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Details
B.Sc.(Hons.) Mathematics is a full-time 3-year undergraduate program.This Data Science program is designed for students who want to pursue Mathematics for a deeper understanding and knowledge of the subject. The students through this B.Sc.(Hons.) Mathematics in Data Science program will get an opportunity to have in-depth knowledge of Data Science and Computational Mathematics. Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data whereas Computational Mathematics involves mathematical research in areas of science and engineering where computing plays an essential role.
The study of data analysis and computational mathematics courses would help the students to have prospects in manufacturing and service sectors under the ‘Make in India’ program which include automobiles, automobile components, aviation, biotechnology, chemicals, construction, defence manufacturing electrical machinery, electronic systems, food processing, IT & BPM, leather, media and entertainment, mining, oil and gas, pharmaceuticals, ports and shipping, railways, renewable energy, roads and highways, space, textile and garments, thermal power, tourism, hospitality and wellness.
Durations
3 Years (6 semesters)
MOUS
DESIGN YOUR OWN DEGREE
UPES’ curriculum framework is holistic in its overall structure and yet focuses on the individual need of the student to discover, experience, explore and challenge. Along with the core subject, students have the option to choose from subject-focused specialisations. They are also allowed to choose minor/exploratory subject from other schools at UPES that are: School of Engineering, School of Computer Science, School of Law, School of Business, School of Health Sciences, School of Design, School of Modern Media, and School of Liberal Studies.
Further, based on the multifaceted needs of the global workplace and evolving lifestyles, the curriculum offers Signature and Life-Skills courses through School for Life. To round off this learning experience, students are required to do mandatory internships in the social sector, government/public sector, and industry. The combinations available for students to pick and choose from are endless, ensuring both depth and width of knowledge.
Details
PEO-1: To train students to appreciate mathematical and computational skills to clear various competitive examinations including civil services, and JAM.
PEO-2: To provide knowledge of highest standards in Mathematics so that the students are able to utilize significant opportunities in various service sectors such as banks, government offices and higher studies in leading universities and institutions.
PEO-3: To engage students in life-long learning and guide them to continuously explore, learn and adapt new skills that will help them in their higher education and career prospects.
PEO-4: To encourage students to be committed towards ethical practices and development of the society
PSO 1: Understand mathematical concepts in the field of algebra, analysis, computational techniques, optimisation, differential equations, etc.
PSO 2: Able to acquire critical thinking and effective reasoning skills for establishing mathematical results and to provide a knowledge base for advanced study or research in Mathematics.
PSO 3: Execute new ideas in the field of research and development using principles of Mathematics learned through activities such as expert lecturers, workshops, seminars, and field projects.
B.Sc. (Hons.) Mathematics with a specialisation in Data Science and Computational Mathematics prepares students to be suitable for various roles in the industry such as:
- Data Analyst
- Software Developer
- Cryptanalyst
- Business Analyst
- Auditor
- Accountant
- Research Analyst
- Content Developer
The curricula for the program have been framed in consultation with industry, academia, alumni, and parents to make the students industry-ready upon graduation.
BSc. (Hons.) Mathematics Specialisation in Data Science
Curriculum 2023 – 2026
SEMESTER I
| Subject | Credits | |
| G | Physics/Chemistry/Geology | 6 |
| M | Environmental Sciences | 2 |
| M | Algebra | 5 |
| M | Differential Calculus | 4 |
| M | Differential Calculus Lab | 1 |
| L | Learning How to Learn | 2 |
| L | Living Conversation | 2 |
| M | Computational Techniques | 2 |
| Total | 24 |
SEMESTER II
| Subject | Credits | |
| G | Physics/Chemistry/Geology | 6 |
| M | Real Analysis I | 5 |
| M | Linear Algebra | 5 |
| M | Integral Calculus | 4 |
| M | Integral Calculus Lab | 1 |
| L | Leadership & Teamwork | 2 |
| L | Critical Thinking & Writing | 3 |
| Total | 26 |
SEMESTER III
| Subject | Credits | |
| G | Physics/Chemistry/Geology | 6 |
| M | Analytical Geometry | 5 |
| M | Complex Analysis | 5 |
| M | Ordinary Differential Equations | 4 |
| M | Ordinary Differential Equations Lab | 1 |
| M | Skill Enhancement Electives I | 2 |
| L | Design Thinking | 2 |
| L | Social Internship | 0 |
| Total | 25 |
SEMESTER IV
| Subject | Credits | |
| G | Physics/Chemistry/Geology | 6 |
| M | Real Analysis II | 5 |
