Programme Structure for 2024/2025
| Curricular Courses | Credits | |
|---|---|---|
| 1st Year | ||
|
Introduction to Data Science
6.0 ECTS
|
Introduction to Data Science
On successful completion of the course, students should be able to:
LG1. Explain what are the aplication domains and what skills are necessary to analyse various data.
LG2. Define concepts such as exploratory data analysis, statistical inference and modelling, machine learning and high-dimensional data analysis.
LG3. Justify the need for reproducible research, the ethical and normative issues behind data-driven decision-making, and its potential bias.
LG4. Explain the underlying techniques of visualization and communication of results.
Syllabus contents (SC):
SC1. Introduction to Data Science: main concepts and methodologies.
SC2. Data to support to decision making: privacy and ethics.
SC3. Presentation of case studies that include the complete data cycle from different areas.
SC4. Collection and treatment of unstructured data.
SC5. Concepts and techniques for data visualization and visual perception for the communication of knowledge.
SC6. Structured data preparation elementary techniques.
SC7. Construction of inference models based on the data.
The following teaching-learning methodologies (MEA) will be used:
MEA1: Illustrative, exemplifying theoretical concepts in real contexts and through invited lectures.
MEA2: Discussion with presentation and discussion of group work.
MEA3: Participative and Active by conducting research work and class exercices.
MEA4: Experimental, developing and exploring models using ""black box"" software.
MEA5: Self-study autonomous work by the student
Rachel Schutt and Cathy O'Neil. 2013. Doing Data Science: Straight Talk from the Frontline. O'Reilly Media, Inc. Alberto Cairo. 2012. The Functional Art: An introduction to information graphics and visualization (1st. ed.). New Riders Publishing, USA. Voeneky, S., Kellmeyer, P., Mueller, O., & Burgard, W. (Eds.). 2022. The Cambridge Handbook of Responsible Artificial Intelligence: Interdisciplinary Perspectives. Cambridge: Cambridge University Press.
P. Mathur. 2028. Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance. Apress. P. Tattar, T. Ojeda, S. P. Murphy B. Bengfort, A. Dasgupta. 2017. Practical Data Science Cookbook, Second Edition. Packt Publishing. I. Foster, R. Ghani, R. S. Jarmin, F. Kreuter, J. Lane. 2016. Big Data and Social Science: A Practical Guide to Methods and Tools, 1st Edition. CRC Press, Chapman & Hall. T. W. Miller. 2015. Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python?. O'Reilly. M. N. Jones, 2016. Big Data in Cognitive Science (Frontiers of Cognitive Psychology), Taylor & Francis. F. Provost. 2013. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. O'Reilly. S. Few. 2004. Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press.