Data Analysis

Course Information
TitleΑνάλυση Δεδομένων / Data Analysis
Code044
FacultyEngineering
SchoolElectrical and Computer Engineering
Cycle / Level1st / Undergraduate
Teaching PeriodWinter
CoordinatorDimitris Kugiumtzis
CommonNo
StatusActive
Course ID600000993

Programme of Study: Electrical and Computer Engineering

Registered students: 44
OrientationAttendance TypeSemesterYearECTS
ELECTRICAL ENERGYElective Courses744
ELECTRONICS AND COMPUTER ENGINEERINGElective Courses744
TELECOMMUNICATIONSElective Courses744

Class Information
Academic Year2019 – 2020
Class PeriodWinter
Faculty Instructors
Class ID
600144668
Course Type 2016-2020
  • Scientific Area
Course Type 2011-2015
General Foundation
Mode of Delivery
  • Face to face
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
Prerequisites
Required Courses
  • 020 Probability Theory and Statistics
Learning Outcomes
1. Understanding the significance of data analysis in engineering science. 2. Acquaintance with the terms of probability and statistics. 3. Understanding the concepts in the fields of measurement errors, correlation and regression. 4. Acquaintance with computational approaches for the solution of problems in data analysis. 5. Ability of analyzing real data in the computer.
General Competences
  • Apply knowledge in practice
  • Make decisions
  • Work autonomously
Course Content (Syllabus)
Introduction: definitions, data, examples. Elements of probability and statistics: random variables, univariate and multivariate distributions, distribution parameters, estimation and hypothesis tests for parameters, hypothesis test of distribution goodness of fit. Uncertainty and measurement error: systematic and random errors. Correlation and regression: correlation, simple and multiple regression, linear and nonlinear regression. Computational (resampling) methods in parameter estimation, statistical tests, correlation and regression.
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
Description
Επίλυση παραδειγμάτων/ασκήσεων, εξάσκηση στο Matlab. Exercise deposition and communication through elearning.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures35.51.2
Laboratory Work19.50.7
Tutorial351.2
Exams301
Total1204
Student Assessment
Description
1. Examination on Matlab of 180 minutes. 2. Project in Matlab. 3. Optional assignments of exercises on Matlab during the course
Student Assessment methods
  • Performance / Staging (Summative)
  • Written Exam with Problem Solving (Summative)
  • Labortatory Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Εφαρμοσμένη Στατιστική, Μπόρα-Σέντα Ε. και Μωυσιάδης Χ., Εκδόσεις Ζήτη, Θεσσαλονίκη 1997 (Εύδοξος: 11028) Resampling Methods: A Practical Guide to Data Analysis, Good P.I., Springer, 2006 (Εύδοξος: 173198) Data Analysis Using the Method of Least Squares: Extracting the Most Information from Experiments, Wolberg J., Springer, 2006 (Εύδοξος: 174465)
Additional bibliography for study
Computational Statistics Handbook with MATLAB}, Martinez W.L. and Martinez A.R., Chapman and Hall, 3rd edition 2015 Exploratory Data Analysis with MATLAB}, Martinez W.L., Martinez A.R. and Solka J., Chapman and Hall, 3rd edition 2017 Making Sense of Data, A Practical Guide to Exploratory Data Analysis and Data Mining, Myatt G.J., Wiley-Interscience, 2nd edition, 2014 Statistical Techniques for Data Analysis, Taylor J.K. and Cihon C., Chapman and Hall, 2004 Hyperstat, βιβλίο στο διαδίκτυο (online Book): http://davidmlane.com/hyperstat/ Concepts and Applications of Inferential Statistics, Lowry R., βιβλίο στο διαδίκτυο (online book): http://vassarstats.net/textbook
Last Update
20-11-2019