COURSE UNIT TITLE

: DATA ANALYSIS IN PHYSICS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
FIZ 3120 DATA ANALYSIS IN PHYSICS ELECTIVE 2 2 0 7

Offered By

Physics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

DOCTOR MEHMET TARAKÇI

Offered to

Physics

Course Objective

It is aimed to illustrate of experimental results and understanding of uncertainties in measurement, analyzing the experimental results, using some important statistical distributions, demonstrate data analysis techniques using python programming language and libraries.

Learning Outcomes of the Course Unit

1   Defines the basic concepts of the measurement process.
2   Calculates the uncertainties in a measurement process.
3   Interpret the measurement results.
4   Uses Python programming language and libraries for data analysis.
5   Shows the experimental data in a convenient and understandable way.
6   Interpret the experimental results by making the necessary visualizations.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Measurement and error Introduction to Python - Why Python Installing Anaconda environment and using.
2 Showing of measurement results and its uncertainties Basic operators and using in Python
3 Some basic Probability distributions Using libraries in Python
4 Error analysis and error propagation Introduction and use of Python basic programming steps Cycles, conditions ...
5 Visualization of data and interpretation of graphics Python - Introduction and use of Numpy libraries
6 Regression (curve fitting) - linear - least squares method Python - the use of matplotlib graphics library - the importance of graphics in data analysis
7 I. Midterm Exam
8 Regression (curve fitting) - nonlinear Python - Introduction and use of Pandas libraries
9 Chi-square distribution and test Nonlinear curve fitting applications in the Python environment
10 Interpolation Python - Interpolation
11 Extrapolation Python Extrapolation
12 Introducing advanced methods used in data analysis Object oriented programming in Python
13 Introducing advanced methods used in data analysis General examples in Python
14 Presentation of project and its evaluation

Recomended or Required Reading

Ana kaynak
1. Taylor J.R., An introduction to error analysis, 2ed, Universty Science Books, Califorrnia.
2. Özgül F., Python 3 için Türkçe Kılavuz, 2016.

Yardımcı kaynaklar:
1. Gerhard Bohm, Günter Zech (2010), Introduction to Statistics and Data Analysis for Physicists, Wiley, New York.
2. Philip Bevington, D. Keith Robinson 1980, Data Reduction and Error Analysis for the Physical Sciences 3rd Edition, McGraw-Hill Higher Education.
2. Les Kirkup (2002). Data Analysis with Excel: An Introduction for Physical Scientist, Cambridge University Press, London.
3. Isa Eşme (1993), Fiziksel Ölçmeler ve Değerlendirilmesi, Marmara Üniversitesi Yayınları.

https://www.scipy.org

Planned Learning Activities and Teaching Methods

1. Method of Expression
2. Question & Answer Techniques
3. Discussion
4. Homework
5. Project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 PRJ PROJECT
4 FIN FINAL EXAM
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.05 + PRJ * 0.15 + FIN * 0.50
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.05 + PRJ * 0.15 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

1. Midterm exams and assignments are taken as the achievements of students for the semester.
2. Final exam will be added to the success of the study of midterms and assignments, thereby the student's success will be determined

Language of Instruction

Turkish

Course Policies and Rules

1. 70% of the participation of classes is mandatory.
2. Students, who do not participate in Midterm exams and not do regular assignments, are not allowed to enter the final exam.
3. Every trial of cheating will be punished according to disciplinary proceedings.
4. Faculty reserves the right to make practical exam. This exam will be taken from the notes will be added to the midterm and final exam grades.

Contact Details for the Lecturer(s)

mehmet.tarakci@deu.edu.tr

Office Hours

Students will be informed at the beginning of the term.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Tutorials 14 2 28
Preparations before/after weekly lectures 13 4 52
Preparation for midterm exam 1 8 8
Preparing assignments 1 4 4
Project Preparation 1 16 16
Preparations before/after weekly lectures 1 10 10
Midterm 1 8 8
Project Assignment 1 4 4
Final 1 8 8
TOTAL WORKLOAD (hours) 166

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.121551511111111
LO.221551511111111
LO.321551511111111
LO.455551111111111
LO.555551111111111
LO.655551111111111

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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