During the subject the students will learn how to manage data, perform time series analysis and forecasting in Python. They will learn how to fit data to probability distributions and regression models, use Python/Pandas to perform general statistical analysis in their data, apply appropriate and useful Geographic Information System (GIS) tools and Geostatistics in Python to find spatial data patterns.

Students will learn analytical skills and the most important supervised and unsupervised learning algorithms for regression, classification and clustering, to apply them to an analytic or business intelligence strategy. They will learn how to use Python libraries to run these data mining algorithms and use the trained models for prediction.

The students will understand how big data projects can bring value to an organization, how data can be applied to solve real business problems because of the large amounts of data available generated by digitalization. Student will also develop communication and persuasion skills, since data results and projects usually have to be given and carried out in a company context.

Students will experience the process of discovery, interpretation and ideation. They will learn techniques to identify opportunities and understand customer needs. They will integrate the process of conceptualization and creative design that will help them design innovative digital solutions.

Students will learn how to use software for managing large amounts of data and fast data streams, particulary focusing on distributed systems for storage and processing data with an emphasis on the open source. They will learn how to design efficient system architectures to capture, process and store Big Data, as well as the best practices in provisioning, deploying, tuning and maintaining Big Data systems.

As a continuation of the Classical Data Analysis module, this one will develop the skills to deal with complex data types such as a “basket” (a set of elements like a shopping cart), a network/graph, or georeferenced data. Students will learn different methods to analyse each data type, like association rules mining, algorithms, georeferencing, creation of maps and location-aware models.

Students will learn what are the legal barriers of the data management and the core security principles in a Big Data project, including confidentiality, integrity and availability (CIA). They will also learn the principles behind symmetric and asymmetric cryptography and they will apply them by building an end-to-end security implementation for a corporate network.

Students will learn to shape opportunities, provide the leadership and build the team to create economic and social value. They will create and develop business models for start-ups based on customer development and continuous validation, managing financial metrics and dealing with investors.

Students will learn the specialized data management infrastructures and algorithms needed to deal with challenging data sources. They will learn distributed algorithms, which are specifically designed for large-scale data being processed in a parallel/distributed computing environment. They will also perform operations in data streams, including clustering data, and use the map-reduce paradigm in a distributed setting.

Students will learn how to use artificial neural networks in data analysis projects. They will learn methods for training neuronal networks, modern neural network architectures and frameworks, text classification and image classification.

The students will learn the skills necessary to explain the processes they performed with the data, including the entire data analysis pipeline, and the obtained results, in an effective and clear way to a broad audience. This includes being able to communicate all assumptions and nuances that this may have. Data visualization paradigms will be introduced and explored in the context of storytelling, describing how to use them to convey complex data-driven messages.

The students will immerse in the Agile mindset by experimenting the Agile management practices. At the end of this subject they will be able to practice Scrum, understand Kanban, develop and idea from Vision to Deployment from an Agile perspective and define requirements with User Stories.