Master’s in Data Science & Business Analytics

This Prework stage introduces concepts that the program will take to greater depth, so students are well grounded from the first day and the entire group has an even level, which allows for further and better progress, as well as improving cooperation between all participants.

1. Computer basics: Concepts including hardware and software, CPU, memory, storage devices, operating systems and networks.
2. Introduction to programming languages: Explanation of what a programming language is, what it is used for, and the types of languages that exist (compiled and interpreted). An overview of the most commonly used languages today, and why they are used.
3. Fundamental programming concepts: Discussion of variables, data types, operations, control flow structures (if/else, loops), and functions. How to break down a complex problem into smaller, more manageable subproblems. Issues are explained in a basic way to avoid overlapping with the Programming Fundamentals module (with Python).
4. Development tools and good practices. Introduction to the use of an IDE, such as PyCharm or VSCode, as well as notebooks. Discussion about version control with Git. Underlining good programming practices, such as the importance of commenting code and following style conventions (Pythonic Code).
5. Introduction to data structures: Overview of concepts such as arrays, lists, sets, dictionaries/maps and trees. The focus is not confined to a specific programming language, but also at pseudocode level. The aim is for students to understand what they are, what they are used for, and when it might be appropriate to use one data structure over another.
6. Fundamental database concepts: Explanation of what a database is, what it is used for, and the various types that exist (e.g. relational and non-relational databases). Introduction to key concepts such as table, record, field, primary key, and relationships between tables.

50h.

Review of the key programming concepts needed to process and use data by means of code. Introduction to R programming language and an extensive overview of the capabilities that Python offers.

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

80h.

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.

80h.

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

80h.

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

80h.

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

80h.

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.

64h.

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.

64h.

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.

80h.

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.

80h.

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

80h.

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.

64h.

• 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.

192h.

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.