Degree | Type | Year | Semester |
---|---|---|---|
2500259 Political Science and Public Management | FB | 1 | 1 |
None.
The aim of this course is to familiarize students with the practice of data analysis. Students will learn to import, transform, and explore data to formulate and answer questions. We will prioritize practical training and the interpretation and presentation of results over mathematical issues. The course revisits the use of spreadsheets and introduces students to the R language of statistical computing through RStudio, to provide the essential tools for data management, description, and visualization, reproducibility, and effective communication of results. Throughout the course we will work with real-world, socially relevant data, while also encouraging a critical and responsible usage of open data.
1. Data science principles
2. Data import
3. Data transformation
4. Exploratory data analysis: how to describe and visualize data
5. Communication and reproducibility
All sessions consist of a mix of lecture and lab activities. Students are expected to bring their laptops to class.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
In-class sessions: lecture and lab activities | 49.5 | 1.98 | 1, 21, 5, 2, 3, 4, 9, 8, 13, 12, 10, 11, 18, 22, 16, 14, 15, 19, 20, 25, 24, 17, 23, 7, 6 |
Type: Supervised | |||
Tutorials | 15 | 0.6 | 1, 21, 5, 2, 3, 4, 9, 8, 13, 12, 10, 11, 18, 22, 16, 14, 15, 19, 20, 25, 24, 17, 23, 7, 6 |
Type: Autonomous | |||
Study, readings, assignments | 83.5 | 3.34 | 1, 21, 5, 2, 3, 4, 9, 8, 13, 12, 10, 11, 18, 22, 16, 14, 15, 19, 20, 25, 24, 17, 23, 7, 6 |
The evaluation will be based on the following activities:
To pass the course, it is required that all of the following conditions are met:
Retake process
Only the exam is recoverable; in-class exercises and home assignments are excluded from the retake process.
To be eligible to participate in the retake process, it is required that both these conditions are met:
Important considerations
The fact of taking the exam or handing in an exercise or an assignment exempts the student from the “Not assessable” grade.
In accordance with article 117.2 of the UAB Academic Regulation, the evaluation of those students who have been enrolled before may consist of a single synthesis examination. The students who wish to be evaluated this way should contact the professor at the beginning of the semester (first week of October at the latest).
Evidence of plagiarism or any other irregularity that could lead to a significant variation in the grade of an activity involves failing the corresponding evaluation with agrade of 0. In case of multiple irregularities in the evaluation of the same subject, the final grade of this subject will be 0.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Final exam | 50% | 2 | 0.08 | 1, 21, 5, 2, 3, 4, 9, 8, 13, 12, 10, 11, 22, 16, 14, 15, 19, 20, 25, 24, 17, 23, 7, 6 |
In-class exercises | 15% | 0 | 0 | 1, 21, 5, 2, 3, 4, 9, 8, 13, 12, 10, 11, 18, 22, 16, 14, 15, 19, 20, 25, 24, 17, 23, 7, 6 |
Take-home assignments | 35% | 0 | 0 | 1, 21, 5, 2, 3, 4, 9, 8, 13, 12, 10, 11, 22, 16, 14, 15, 19, 20, 25, 24, 17, 23, 7, 6 |
Basic
Ismay, C., & Kim , A. Y. (2020). Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. CRC Press / Taylor & Francis Group. Freely available at moderndive.com.
Wickham, H., & Grolemund, G. (2016). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media. Freely available at r4ds.had.co.nz. Spanish version: es.r4ds.hadley.nz.
Complementary
Baumer, B., Kaplan, D., & Horton, N. J. (2021). Modern data science with R (2nd ed). CRC Press. Freely available at mdsr-book.github.io/mdsr2e.
Bolker, E. D., & Mast, M. B. (2020). Common Sense Mathematics (2nd ed). American Mathematical Society.
Broman, K. W., & Woo, K. H. (2018). Data Organization in Spreadsheets. The American Statistician, 72(1), 2–10. doi.org/10.1080/00031305.2017.1375989.
Çetinkaya-Rundel, M., & Hardin, J. (2021). Introduction to Modern Statistics. OpenIntro. Freely available at openintro-ims.netlify.app.
Chang, W. (2018).. R Graphics Cookbook: Practical Recipes for Visualizing Data (2nd ed). O’Reilly Media. Freely available at r-graphics.org.
Dougherty, J., & Ilyankou, I. (2021). Hands-On Data Visualization. O’Reilly Media. Freely available at handsondataviz.org.
Healy, K. (2018). Data visualization: A practical introduction. Princeton University Press. Freely available at socviz.co.
Klass, G. M. (2012). Just Plain Data Analysis: Finding, Presenting, and Interpreting Social Science Data (2nd ed). Rowman & Littlefield.
Mas Elias, Jordi. 2020. Análisis de Datos con R en Estudios Internacionales. Editorial UOC. This book can be accessed via the ARE service: https://login.are.uab.cat/login?url=https://login.are.uab.cat/login?url=https://elibro.net/es/ereader/uab/167261.
Miller, J. E. (2022). Making Sense of Numbers: Quantitative Reasoning for Social Research. Sage.
Sevilla, A. N., & Somers, K. (2013). Quantitative Reasoning: Tools for Today’s Informed Citizen (2nd ed). Wiley.
Wilke, C. (2019). Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O’Reilly Media. Freely available at clauswilke.com/dataviz.