Data Statistical Analysis
| Title | Στατιστική Ανάλυση Δεδομένων / Data Statistical Analysis |
| Code | IHST105 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Eleftherios Angelis |
| Common | No |
| Status | Active |
| Course ID | 600018374 |
Programme of Study: PMS TECΗNOLOGIES DIADRASTIKŌN SYSTĪMATŌN (2018 éōs sīmera) PF
Registered students: 16
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses | 1 | 1 | 7.5 |
| Academic Year | 2020 – 2021 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Class ID | 600176635
|
Course Type 2021
General Foundation
Course Type 2016-2020
- Background
- General Knowledge
- Skills Development
Course Type 2011-2015
General Foundation
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600176635
- eLearning:
- eLearning (Moodle):
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Prerequisites
General Prerequisites
Fundamentals of Probabilities and Statistics
Learning Outcomes
Cognitive: Students learn what is research, where and how it is done, basic principles, how research is conducted with data (collection, analysis and interpretation), how research is done with experiments, simulations and systems design while a part of the course is related to research based in logic.
Skills: Upon completion of the course the student learns various aspects of scientific research so that when he/she needs to solve problems to plan the research from the beginning to the end.
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
- Work in teams
- Work in an international context
- Work in an interdisciplinary team
- Generate new research ideas
- Design and manage projects
- Appreciate diversity and multiculturality
- Respect natural environment
- Demonstrate social, professional and ethical commitment and sensitivity to gender issues
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Statistical methods in data analysis using the R language: Descriptive statistics and graphical representation of data. Discrete and continuous distributions, random numbers generators and distributions. Statistical inference with parametric and non-parametric methods. Hypothesis tests and confidence intervals by resampling methods. Regression models (linear regression and generalized models for continuous, binary, categorical, count dependent variables and mixed independent variables). Non-parametric regression. Multivariate analysis: Factor analysis, cluster analysis and correlation analysis.
Keywords
Research, Data, Statistics, Experiments, Simulation, Logic
Educational Material Types
- Notes
- Slide presentations
- Interactive excersises
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | |||
| Project | 92 | ✓ | |||
| Written assigments | 91 | ✓ | |||
| Exams | 3 | ✓ | |||
| Total | 225 |
Student Assessment
Student Assessment methods
- Written Exam with Extended Answer Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
- Performance / Staging (Formative, Summative)
- Written Exam with Problem Solving (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
ΕΙΣΑΓΩΓΗ ΣΤΗ ΣΤΑΤΙΣΤΙΚΗ ΑΝΑΛΥΣΗ ΜΕ ΤΟ R
CRAWLEY J. MICHAEL
Additional bibliography for study
Free tutorials for R
Last Update
23-01-2024
