Admissions 2019
Academics
The 5-year Integrated Mathematics Program with Minor in Data Sciences represents a new direction towards an integrated approach to Mathematics that combines Pure and Applied Mathematics with Data Analytics and Computation.
In addition to learning the basic and advanced courses in Pure Mathematics up to Masters level, Students also learn a combination of Modelling, Analytics, and Computing. This unique combination significantly enhances the job prospects, as well as qualifying exams like CSIR-NET, GATE etc, for the students. Our innovative program enables the students to pursue their career in academics as well in research labs, leading industries, and government sector.
Interested students will be able to pursue summer internship opportunities. Graduates will be well equipped to pursue their dreams vis-à-vis higher education and research or employment. Employment opportunities will be in the Government Sector like National Research Labs, Space, and Defence Research Organisations, as well as in the booming Data Science market in both private and public sectors.
Curriculum
Semester 1
| Sl No. | Course Title | Contact Hours/Week | No. of Credits | Remarks | ||
| Lecture | Tutorial | Practical | ||||
| 1 | Single Variable Calculus | 3 | 1 | 0 | 4 | |
| 2 | Introduction to Mechanics | 3 | 1 | 0 | 4 | |
| 3 | Vectors and Geometry | 3 | 1 | 0 | 4 | |
| 4 | Communicative English | 2 | 0 | 2 | 3 | |
| 5 | Introduction to Programming 1 | 3 | 0 | 2 | 4 | |
| 6 | Language 1 | 1 | 0 | 2 | 2 | |
| 7 | Cultural Education 1 | 2 | 0 | 0 | 2 | |
| 8 | Mathematics Lab 1 | 0 | 0 | 2 | 1 | |
| Total | 24 | |||||
Semester 2
| Sl No. | Course Title | Contact Hours/Week | No. of Credits | Remarks | ||
| Lecture | Tutorial | Practical | ||||
| 1 | Multivariable Calculus | 3 | 1 | 0 | 4 | |
| 2 | Ordinary Differential Equations | 3 | 1 | 0 | 4 | |
| 3 | Discrete Mathematics | 3 | 1 | 0 | 4 | |
| 4 | Introduction to Programming 2 | 3 | 0 | 2 | 4 | |
| 5 | Professional Communication | 1 | 0 | 2 | 2 | |
| 6 | Cultural Education 2 | 2 | 0 | 0 | 2 | |
| 7 | Mathematics Lab 2 | 0 | 0 | 2 | 1 | |
| 8 | Language 2 | 1 | 0 | 2 | 2 | |
| Total | 23 | |||||
Semester 3
| Sl No. | Course Title | Contact Hours/Week | No. of Credits | Remarks | ||
| Lecture | Tutorial | Practical | ||||
| 1 | Linear Algebra 1 | 3 | 1 | 0 | 4 | |
| 2 | Transforms & PDE | 3 | 1 | 0 | 4 | |
| 3 | Introduction to Probability and Statistics | 3 | 1 | 0 | 4 | |
| 4 | Data Structures & Algorithms | 3 | 1 | 0 | 4 | |
| 5 | Environmental Science | 3 | 0 | 0 | 3 | |
| 6 | Computing using R (Lab 1) | 0 | 0 | 2 | 1 | |
| 7 | Mathematics Lab 3 | 0 | 0 | 2 | 1 | |
| 8 | Amrita Value Programme 1 | 1 | 0 | 0 | 1 | |
| Total | 22 | |||||
Semester 4
| Sl No. | Course Title | Contact Hours/Week | No. of Credits | Remarks | ||
