Materias – Data Science and Engineering

Data Science and Engineering(Classroom modality)
(RVOE) as set forth by Ministerial Agreement No. 15018, published in the Official Journal of the Federation on November 29, 1976.
Practical Area
Modeling Area
Disciplinary Fundamentals Area
Specific Complementary Area
Elective Complementary Area
University Curriculum


Data Visualization Laboratory

In this course, you will learn to identify the issues that data science addresses, its applications and impact. You will also learn techniques to graphically represent results obtained from forecasts of different proposed models and you will apply procedures to manipulate data for the purpose of identifying the graphic representation that best suits the problem at hand, considering that the target audience for the most part is not proficient in technical matters.

Algorithms and Programming

Differential Calculus

Oral and Written Communication

Information and Numerical Data Management



Data Processing Laboratory

Today, millions of pieces of data are captured every minute, from the measurement of natural phenomena, laboratory experiments, internet user searches, trends, interests, etc. These datasets may contain spurious, repeated, missing, or incorrect data, among other inaccuracies, which means that the data need to be processed in order to convert them into information. The purpose of this lab course is to use computer tools to build and prepare datasets obtained from a variety of sources.

Characteristics Engineering

Programming for Data Analysis

Probability and Statistics

Integral Calculus

Linear Algebra



Data Modeling Laboratory

A successful data-based model requires data quality, which in turn requires pre-processing the data and, once the model is built, assessing its validity by measuring and assessing unknown test data.

In this course you will prepare data and build forecasting models, which will be validated using error metrics and acceptance criteria. Furthermore, you will address and justify the model’s construction by communicating results, assessing the model’s forecasts and applying the methodology established for data preparation.

Database Design

Bayesian Statistical Methods

Multivariable Differential Calculus

Linear Numerical Algebra

Ethics, Identity and Profession



Data Engineering Project

Data professionals must deal with large amounts of data, different formats, and a considerable volume of variables, which is why they need to make modifications to traditional data analysis methods and implement different tools and strategies to meet these challenges.

This course is designed for students to solve predictive modeling problems taken from real-life scenarios, and to implement these models in computer systems using available cloud resources or other types of platforms designed for massive data processing or final users.

Time Series

Graph Mining

Multivariable Statistical Analysis

Optimal Estimation

Knowledge and Culture



Data Science Project

It is not enough to know how to solve a problem; it is also important to know how to manage a project in order to achieve planned objectives within a specific timeframe. This process involves planning, proposing solutions or alternatives, as well as execution, monitoring, and feedback. In this course, you will learn about the management of data science projects with an emphasis on the execution, monitoring and feedback stages, as well as on the effective communication of the model’s results and forecasts.

Regression Analysis

Machine Learning

Text Mining

Historical Social Context

Elective I



Ethics in Data Science

Non-linear Forecasting Models

Deep Learning

Deep Learning is a very useful tool for developing solutions in artificial vision, voice processing, natural language processing, automatic translation and search engines; like Machine Learning, it offers a way to deal with problems of classification and forecasting. However, Deep Learning techniques have achieved better results based on the processing of large amounts of data and the use of deep neuronal networks. In this course, you will learn Deep Learning techniques to solve classification and forecasting problems with applications in different areas of knowledge, such as computer vision, and audio and natural language processing. For this, you will analyze algorithms, their foundations, and considerations for their application, so that you can use and adapt them correctly to solve problems in a particular context.

Contemporary Ethical Challenges I

Innovation and Entrepreneurship

Elective II

Specific Comple-mentary Course I


Professional Application Project I

Contemporary Ethical Challenges II

Elective III

Specific Complementary Course II

Specific Complementary Course III


Professional Application Project II

Elective IV

Elective V

Specific Complementary Course IV

Practical Area
Modeling Area
Disciplinary Fundamentals Area
Specific Complementary Area
Elective Complementary Area
University Curriculum