Programming (Course 1, Semester 1) Data structures and Algorithms (Course 1, Semester 2) Data Base (Course 2, Semester 1) Web Applications (Course 3, Semester 1)

BC1: Students should be able to demonstrate they have acquired and understood the knowledge associated to an area that starts from high school education and reach a level that although it is based on text books, it also includes aspects that include concepts coming from up-to-date knowledge in the referread area. BC2: Students should be able to apply the acquired knowledge to their job in a professional way and should incorporate the required competences that can be shown through solid arguments and the resolution of problems within their area of study. EC15: Ability to design solutions based on automatic knowledge within applications applied to specific domains such as: recommendation systems, natural language processing, the WEB or online social networks. EC19: Ability to develop web and mobile applications and crawlers to collect data using them. EC20: Ability to develop data visualization tools to communicate the results derived from data analysis.

1. Web crawlers 2. Web usage mining · Data capture · Preprocessing · Mining algorithms 3. Link mining 4. Social network analysis · Data capture · Centrality and influence · Communities detection · Visualization of social networks 5. Query logs mining 6. Linked data

The course will be based in the following activities: - Lectures: theoretical lessons that will introduce the main concepts of the course. Students participation to discuss the concepts and problems introduced in the lectures will be encouraged. - Lab: practical lessons in which students will bring to practice the concepts introduced in lectures. Students will have to solve practical problems associated to web analytics. - Web analytics project: Students will be assigned a project that will be developed throughout the semester in groups of 2. Students have to either chose one project among a list of projects pre-defined or propose their own project. In the latter case, the responsible professor has to approve the student proposal. The project will include the following elements: 1- An initial definition of the goals of the project, technology used and expected results 2- Implementation of a web crawler that collects information from some popular online service or social network. 3- Data analysis using up to date technological frameworks (for instance python, R, etc). 4- Results visualization. The students will defend their project in a public exposition to the rest of students at the end of the semester. There will be a number of lab classes that will be used to supervise the evolution of the project and to allow students progressing in its development. The students will get access to meetings with professors every week individually or collectivelly in order to clarify theorical and/or practical concepts. In addition, these meetings are valid to access to a more detailed supervision of student proyects.