Data Statistical Analysis

Course Information
TitleΣτατιστική Ανάλυση Δεδομένων / Data Statistical Analysis
CodeIHST105
FacultySciences
SchoolInformatics
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter
CoordinatorEleftherios Angelis
CommonNo
StatusActive
Course ID600018374

Programme of Study: PMS TECΗNOLOGIES DIADRASTIKŌN SYSTĪMATŌN (2018 éōs sīmera) MF

Registered students: 1
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses117.5

Programme of Study: PMS TECΗNOLOGIES DIADRASTIKŌN SYSTĪMATŌN (2018 éōs sīmera) PF

Registered students: 5
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses117.5

Class Information
Academic Year2021 – 2022
Class PeriodWinter
Faculty Instructors
Weekly Hours3
Class ID
600200589
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
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
Data analysis with statistical methodologies using the statistical language R
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
Statistical analysis, R language
Educational Material Types
  • Notes
  • Slide presentations
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
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures39
Laboratory Work
Project13
Written assigments
Total52
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)
Last Update
12-10-2021