Course outline
Faculty | Health Sciences | ||
Department | Medicine | ||
Education level | Postgraduate / Master of Science | ||
Course code | A1 | Semester | 1 |
Course title | Introduction to Data Analytics | ||
Independent teaching activities | Hours per week | ECTS | |
Lectures | 2 | ||
Practice | 6 | ||
Total | 8 | 7,5 | |
Coursetype | General setting course, skills development | ||
Prerequisite courses | None | ||
Teaching and assessment language | English |
Learning outcomes
Objective
Upon completion of the course, students will be able to understand the different data sources available to support decision-making; understand which techniques and methodologies are most appropriate to investigate data; experience key technical skills and software for working with and manipulating biomedical datasets in a reproducible way; and know how to communicate and visualize results and ideas to various stakeholders (often from a non-technical background).
Knowledge
Upon completion of the course, graduate students will be familiar with:
- The search and access of biomedical data from different sources
- The basic commands of Structured Query Language (SQL)
- The import of data into R from different file formats
- The manipulation and exploration of health and biological data
- The selection of graphical elements for effective presentation of data and the critical evaluation of existing data visualizations
- The principles of the reproducible research
Capacities
The course participants upon completion will be able to:
- Identify and appraise data sources used to support healthcare decision making
- Experience key technical skills and software for working with and manipulating biomedical datasets
- Design, produce and communicate data visualizations
- Follow reproducible methods in data analytics
Course contents
During the course, students will work on their own research question, and will experience the process of review at all stages. So they will learn how to:
- Search and access to data sources related to health
- The fundamentals of relational databases through the use of Structured Query Language (SQL)
- The basics of programming in R
- Data wrangling and exploration, and code recipes for doing data analytics with R
- Data visualization: publication ready static plots, interactive plots and animation of data
- Research workflow and reproducible methods in data analytics
Teaching methods | Face to face Distance learning | |
Use of information and communication technologies (ICT) | Use of ICT in Teaching- Moodle Virtual learning environment (VLE) (asynchronous learning, wikis, Online Discussion Fora, Educational Portfolio, assignment submission, assessment process) | |
Use of ICT in Communication with students (email, instant messaging via Moodle) | ||
Module structure | Work Hours per Semester | Activity |
Lectures | 55 | |
Exercises (Quiz) | 10 | |
Exercises (Wikis) | 10 | |
Exercises (Online discussion fora) | 20 | |
Exercises (Study relevant papers) | 20 | |
Essay background work | 40 | |
Essay writing | 45 | |
Overall work for the course | 200 | |
Assessment Methods | Written assignment, in English, approximately 2,500 words long, to be submitted by each student at the end of the course | |
Assessment of knowledge at the beginning and the end of the course with short-answer questions and essays development | ||
Weekly quizes, with multiple choice questions | ||
Assessment based on comments submitted by each student in online discussion fora |
Recommended Bibliography
- DeBarros, A. (2018). Practical SQL: A Beginner’s Guide to Storytelling with Data. No Starch Press.
- Taylor, A. G. (2018). SQL For Dummies (For Dummies (Computer/Tech)) (9th ed.). For Dummies.
- Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (1st ed.). O’Reilly Media.
- Ismay, C., & Kim, A. Y. (2019). Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Chapman & Hall/CRC The R Series) (1st ed.). Chapman and Hall/CRC.
- Kabacoff, R. (2015). R in Action, Second Edition: Data analysis and graphics with R. Manning Publications.
- Wilke, C. O. (2019). Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures (1st ed.). O’Reilly Media.
- Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis (Use R!) (2nd ed. 2016 ed.). Springer.
- Chang, W. (2018). R Graphics Cookbook: Practical Recipes for Visualizing Data (2nd ed.). O’Reilly Media.
- Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R Markdown (Chapman & Hall/CRC The R Series) (1st ed.). Routledge.