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Course Code: 
PHYS 405
Course Type: 
Area Elective
P: 
2
Lab: 
2
Laboratuvar Saati: 
0
Credits: 
3
ECTS: 
9
Course Language: 
English
Course Coordinator: 
Serhat Iştın
Course Objectives: 
The aim of this course is to teach the basics of data analysis in physical sciences and in particular, to have students develop necessary mathematical and software skills to analyze/report scientific data.
Course Content: 

Review of program and data structures in a structured programming language. Processing large volumes of data with computers and collection of statistics. Measures of central tendency and dispersion. Moment generating functions, Poisson and Bernoulli processes and hypothesis testing. Variance analysis. Least squares, maximum likely hood, and Bayes analysis. Error analysis and propagation. Monte Carlo simulation and its applications. Case studies, laboratory exercises, and projects on the computer, supporting topics covered in lectures

Course Methodology: 
1: Lecture, 2: Question-Answer, 5: Problem Solving, 14: Laboratory ; 15:Homework
Course Evaluation Methods: 
A: Exam, B: Final,C: Homework, I:Laboratory

Vertical Tabs

Course Learning Outcomes

Learning Outcomes

Teaching Methods

Assessment Methods

1) Gets prepared to use computers and to develop necesary tools to perform data analyses.

1,2,5,14,15

A,B,C,I

2) Identifies, formulates and solves  problems regarding  data analysis.

1,2,5,14,15

A,B,C,I

3) Explains the relevant mathematical methods.

1,2,5,14,15

A,B,C,I

4) Learns how to prepare, analyse and visualise data in a publication-ready format.

1,2,5,14,15

A,B,C,I

5) Works out examples.

1,2,5,14,15

 
 
 

Course Flow

COURSE CONTENT

Week

Topics

Study Materials

1

Review of program and data structures in a structured programming language.

 

2

Measures of central tendency and dispersion.

 

3

Random variables. Discrete and continious probabilty distributions

 

4

Moments and moment generating functions.

 

5

Poisson and Bernoulli processes.

 

6

MIDTERM

 

7

Hypothesis tests.

 

8

Least squares, Maximum likelihood methods.

 

9

Linear / non-linear fitting.

 

10

Bayesian Inference of Probabilities.

 

11

Bayesian Analysis.

 

12

Random Number generation and its applications

 

13

Monte Carlo Integration.  Basic Simulations. Random Sampling.

 

14

Error analysis and propagation

 

15

Laboratory exercise

 

16

FINAL

 
 
 

Recommended Sources

RECOMMENDED SOURCES

Textbook

Data Analysis. Statistical and Computational Methods for Scientists and Engineers. Brandt, Siegmund

 

Additional Resources

In class prepared software applications.

Book: Statistical Methods in Experimental Physics. Frederick James (2nd edition)
 
 
 

Material Sharing

MATERIAL SHARING

Documents

 

 

Assignments

Reading formatted data, summary and descriptive statistics, toy simulations, data acquisition and analysis in a lab.

Exams

 
 
 
 

Assessment

ASSESSMENT

IN-TERM STUDIES

NUMBER

PERCENTAGE

Mid-terms

1

30

Laboratory

1

10

Assignment

5

30

Total

 

70

CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE

 

30

CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE

 

70

Total

 

100
 
 
 

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAM

No

Program Learning Outcomes

Contribution

1

2

3

4

5

 

1

gains the ability to apply the knowledge in physics and mathematics

 

 

 

X

 

 

2

gains the ability to construct an experimental setup, perform

the experiment, analyze and interpret the results

 

 

 

X

 

 

3

is supposed to have the education required for the measurements in scientific and technological areas 

 

 

 

 

X

 

4

is able to work in an interdisciplinary team

 

 

 

X

 

 

5

is able to identify, formulate and solve physics problems

 

 

 

x

 

 

6

is conscious for the professional and ethical responsibility

 

 

X

 

 

 

7

is able to communicate actively and effectively

X

 

 

 

 

 

8

is supposed to have the required education for the industrial applications and the social contributions of physics

 

X

 

 

 

 

9

is conscious about the necessity of lifelong education and can implement it

 

 

X

 

 

 

10

is supposed to be aware of the current investigations and developments in the field

X

 

 

 

 

 

11

can make use of the techniques and the modern equipment required for physical applications

 

 

 

X

 

 
 

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities

Quantity

Duration
(Hour)

Total
Workload
(Hour)

Course Duration (Including the exam week: 14x Total course hours)

14

4

56

Hours for off-the-classroom study (Pre-study, practice)

14

10

140

Mid-terms

1

2

2

Lab

1

2

2

Homework

5

4

20

Final examination

1

3

3

Total Work Load

 

 

223

Total Work Load / 25 (h)

 

 

8.92

ECTS Credit of the Course

 

 

9
 
 
 

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