# Data Analysis

 Title Ανάλυση Δεδομένων / Data Analysis Code 044 Faculty Engineering School Electrical and Computer Engineering Cycle / Level 1st / Undergraduate Teaching Period Winter Coordinator Dimitris Kugiumtzis Common No Status Active Course ID 600000993

### Programme of Study: Electrical and Computer Engineering

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

 Academic Year 2019 – 2020 Class Period Winter Faculty Instructors Class ID 600144668

### Class Schedule

 Building Πολυτεχνείο - πτέρυγα Γ (ΤΗΜΜΥ & Τοπογράφων Μηχ.) Floor Όροφος 1 Hall Α4 (5) Calendar Τρίτη 09:00 έως 12:00
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
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)