Computing programming for health care professionals
| Title | Προγραμματισμός Υπολογιστών για επαγγελματίες Υγείας / Computing programming for health care professionals |
| Code | MEI001 |
| Faculty | Health Sciences |
| School | Medicine |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Vasileia Paschaloudi |
| Common | No |
| Status | Active |
| Course ID | 600021903 |
Programme of Study: PMS "Iatrikī Mīchanikī kai Plīroforikī" (2022-sīmera)
Registered students: 2
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the selected specialization | 1 | 1 | 10 |
| Academic Year | 2024 – 2025 |
| Class Period | Winter |
| Faculty Instructors |
|
| Instructors from Other Categories |
|
| Weekly Hours | 4 |
| Total Hours | 52 |
| Class ID | 600265087
|
Course Type 2021
Specific Foundation
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600265087
- eLearning: https://elearning.auth.gr/course/view.php?id=17075
Language of Instruction
- English (Instruction, Examination)
Learning Outcomes
1 Students by the end of the course should have understood basic programming concepts that apply to not only R, but also to other programming languages.
2 Students should have familiarity with foundational statistical values and concepts. Students should be able to understand the meaning of statistical words like variance, standard deviation, p-value and compute them by using R.
3 They should be able to read and interpret graphs and formulate their own meaningful graphs from available data
4 They could perform basic statistical analysis.
5 They could experiment on forming a machine learning model, using medical data.
6 Students could be able to use Matlab to handle sets of data, images and signals (i.e. apply filter transformation).
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Work autonomously
- Advance free, creative and causative thinking
Course Content (Syllabus)
Basic concepts of programming. Introduction to R programming (data types, operations, functions)
Dealing with data in R (importing, tidying, transforming, viewing, filtering, summarizing )
Data visualization using R (making meaningful, legible plots from data)
Exploratory Data Analysis (EDA).Extracting statistics from data using R (descriptive statistics: mean, standard deviation, variance)
Methods used for Hypothesis testing in R. Statstical tests (t-test, Wilcoxon test)
Predictive analytics: using data to build a model that predicts an output in R
Analysis and modeling. Linear Regression and Nonlinear Regression using R-examples
Creating a machine learning model in R - Work flow - Example
Introduction to programming in Matlab
Programming for Medicine using Matlab(handling data: types of data, reading, transforming, plotting)
Signal handling in Matlab(filtering, transformations, display functions)
Image processing using Matlab(filters, transformations, Region of Interest). Dealing with DICOM files in Matlab
Keywords
Data transformation, Data summarization, Data visualization, Exploratory Data Analysis, Statstical tests, regression analysis, machine learning modelσ, Signal handling in Matlab, , DICOM files in Matlab
Educational Material Types
- Notes
- Slide presentations
- 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
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 156 | 6.2 | ✓ | ||
| Laboratory Work | 44 | 1.8 | ✓ | ||
| Project | 50 | 2 | ✓ | ||
| Total | 250 | 10 |
Student Assessment
Description
20% of the grade is earned from a test at the end of each lesson while 80% of the grade is from a project presented in class. The project deals with the entire content of the lectures.
Student Assessment methods
- Written Exam with Multiple Choice Questions (Formative)
- Labortatory Assignment (Formative)
Bibliography
Course Bibliography (Eudoxus)
R Data Science Quick Reference [electronic resource], 91693730
Guide to Programming and Algorithms Using R [electronic resource], 73240114
Additional bibliography for study
Beginning Data Science in R Data Analysis, Visualization, and Modelling for the Data Scientist ( PDFDrive )
ggplot2 [electronic resource] 978-3-319-24277-4
Last Update
08-12-2023
