Mathematical Tools for Data Science

Data science is about learning from data. It is rapidly emerging as a fundamental pillar of our digital society. Positioned at the intersection of Applied Mathematics, Computer Science and Statistics, it demands an integrated approach, where individuals possess solid knowledge and a sound intuition of how to handle (complex) data with uncertainty, how to formulate pertinent questions, and how to solve them using mathematical modeling and efficient numerical algorithms, taking advantage of specialized software.

The MSSG Spring Semester offers a range of courses aimed at strengthening these indispensable skills. Special emphasis is placed on project-based learning, allowing students to put their knowledge into practice and gain hands-on experience.

The goal of MSSG Spring Semester is to provide students with a solid and rigorous problem solving-oriented understanding of Data Science. Data science involves strong interactions between statistics, mathematical modeling, pure mathematics and computer science. Students will be exposed to concepts and terminology for preparing them to communicate with and take part in Data Science teams. In addition, they will learn the fundamental theoretical bases of quantitative methods, statistical models, and computer science, equipping them with the ability to choose relevant and efficient algorithmic solutions for solving problems in Data Science and mathematical modeling.

Click on the tiles below to see details of each course of the Spring Program

Design of Algorithms  Probability and Statistics  Numerical Optimization and Machine Learning  Geometric and Graphing Tools  Software Tools for Data Science

Other courses, including an Independent Research Project, might be offered depending on the academic background of the students admitted to the program. The minimum course load is three mathematics courses per semester. A one-week workshop on computational tools for data science is organized at the beginning of the semester

The aim of this semester is to learn and master the mathematical and computational foundations necessary for students to deepen their knowledge and practical skills in areas related to Data Science. By the end of the semester, students should:

1. Dominate the mathematical foundations of Data Science.
2. Dominate the algorithmic foundations of Data Science (e.g., the basic tools to quantify algorithm complexity) and be able to identify canonical algorithmic problems and propose adequate algorithms to solve them.
3. Be able to effectively participate in a multi-disciplinary team involving statisticians, mathematicians, computer scientists and specialists from other areas to solve a data science problem as a team.
4. Discover different aspects of the Mexican culture by immersing themselves in one of its most vibrant historical cities in the heartland of Mexico.

Successful applicants will:

• Be currently enrolled in a higher education institution, pursuing a major that includes components involving Mathematics, Statistics, Data Science, or Computer Science.
• Have studied at least one linear algebra course. The student should be familiar with the concepts of vector spaces, bases, dimensions, matrices, linear applications, determinants, and kernels.
• Have studied differential, integral and multivariate calculus courses. The applicant should be familiar with the notions of limits, integration, derivatives, and series. 
• Have studied at least one introductory programming course. The applicant should understand the notions of control structures, conditionals, variables, and functions.