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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. |
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Algorithms and Programming
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Differential Calculus
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Oral and Written Communication
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Information and Numerical Data Management
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Languages
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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. |
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Characteristics Engineering
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Programming for Data Analysis
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Probability and Statistics
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Integral Calculus
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Linear Algebra
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Languages
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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. |
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Database Design
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Bayesian Statistical Methods
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Multivariable Differential Calculus
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Linear Numerical Algebra
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Ethics, Identity and Profession
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Languages
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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. |
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Time Series
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Graph Mining
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Multivariable Statistical Analysis
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Optimal Estimation
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Knowledge and Culture
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Languages
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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. |
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Regression Analysis
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Machine Learning
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Text Mining
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Historical Social Context
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Elective I
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Languages
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Ethics in Data Science
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Non-linear Forecasting Models
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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. |
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Contemporary Ethical Challenges I
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Innovation and Entrepreneurship
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Elective II
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Specific Comple-mentary Course I
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Professional Application Project I
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Contemporary Ethical Challenges II
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Elective III
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Specific Complementary Course II
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Specific Complementary Course III
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Professional Application Project II
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Elective IV
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Elective V
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Specific Complementary Course IV
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