B.Sc. Applied Mathematics
CURRICULUM BASED ON NEP 2020 GUIDELINES
Why choose B.Sc.Applied Mathematics?
- Real-World Relevance: Solve practical problems in science, engineering, business, and technology.
- Diverse Career Opportunities: Roles in data science, finance, IT, engineering, research, and more.
- Foundation for Interdisciplinary Fields: Prepares for AI, cryptography, financial engineering, and climate modeling.
- Strong Analytical and Problem-Solving Skills: Logical thinking and modeling skills are highly valued.
- High Demand in Industry: Growing need for math modeling and data analysis in public and private sectors.
Eligibility
- Passed 12th in Science stream or an equivalent exam.
- Completed a 3-year Diploma course after 10th from the Government of Maharashtra or an equivalent board.
Admission Process
Step – 1
Fill Application Form
Step – 2
Check General List and report discrepancies, if any.
Step – 3
Check schedule for Table admission round
Step – 4
if eligible for Table admission round, remain present in college with all relevant documents, original and photocopy.
Step – 5
once document verification is done according to merit, fill admission form, select courses from other categories and pay admission fees.
Career Opportunities
Data Science and Analytics
Finance and Actuarial Science
Engineering and Manufacturing
Software Development and IT
Operations Research
Scientific Research and Academia
Course Structure
Core subjects: Calculus, Algebra, Differential Equations, Statistics.
Application-focused subjects: Mathematical Modeling, Operations Research.
Programming and computing: Python, MATLAB, numerical methods.
Lab and project work: Hands-on training in simulations and real-world problem-solving.
Placements & Skill Development
Skill development in
Analytical thinking and problem solving
Prepares students for
Program Highlights
Strong foundation in core math: Calculus, Algebra, Differential Equations, and Statistics.
Real-world applications: Mathematical modeling, Operations Research, Optimization
Computational skills: Programming (Python, MATLAB) and numerical methods.
Interdisciplinary exposure: Finance, physics, data science, and engineering.
Project and lab work: Simulations, modeling, and research-based learning.
Career and research readiness: Prepares for analytics, tech, finance, or higher studies.