| Lecture | Tutorial | Practical | ||||
| 1 | Numerical Methods | 3 | 1 | 0 | 4 | |
| 2 | Real Analysis 1 | 3 | 1 | 0 | 4 | |
| 3 | Abstract Algebra 1 | 3 | 1 | 0 | 4 | |
| 4 | Statistical Inference | 3 | 1 | 0 | 4 | |
| 5 | Python Lab for Numerical Methods | 0 | 0 | 2 | 1 | |
| 6 | Computing Using R (Lab 2) | 0 | 0 | 2 | 1 | |
| 7 | Amrita Value Programme 2 | 1 | 0 | 0 | 1 | |
| 8 | Life Skills 1 | 1 | 0 | 2 | 2 | |
| Total | 21 | |||||
Semester 5
| Sl No. | Course Title | Contact Hours/Week | No. of Credits | Remarks | ||
| Lecture | Tutorial | Practical | ||||
| 1 | Real Analysis 2 | 3 | 1 | 0 | 4 | |
| 2 | Complex Analysis 1 | 3 | 1 | 0 | 4 | |
| 3 | Open Elective / Live in Lab | 3 | 0 | 0 | 3 | |
| 4 | Operations Research | 3 | 1 | 0 | 4 | |
| 5 | Machine Learning 1 | 3 | 1 | 0 | 4 | |
| 6 | OR Lab | 0 | 0 | 2 | 1 | |
| 7 | Machine Learning Lab 1 | 0 | 0 | 2 | 1 | |
| 8 | Life Skills 2 | 1 | 0 | 2 | 2 | |
| Total | 23 | |||||
Semester 6
| Sl No. | Course Title | Contact Hours/Week | No. of Credits | Remarks | ||
| Lecture | Tutorial | Practical | ||||
| 1 | Linear Algebra 2 | 3 | 1 | 0 | 4 | |
| 2 | ODE & Calculus of Variations | 3 | 1 | 0 | 4 | |
| 3 | Optimization Techniques | 3 | 1 | 0 | 4 | |
| 4 | Complex Analysis 2 | 3 | 1 | 0 | 4 | |
| 5 | Machine Learning 2 | 3 | 1 | 0 | 4 | |
| 6 | Machine Learning Lab 2 | 0 | 0 | 2 | 1 | |
| 7 | Optimization Lab | 0 | 0 | 2 | 1 | |
| Total | 22 | |||||
Semester 7
| Sl No. | Course Title | Contact Hours/Week | No. of Credits | Remarks | ||
| Lecture | Tutorial | Practical | ||||
| 1 | Abstract Algebra 2 | 3 | 1 | 0 | 4 | |
| 2 | Real Analysis 3 | 3 | 1 | 0 | 4 | |
| 3 | Topology | 3 | 1 | 0 | 4 | |
| 4 | Multivariate Statistics | 3 | 1 | 0 | 4 | |
| 5 | Data Visualization | 3 | 0 | 2 | 4 | |
| 7 | Computing Using R (Lab 3) | 0 | 0 | 2 | 1 | |
| 8 | Seminar | 2 | ||||
| Total | 23 | |||||
Semester 8
| Sl No. | Course Title | Contact Hours/Week | No. of Credits | Remarks | ||
| Lecture | Tutorial | Practical | ||||
| 1 | Measure and Integration | 3 | 1 | 0 | 4 | |
| 2 | Stochastic Processes | 3 | 1 | 0 | 4 | |
| 3 | Functional Analysis | 3 | 1 | 0 | 4 | |
| 4 | PDE & Integral Equations | 3 | 1 | 0 | 4 | |
| 5 | Database Management for Big Data | 3 | 1 | 0 | 4 | |
| 6 | Computing Using R (Lab 4) | 0 | 0 | 2 | 1 | |
| Total | 21 | |||||
Semester 9
| Sl No. | Course Title | Contact Hours/Week | No. of Credits | Remarks | ||
| Lecture | Tutorial | Practical | ||||
| 1 | Elective 1 | 3 | 0 | 0 | 3 | |
| 2 | Elective 2 | 3 | 0 | 0 | 3 | |
| 3 | Mini Project | 6 | ||||
| Total | 12 | |||||
Semester 10
| Sl No. | Course Title | Contact Hours/Week | No. of Credits | Remarks | ||
| Lecture | Tutorial | Practical | ||||
| 1 | Project | 15 | ||||
| Total | 15 | |||||
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