Mathematical Programming for Data Science

Mathematical Programming for Data Science provides a comprehensive exploration of optimization techniques essential for data analysis and decision-making. Participants will engage in hands-on projects that apply mathematical models to real-world data challenges, enhancing their analytical skills and technical proficiency. The course emphasizes practical applications, allowing learners to tackle complex problems using various programming languages and tools.
Through a project-based approach, attendees will collaborate on assignments that simulate industry scenarios, culminating in a final project that showcases their ability to implement mathematical programming strategies. This course not only equips participants with the necessary skills to excel in data-centric roles but also encourages the dissemination of their findings through publication in Cademix Magazine, fostering a culture of knowledge sharing and professional growth.
Linear Programming: Formulation and Solutions
Integer and Mixed-Integer Programming Techniques
Nonlinear Programming Methods
Dynamic Programming Applications
Stochastic Programming Fundamentals
Optimization in Machine Learning
Constraint Satisfaction Problems
Game Theory and Decision Making
Case Studies in Industry Applications
Final Project: Implementing a Mathematical Model on a Real Dataset

Equip participants with advanced mathematical programming skills applicable in data science, enabling them to solve complex optimization problems.

Graduates, job seekers, business professionals, and optionally researchers or consultants.

Basic knowledge of calculus, linear algebra, and programming (Python or R preferred).