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) 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

 Academic Year 2021 – 2022 Class Period Winter Faculty Instructors Eleftherios Angelis 39hrs Weekly Hours 3 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
• 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