| M | Probability and Statistics | 5 |
| M | Function of Several Variable & PDE | 4 |
| M | Function of Several Variable & PDE Lab | 1 |
| M | Skill Enhancement Electives II | 2 |
| L | Working with Data | 2 |
| L | Ethical Leadership in the 21st Century (Human Values and Ethics) | 3 |
| Total | 28 |
SEMESTER V
| Subject | Credits | |
| M | Advanced Algebra | 5 |
| M | Linear and Non-linear Programming | 5 |
| M | Specialisation Course I (Data Science/ Computational Mathematics) | 4 |
| M | Specialisation Course II (Data Science/ Computational Mathematics) | 4 |
| M | Seminar | 2 |
| L | Persuasive Presence | 2 |
| L | Environment and Sustainability - Himalaya Fellowship | 3 |
| Total | 25 |
SEMESTER VI
| Subject | Credits | |
| M | Mathematical Methods | 4 |
| M | Mathematical Methods Lab | 1 |
| M | Specialisation course III (Data Science/ Computational Mathematics) | 4 |
| M | Specialisation course IV (Data Science/ Computational Mathematics) | 4 |
| M | Dissertation | 6 |
| L | *Signature Course | 3 |
| (Solving Complex Problems/Technologies of the Future/Future Casting/Managing Relationships and Being Happy) | ||
| Total | 22 |
SPECIALISATION
| 1 | Bayesian Data Analysis | 4 |
| 2 | Financial Data Analysis | 4 |
| 3 | Big Data handling with Hadoop and Spark | 4 |
| 4 | Time Series and Forecasting Methods | 4 |
| 5 | Multivariate Statistics | 4 |
| 6 | Text Analytics | 4 |
Skill enhancement courses
| Logic and Sets | 2 |
| Computer Graphics | 2 |
| Graph Theory | 2 |
| Operating System: Linux | 1 |
| Operating System: Linux Lab | 1 |
| Introduction to MATLAB Programming | 1 |
| Introduction to MATLAB Programming Lab | 1 |
| Weather Forecasting | 2 |
| Latex and HTML | 1 |
| Latex and HTML Lab | 1 |
| Computer Algebra Systems and Related Software's | 1 |
| Computer Algebra Systems and Related Software's Lab | 1 |
Generic Elective courses
| Calculus | 6 |
| Differential Equations | 6 |
| Algebra | 6 |
| Linear Algebra | 6 |
| Matrices | 6 |
| Computer Programming | 4 |
| Computer Programming Lab | 2 |
| Real Analysis | 6 |
| Numerical Methods | 6 |
| Complex Analysis | 6 |
| Linear Programming | 6 |
| Probability and Statistics | 6 |
| Cryptography and Network Security | 6 |
| Information Security | 6 |
| Applications of Algebra | 6 |
| Combinatorial Mathematics | 6 |
We, at the department, believe in experiential teaching and learning and strive to imbibe problem-solving skills and critical thinking in the students. The following practices are in place to improve the quality of teaching-learning and student experience:
- Design and review of individual course plans at the beginning of session
- Course Completion Report (CCR)
- Academic planning and monitoring
- Real-world/Project-based assignments
- Quality laboratory experience
- Encouraging advanced learner
- Slow learners support
- ICT-enabled classroom (sound system, mic and projector)
- Guest lectures from industry experts
- Industry-led courses
- Certification courses
- Professional Software Training (PST) and Certification
- NPTEL lectures
- Prototype development
- Use of virtual labs
- Participation in competitive events (in-house/national/international)
- Participation in conferences/seminars/workshops (national/international)
Minimum 50% marks in Class X and XII (PCM/B)
Personal Interview
Details
PROGRAM EDUCATIONAL OBJECTIVES (PEOs)
PEO-1: To train students to appreciate mathematical and computational skills to clear various competitive examinations including civil services, and JAM.
PEO-2: To provide knowledge of highest standards in Mathematics so that the students are able to utilize significant opportunities in various service sectors such as banks, government offices and higher studies in leading universities and institutions.
PEO-3: To engage students in life-long learning and guide them to continuously explore, learn and adapt new skills that will help them in their higher education and career prospects.
PEO-4: To encourage students to be committed towards ethical practices and development of the society
PROGRAM-SPECIFIC OUTCOMES (PSOs)
PSO 1: Understand mathematical concepts in the field of algebra, analysis, computational techniques, optimisation, differential equations, etc.
PSO 2: Able to acquire critical thinking and effective reasoning skills for establishing mathematical results and to provide a knowledge base for advanced study or research in Mathematics.
PSO 3: Execute new ideas in the field of research and development using principles of Mathematics learned through activities such as expert lecturers, workshops, seminars, and field projects.
CAREER PROSPECTS
B.Sc. (Hons.) Mathematics with a specialisation in Data Science and Computational Mathematics prepares students to be suitable for various roles in the industry such as:
- Data Analyst
- Software Developer
- Cryptanalyst
- Business Analyst
- Auditor
- Accountant
- Research Analyst
- Content Developer
The curricula for the program have been framed in consultation with industry, academia, alumni, and parents to make the students industry-ready upon graduation.
