Introduction to Data Analytics
| Title | Εισαγωγή στην Ανάλυση Δεδομένων / Introduction to Data Analytics |
| Code | ΜΙΣΤΑ001 |
| Faculty | Health Sciences |
| School | Medicine |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Anna-Bettina Haidich |
| Common | No |
| Status | Active |
| Course ID | 600020022 |
Programme of Study: PPS Health Statistics and Data Analytics (2020-today)
Registered students: 27
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Compulsory Course | 1 | 1 | 7.5 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors |
|
| Instructors from Other Categories |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600289124
|
Course Type 2021
Skills Development
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600289124
- eLearning: https://elearning.auth.gr/course/view.php?id=15589
Language of Instruction
- Greek (Instruction)
- English (Instruction, Examination)
Learning Outcomes
Knowledge
Upon successfully completing this course, 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
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
Course Content (Syllabus)
1. Search and access to data sources related to health
2. The fundamentals of relational databases through the use of Structured Query Language (SQL)
3. The basics of programming in R
4. Data wrangling and exploration, and code recipes for doing data analytics with R
5. Data visualization: publication ready static plots, interactive plots and animation of data
6. Research workflow and reproducible methods in data analytics
Keywords
Data Analysis, Data Management, Database Access, Programming with R, SQL
Educational Material Types
- Notes
- Slide presentations
- Video lectures
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 45 | ✓ | |||
| Laboratory Work | 60 | ✓ | |||
| Reading Assigment | 20 | ✓ | |||
| Exams | 65 | ✓ | |||
| Total | 190 |
Student Assessment
Description
Weekly quizes, with multiple choice questions
Assessment based on comments submitted by each student in online discussion for a
Final exam with multiple choice questions
Student Assessment methods
- Written Exam with Multiple Choice Questions (Formative, Summative)
- Written Exam with Problem Solving (Formative, Summative)
- Labortatory Assignment (Formative, Summative)
Bibliography
Additional bibliography for study
• 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.
Last Update
14-01-2023
