DATA ANALYSIS WITH R
| Title | ΑΝΑΛΥΣΗ ΔΕΔΟΜΕΝΩΝ ΜΕ ΤΗΝ R / DATA ANALYSIS WITH R |
| Code | ΕΔΥ02 |
| Faculty | Social and Economic Sciences |
| School | Economics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Emmanouil Trachanas |
| Common | No |
| Status | Active |
| Course ID | 600026516 |
Programme of Study: Data Science in Economics and Finance
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Compulsory Course | 1 | 1 | 6 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors | |
| Instructors from Other Categories | |
| Class ID | 600273576
|
Course Type 2021
Specific Foundation
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600273576
Language of Instruction
- Greek (Instruction, Examination)
Learning Outcomes
Upon completion of the course, students are expected to be able to: 1) Write code to import data from other statistical packages, files, and other programs, having seen how the basic structure of R works. 2) Manage data and produce graphs. 3) Analyze data and calculate statistical measures. 4) Apply statistical methods to samples.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
Course Content (Syllabus)
Introduction to R, Data Entry, Data Visualization, Exploratory Data Analysis, Statistical Inference, Correlation and Regression, Introduction to Machine Learning
Educational Material Types
- Slide presentations
- Book
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 | 39 | ✓ | |||
| Reading Assigment | 81 | ✓ | |||
| Written assigments | 60 | ✓ | |||
| Total | 180 |
Student Assessment
Student Assessment methods
- Written Assignment (Formative)
- Oral Exams (Formative)
- Labortatory Assignment (Formative)
Bibliography
Additional bibliography for study
Arnold, M. C., & Hanck, C. (n.d.). Introduction to econometrics with R.
Bruce, P., Bruce, A., & Gedeck, P. (2020). Practical statistics for data scientists: 50+ essential concepts using R and Python (2nd ed.). O’Reilly Media.
Consoli, S., Reforgiato Recupero, D., & Saisana, M. (Eds.). (2021). Data science for economics and finance: Methodologies and applications. Springer.
Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O'Reilly Media.
Ripio, R. L. (n.d.). R for economic research.
Last Update
04-09-2025
