ANALYSIS AND DATA PROCESSING

Course Information
TitleΑΝΑΛΥΣΗ ΚΑΙ ΕΠΕΞΕΡΓΑΣΙΑ ΔΕΔΟΜΕΝΩΝ / ANALYSIS AND DATA PROCESSING
CodeΜΥΠ751
FacultySciences
SchoolPhysics
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter
CommonNo
StatusActive
Course ID40000209

Programme of Study: PPS Computional Physics (2014-today)

Registered students: 8
OrientationAttendance TypeSemesterYearECTS
KORMOSCompulsory Course117.5

Class Information
Academic Year2018 – 2019
Class PeriodWinter
Faculty Instructors
Weekly Hours3
Class ID
600133688
Type of the Course
  • Scientific Area
Course Category
Specific Foundation / Core
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)
Learning Outcomes
The scope of the course is the introduction of concepts and methods of data analysis, as well as their application to real-world problems. Within the framework of application the scope is the use of relevant software.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Work autonomously
Course Content (Syllabus)
Introduction: definitions, data, examples. Probability and random variables: fundamentals on probability, distributions, parameters of distributions, basic distributions. Elements of statistics: parameter estimation and hypothesis testing. Uncertainty and measurement error: systematic and random errors, error propagation. Correlation and regression: correlation, simple and multiple regression, linear and nonlinear regression. Time series: basic characteristics of time series, correlation in time series.
Keywords
data analysis, statistics, probability, time series
Educational Material Types
  • Notes
  • Slide presentations
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
Description
A practical lab on the computational environment Matlab.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures180.6
Laboratory Work180.6
Total361.2
Student Assessment
Description
Lab exam (the computational environment Matlab): 50% of the final mark Project on the basis of the computational environment Matlab: 50% of the final mark
Student Assessment methods
  • Labortatory Assignment (Formative, Summative)
  • Exam on computer
Bibliography
Course Bibliography (Eudoxus)
1. Σημειώσεις "Ανάλυση δεδομένων", Δ. Κουγιουμτζής, 2012 (δες http://users.auth.gr/dkugiu/Teach/DataAnalysis/index.html)
Additional bibliography for study
1. Εφαρμοσμένη Στατιστική, Μπόρα-Σέντα Ε. και Μωυσιάδης Χ., Εκδόσεις Ζήτη, Θεσσαλονίκη 1997 2. Computational Statistics Handbook with MATLAB, Martinez W.L. and Martinez A.R., Chapman and Hall, 2002 3. Exploratory Data Analysis with MATLAB, Martinez W.L. and Martinez A.R., Chapman and Hall, 2005 4. Statistical Techniques for Data Analysis, Taylor J.K. and Cihon C., Chapman and Hall, 2004 5. Making Sense of Data, A Practical Guide to Exploratory Data Analysis and Data Mining, Myatt G.J., Wiley-Interscience, 2007 6. Time Series Analysis, Forecasting and Control, Box G.E.P., Jenkins G.M. and Reinsel G.C., Prentice Hall, 1994 7. Hyperstat, βιβλίο στο διαδίκτυο (online Book): http://davidmlane.com/hyperstat/ 8. Concepts and Applications of Inferential Statistics, Lowry R., βιβλίο στο διαδίκτυο (online book): http://faculty.vassar.edu/lowry/webtext.html
Last Update
02-09-2013