1 (Level-1) | 3-1-0-0 | 11 | IC: CHM 102A General Chemistry |
2-1-0-0 | 08 |
| IC:PE 101A Morning Exercise |
0-0-3-0 | 03 | IC: PE 102A Evening Exercise |
0-0-3-0 | 03 |
| IC: TA 101A Engineering Graphics |
2-0-3-0 | 09 | |||
| Total | 53 | Total | 51 | ||
| III Semester | IV Semester | ||||
| DC: MTH 301A Analysis I |
3-1-0-0 | 11 | DC: MTH 211A Theory of Statistics |
3-1-0-0 | 11 |
| ESO/SO-1: MSO 205A Introduction to Probability Theory |
3-1-0-0 | 11 | DC: MTH 210A Statistical Computing |
3-0-1-0 | 10 |
| ESO/SO-2: MSO 202A Introduction to Complex Analysis (Modular I) |
3-1-0-0 | 06 | DC: MTH 212A Elementary Stochastic Processes I (Modular I) |
3-1-0-0 | 06 |
| DC: MTH 207A Matrix Theory and Linear Estimation (Modular II) |
3-1-0-0 | 06 | DC: MTH 209A Data Science Lab 2 |
1-0-2-0 | 05 |
| DC: MTH 208A Data Science Lab 1 |
0-0-3-2 | 05 | HSS: HSS-2 (Level-1) | 3-1-0-0 | 11 |
| IC: ESC 201A Introduction to Electronics |
3-1-3-0 | 14 | IC: TA 202A Manufacturing Process II |
1-0-3-0 | 06 |
| IC: TA 201A Manufacturing Process I |
1-0-3-0 | 06 | ESO/SO-3: | 3-0-0-0 | 09 |
| Total | 59 | Total | 58 | ||
| V Semester | VI Semester | ||||
| DC: MTH 442A Time Series Analysis |
3-0-1-0 | 10 | DC: MTH 422A Bayesian Analysis |
3-0-1-0 | 10 |
| DC: MTH 441A Linear Regression and ANOVA |
3-0-1-0 | 10 | DC: MTH 314A Multivariate Analysis |
3-0-1-0 | 10 |
| DC: MTH 399A Communication Skills |
0-0-2-0 | 02 | DC: MTH 312A Data Science Lab 3 |
1-0-2-0 | 05 |
| ESO/SO-4: ESO 207A Data Structures and Algorithms |
3-0-3-0 | 12 | ESO/SO-5: | 3-0-0-0 | 09 |
| IC: Com 200 Communication Skills: Composition |
1-1-0-0 | 05 | HSS: HSS-4 (Level-2) | 3-0-0-0 | 09 |
| HSS: HSS-3 (Level-2) | 3-0-0-0 | 09 | OE-2/UGP - 2 | 0-0-9-0 | 09 |
| OE-1: | 3-0-0-0 | 09 | |||
| UGP -1 (Extra Credit) | 0-0-4-0 | 04 | |||
| Total | 57/61 | Total | 52 | ||
| VII Semester | VIII Semester | ||||
| DC: MTH 443A Statistical and AI Techniques in Data Mining |
3-0-1-0 | 10 | DE – 3 | 09 | |
| DE - 1 | 09 | DE – 4 | 09 | ||
| DE - 2 | 09 | OE – 6 | 09 | ||
| OE – 3/UGP-3 | 09 | OE – 7 | 09 | ||
| OE – 4 | 09 | HSS: HSS-5 (Level-2) | 09 | ||
| OE - 5 | 09 | UGP4 (Extra Credit) | 09 | ||
| Total | 55 | Total | 45/54 | ||
Minimum credit requirement for graduation
| Bracket | Credits | Percentage |
| Institute Core (IC) | 124 | 28.8 |
| Department Compulsory (DC) | 111 | 25.8 |
| Department Elective (DE) | 36 | 8.4 |
| Open elective / UGP (OE / UGP) | 63 | 14.7 |
| ESO/SO | 47 | 10.9 |
| HSS | 49 | 11.4 |
| Total | 430 |
**Up to 45 credits of internships in lieu of open electives can be taken. This can be done through the courses MTH321 Internship I, MTH322 Internship II, MTH323 Internship III, MTH324 Internship IV, MTH325 Internship V, of 9 credits each. One would have an option to earn 45 credits of OE through internship courses by spending a full semester in an industry or may do online internships (under one or more OEs) from industry, spread across different semesters. The process for enrolling in the internship courses is as follows: the student identifies a viable internship opportunity in the general realm of statistics and data science and identifies a supervisor in the MTH department. The student, in consultation with the host industry/organization, submits a proposal to the department undergraduate committee (DUGC) with the approval of the industry liaison and the departmental supervisor, upon which it will be evaluated for approval and requisite number of credits (in multiples of 9) will be decided. The grading scheme for the internship courses will be S/X.