Handling, processing, and analyzing voluminous and complex data are now incredibly common tasks that influence many aspects of our everyday life. Thanks to the advent of powerful data collection and storage devices in recent decades, the world is all-connected. Geographical, social and cultural preferences, and transportation data, to name just a few examples, are being increasingly used to inform decision-making processes, to discover interesting relationships between variables, or to predict future trends. Major industries and government institutions are now forming data science teams to undertake these challenges.

The scientific and technical issues used to address the above-mentioned topic are complex and highly multidisciplinary in nature. In particular, mathematicians are called upon to prescribe, interpret and supervise quantitative methods of analysis and their corresponding computer implementations.

The goal of this semester is to provide students with a solid and rigorous understanding of the subject’s core. The study of data science with a truly multi‑disciplinary and sound approach necessarily involves strong interaction between statistics, mathematical modeling, pure mathematics and computer science. Students will be exposed to concepts and terminology from data science, 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

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. The aim is that, by the end of the semester, students should:

1. arrow_rightmaster the mathematical foundations of data science; i.e., the most important concepts in discrete mathematics, rigorous proof techniques and multivariate statistics in order to provide sound mathematical models for data science problems
2. arrow_rightmaster 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 algorithm paradigms to solve them
3. arrow_rightbe 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. arrow_rightdiscover 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:

• arrow_rightbe currently enrolled in a higher education institution, pursuing a major that includes components involving Mathematics, Statistics, Data Science, or Computer Science.
• arrow_righthave 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.
• arrow_righthave studied differential, integral and multivariate calculus courses. The applicant should be familiar with the notions of limits, integration, derivatives, and series. 
• arrow_righthave studied at least one introductory programming course. The applicant should understand the notions of control structures, conditionals, variables, and functions.