Checking date: 08/06/2021


Course: 2021/2022

Data Science
(17477)
Study: Bachelor in Management of Information and Digital Contents (340)


Coordinating teacher: CALZADA PRADO, FCO JAVIER

Department assigned to the subject: Department of Library Science and Documentation

Type: Electives
ECTS Credits: 6.0 ECTS

Course:
Semester:




Objectives
CG4 - Getting to know the research models and methods applied to the study of digital information. CE3 - Getting to know infrastructures, social aspects, technical and data provenance, as well as others related to data-intensive research. CE4 - Getting to know different methods to collect, process, clean and aggregate data considering the needs of users and organizations and the formats in which they need them.
Skills and learning outcomes
Description of contents: programme
This course will introduce you to Data Science, its concept, applications, and future perspectives in the Social Sciences. In a globalized, ever-changing, increasingly accelerated and complex world, having professionals who are able to collect, analyze, and interpret the vast amount of existing heterogeneous data (Big Data) is absolutely crucial for decision making in the business, social, economic, and political areas. Data Science has been labeled 'the sexiest job of the 21st century' (Harvard Business Review, 2012), and in fact there is a growing demand of professionals trained in this discipline. In this course, students will approach the management and analysis of different types of data -including those from surveys, web-based and social media, business data, and research data, among others- by means of the latest techniques and tools for statistical learning. Contents: 1. Foundations of Data Science: concept, theories, and approaches. 2. Preliminary analysis/preparation of data: how to collect, clean, treat and combine data from different sources. 3. Data visualization: best practices in large data visualization and communication. 4. Predictive tools: applications of the main tools for statistical learning, regression and classification.
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40
Calendar of Continuous assessment
Basic Bibliography
  • O'Neil, Cathy; Schutt, Rachel. Doing Data Science: Straight Talk from the Frontline. O'Reilly. 2013

The course syllabus may change due academic events or other reasons.