Maestría Oficial en Data Science

Revisión de los conceptos claves de programación necesarios para abordar el tratamiento y aprovechamiento de los datos mediante código. Presentación de las capacidades que ofrece Python.

1. Fundamental concepts of Python and libraries for data science: Numpy, Pandas.
2. Python intermediate and advanced.
3. Data processing and visualization with Python.

This module examines what databases are and discusses the main types. Students explore the world of relational database modeling and learn to program in SQL. In addition, the module looks at ETL processes and how they are designed and implemented.

• Database design
• SQL Standard I
• SQL Standard II
• The data warehouse and ‘extract, transform and load’ (ETL) tools and processes.

Review of the life cycle of data and how it affects the data analysis process. Students also explore the world of data visualization and learn to design dashboards in PowerBI.

1. Data quality and life cycle
2. Data preparation and pre-processing
3. Visualization tools and techniques I
4. Visualization tools and techniques II

This module examines big data analysis as a tool to address key research questions and issues. Students will understand what is meant by big data, how it has developed over time, the causes that have led to the emergence of big data technologies and how it compares with traditional business intelligence.

1. Introduction to the world of big data
2. Business intelligence vs big data
3. Big data technologies
4. The value of data and applications across sectors

Discover and use the tools that comprise the ecosystem in order to process vast quantities of data. These include Spark, Hadoop, and NoSQL databases.

1. HADOOP and its ecosystem
2. SPARK
3. NoSQL databases
4. Cloud platforms

In this module students learn the fundamental concepts of programming in R, a programming language for statistical computing. The main statistical concepts essential for data analysis are also introduced here.

1. Introduction to statistics
2. Probability and sampling
3. Inference and lineal regression
4. Experiment design

Students learn the fundamental concepts and algorithms of machine learning, one of the cornerstones of data science and artificial intelligence.

1. Machine learning tools and the techniques and applications of supervised learning
2. Techniques and applications of unsupervised learning
3. Deep learning models and techniques
4. Cloud solutions for machine learning

The purpose of the Directed Research I module is to enable students to see that research is a systematic and ordered process aimed at managing knowledge. Consequently, during every academic training process, it is essential to carry out research work that is geared towards the needs of a given context and that demonstrates the student's research capabilities.

1. The research question.
2. The rationale for the research.
3. Establishing research objectives.
4. The contextual and temporal scope of the research.

Students examine the concept of artificial intelligence, its meaning and the types of problems it can solve. Identification of decision-making techniques (expert systems and supervised learning), as well as their applications. Analysis of reinforcement learning, its life cycle, the most important components, and the types of problems it solves.

Artificial intelligence and applications in decision making. Reinforcement learning and its applications. Techniques and applications of natural language processing (NLP). Recommendation systems and applications.

Analysis of the concept of the digital transformation from the point of view of the technologies that drive it, with a special focus on the following trends: Big Data, Artificial Intelligence, Blockchain, Internet of Things, Industry 4.0 and Smart Cities.

The Digital Transformation. Blockchain. Internet of Things. Industry 4.0 and Smart Cities

How analytics can be applied to specific scenarios. Discovering how specialized analytical methods can be applied to different types of data.

Scalable analytics. Analysis of social networks and the Internet of Things. Analysis of the financial area and customer service. Analysis of information retrieval techniques.

• Recognizing types of research in line with the knowledge generated.
• Study of research designs.
• Study of the techniques and instruments of data collection during the research process.
• Study of the criteria that characterize the study units and study population.
• Building data collection instruments.
• Applying reliability and validity tests to the data collection instrument(s).
• Collecting data according to the criteria defined in the study.
• Understanding the data analysis process and its stages.
• Identifying analysis techniques to use in line with data codes (verbal or numerical codes).

Data collection techniques. Data collection instruments. Research study units and study population. Instrument validity and reliability procedure. Data analysis techniques.

At Master's level, the written work must be presented and students must generate scientific products from their research experience. Students are responsible for organizing the presentation of the written work.

Introduction Formal issues relating to the presentation of the work. Institutional procedures. Viva voce. Publication.