BIOSTATISTICS
| Title | ΒΙΟΣΤΑΤΙΣΤΙΚΗ / BIOSTATISTICS |
| Code | APBC.01.02 |
| Faculty | Sciences |
| School | Biology |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Athanasios Kallimanis |
| Common | No |
| Status | Active |
| Course ID | 600022121 |
Programme of Study: DIPMS EFARMOSMENĪ VIOPLĪROFORIKĪ 2022
Registered students: 30
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Compulsory Course | 1 | 1 | 6 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors | |
| Weekly Hours | 11 |
| Total Hours | 32 |
| Class ID | 600289740
|
Course Type 2021
Specific Foundation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600289740
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=17264
Language of Instruction
- English (Instruction, Examination)
Learning Outcomes
Upon successful completion of the course, students will:
1) become familiar with the principles and steps of statistical inference
2) be able to perform analyses in the SPSS program
3) be able to perform advanced statistical analyses in the R environment
4) be able to carry out model comparison and selection
5) become familiar with machine learning models with or without supervision in the python environment.
General Competences
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Work in an interdisciplinary team
Course Content (Syllabus)
The contents of the course include:
1) introduction to the basic concepts of statistics, distributions and inference; 2) hands-on statistical analysis in SPSS; 3) learning basic and advanced approaches to statistical analysis in the R environment on different types of data (basic linear model analysis, generalized linear models, and generalized linear mixed-effects models), 4) resampling methods, 5) comparison and selection between models, 6) basic principles of Bayesian statistics and examples, 7) introduction to supervised and unsupervised machine learning models and examples in the python environment.
Educational Material Types
- Slide presentations
- Video lectures
- Interactive excersises
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 | 96 | 3.8 | |||
| Laboratory Work | 4 | 0.2 | |||
| Interactive Teaching in Information Center | 3 | 0.1 | |||
| Written assigments | 24 | 1.0 | |||
| Exams | 23 | 0.9 | |||
| Total | 150 | 6 |
Student Assessment
Student Assessment methods
- Written Assignment (Summative)
- Oral Exams (Summative)
Last Update
28-01-2024
