In the lectures, students will be taught the theoretical principles behind Big Data storage and processing solutions. They will also learn about the challenges that led to the development of these solutions. The potential of Big Data across various academic disciplines will be explored through examples, with an emphasis on social sciences. During the labs, students will have the chance to gain hands-on experience and develop their skills in creating Big Data solutions using Spark. solutions with Spark.
Teaching Assistants: Nuno Alpalhão, Liah Rosenfeld, Maria Almeida, and Niclas Sturm
In this introductory curricular unit, students will be introduced to the basic programming principles and develop essential programming skills with hands-on practical activities and data science projects. To that end, students will work with Python in the Jupyter Lab supported by the Anaconda environment.
Teaching Assistants: Vítor Manita, Nuno Alpalhão, Maria Almeida, and Niclas Sturm
An introductory curricular unit to the principles of data science. In that sense, we will develop skills in Pandas/Python to explore data cleaning, visualization, and processing principles that are essential to perform data exploration and descriptive analysis. The curricular unit also introduces students to Unsupervised Learning and Dimensionality Reduction techniques.
Teaching Assistants: Vítor Manita
We cover the fundamental principles of Network Science, from the fundamentals of Graph Theory to what characterizes real-world Network Analysis. Finally, we discuss how networks influence the dynamical processes they support and their implication to data sciences.
Teaching Assistants: Mariel Barbachan
We cover the fundamental principles of Social Network Analysis by connecting concepts from data-driven Network Analysis to Social Sciences and Marketing. We discuss how networks influence information propagation in light of the current state-of-the-art models of influence propagation, discussing their relevance to Marketing.
Teaching Assistants: Pedro Alves and Liah Rosenfeld
Introduction to Network Science with an applied emphasis on data-driven problems. Students will be introduced to the fundamental concepts of Graph Theory and then to more advanced topics. This curricular unit has a heavy hands-on component and students are expected to be able to develop a network-based project by the end of the semester.
Teaching Assistants: Mariel Barbachan