CURRICULUM
BSc. (Hons.) Mathematics Specialisation in Data Science
Curriculum 2023 – 2026
SEMESTER I
| Subject | Credits | |
| G | Physics/Chemistry/Geology | 6 |
| M | Environmental Sciences | 2 |
| M | Algebra | 5 |
| M | Differential Calculus | 4 |
| M | Differential Calculus Lab | 1 |
| L | Learning How to Learn | 2 |
| L | Living Conversation | 2 |
| M | Computational Techniques | 2 |
| Total | 24 |
SEMESTER II
| Subject | Credits | |
| G | Physics/Chemistry/Geology | 6 |
| M | Real Analysis I | 5 |
| M | Linear Algebra | 5 |
| M | Integral Calculus | 4 |
| M | Integral Calculus Lab | 1 |
| L | Leadership & Teamwork | 2 |
| L | Critical Thinking & Writing | 3 |
| Total | 26 |
SEMESTER III
| Subject | Credits | |
| G | Physics/Chemistry/Geology | 6 |
| M | Analytical Geometry | 5 |
| M | Complex Analysis | 5 |
| M | Ordinary Differential Equations | 4 |
| M | Ordinary Differential Equations Lab | 1 |
| M | Skill Enhancement Electives I | 2 |
| L | Design Thinking | 2 |
| L | Social Internship | 0 |
| Total | 25 |
SEMESTER IV
| Subject | Credits | |
| G | Physics/Chemistry/Geology | 6 |
| M | Real Analysis II | 5 |
| M | Probability and Statistics | 5 |
| M | Function of Several Variable & PDE | 4 |
| M | Function of Several Variable & PDE Lab | 1 |
| M | Skill Enhancement Electives II | 2 |
| L | Working with Data | 2 |
| L | Ethical Leadership in the 21st Century (Human Values and Ethics) | 3 |
| Total | 28 |
SEMESTER V
| Subject | Credits | |
| M | Advanced Algebra | 5 |
| M | Linear and Non-linear Programming | 5 |
| M | Specialisation Course I (Data Science/ Computational Mathematics) | 4 |
| M | Specialisation Course II (Data Science/ Computational Mathematics) | 4 |
| M | Seminar | 2 |
| L | Persuasive Presence | 2 |
| L | Environment and Sustainability - Himalaya Fellowship | 3 |
| Total | 25 |
SEMESTER VI
| Subject | Credits | |
| M | Mathematical Methods | 4 |
| M | Mathematical Methods Lab | 1 |
| M | Specialisation course III (Data Science/ Computational Mathematics) | 4 |
| M | Specialisation course IV (Data Science/ Computational Mathematics) | 4 |
| M | Dissertation | 6 |
| L | *Signature Course | 3 |
| (Solving Complex Problems/Technologies of the Future/Future Casting/Managing Relationships and Being Happy) | ||
| Total | 22 |
SPECIALISATION
| 1 | Bayesian Data Analysis | 4 |
| 2 | Financial Data Analysis | 4 |
| 3 | Big Data handling with Hadoop and Spark | 4 |
| 4 | Time Series and Forecasting Methods | 4 |
| 5 | Multivariate Statistics | 4 |
| 6 | Text Analytics | 4 |
Skill enhancement courses
| Logic and Sets | 2 |
| Computer Graphics | 2 |
| Graph Theory | 2 |
| Operating System: Linux | 1 |
| Operating System: Linux Lab | 1 |
| Introduction to MATLAB Programming | 1 |
| Introduction to MATLAB Programming Lab | 1 |
| Weather Forecasting | 2 |
| Latex and HTML | 1 |
| Latex and HTML Lab | 1 |
| Computer Algebra Systems and Related Software's | 1 |
| Computer Algebra Systems and Related Software's Lab | 1 |
Generic Elective courses
| Calculus | 6 |
| Differential Equations | 6 |
| Algebra | 6 |
| Linear Algebra | 6 |
| Matrices | 6 |
| Computer Programming | 4 |
| Computer Programming Lab | 2 |
| Real Analysis | 6 |
| Numerical Methods | 6 |
| Complex Analysis | 6 |
| Linear Programming | 6 |
| Probability and Statistics | 6 |
| Cryptography and Network Security | 6 |
| Information Security | 6 |
| Applications of Algebra | 6 |
| Combinatorial Mathematics | 6 |
INNOVATIVE TEACHING AND LEARNING
We, at the department, believe in experiential teaching and learning and strive to imbibe problem-solving skills and critical thinking in the students. The following practices are in place to improve the quality of teaching-learning and student experience:
- Design and review of individual course plans at the beginning of session
- Course Completion Report (CCR)
- Academic planning and monitoring
- Real-world/Project-based assignments
- Quality laboratory experience
- Encouraging advanced learner
- Slow learners support
- ICT-enabled classroom (sound system, mic and projector)
- Guest lectures from industry experts
- Industry-led courses
- Certification courses
- Professional Software Training (PST) and Certification
- NPTEL lectures
- Prototype development
- Use of virtual labs
- Participation in competitive events (in-house/national/international)
- Participation in conferences/seminars/workshops (national/international)
ELIGIBILITY
Minimum 50% marks in Class X and XII (PCM/B)
SELECTION CRITERIA
Personal Interview
