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
ELECTIVE

Offered By

Physics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

DOCTOR MEHMET TARAKÇI

Offered to

Physics

Course Objective

Learn how to do experimental data analysis, learn the concept of experimental error and how to do fitting to the distributions to measure the physical quantities, learn data analysis programs and techniques used in Physics in general.

Learning Outcomes of the Course Unit

1   Understanding the measurement process and defining the uncertainties in measurement
2   Learning data analysis techniques
3   Learning the Python programming language and using it for data analysis
4   Learning the importance of visualizing experimental data
5   Learning to interpret experimental data

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 SciPy and Numpy libraries
6 Chi-square distribution and test Python - the use of matplotlib graphics library - the importance of graphics in data analysis
7 I. Midterm Exam
8 Regression (curve fitting) - linear - least squares method Python - Introduction and use of Pandas libraries
9 Regression (curve fitting) - nonlinear 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

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.12555
LO.22555
LO.32555
LO.45555
LO.55555

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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