PSYCHOLOGY (MASTER) (THESIS)
Qualification Awarded	Length of Program	Total Credits (ECTS)	Mode of Study	Level of Qualification & Field of Study
Master's ( Second Cycle) Degree	2	120	FULL TIME	TYÇ, TR-NQF-HE, EQF-LLL, ISCED (2011):Level 7 QF-EHEA:Second Cycle TR-NQF-HE, ISCED (1997-2013): 31

Ders Genel Tanıtım Bilgileri

Course Code:

3020002004

Ders İsmi:

Introduction to Programming in Psychology

Ders Yarıyılı:

Spring

Ders Kredileri:

Theoretical	Practical	Labs	Credit	ECTS
3	0	0	3	6

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Other Recommended Topics for the Course:

yok

Type of course:

Anabilim Dalı/Lisansüstü Seçmeli

Course Level:

Master

TR-NQF-HE:7. Master`s Degree

QF-EHEA:Second Cycle

EQF-LLL:7. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Prof. Dr. Telat Gül ŞENDİL

Course Lecturer(s):

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:

This course aims to introduce psychology students to the basic concepts of programming and to show how programming can be used in psychology research. Students will gain basic coding skills, especially understanding the role of programming in areas such as data analysis, design of experiments and cognitive modeling.

Course Content:

Introduction to Programming: Basic programming concepts and the importance of programming in psychology
Python Basics: Variables, data types, loops, conditional statements
Python for Data Analysis: NumPy, Pandas and data manipulation
Using Programming in Psychology Research: Experiment design and data collection
Coding Psychological Experiments: Experiment design with tools like PsychoPy and OpenSesame
Programming Visual and Auditory Stimuli: Graphics and sound processing
Basic Statistical Analysis: Using Python for data analysis and hypothesis testing
Machine Learning and Psychology: Basic machine learning concepts and applications with psychological data sets
Sentiment Analysis and Natural Language Processing: Sentiment analysis on psychological data sets
Course Projects and Practices: Small projects where students can apply what they have learned

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Understands the Basics of Programming: Explains and applies the basic components of the Python programming language.
2 - Skills
Cognitive - Practical
1) Performs Data Analysis: Uses basic Python libraries (Pandas, NumPy, Matplotlib) to analyze data sets used in psychology research.
2) Apply Statistical Analyses: Performs basic statistical tests (t-test, ANOVA, correlation, etc.) in psychology research using Python.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
1) Works with Cognitive Modeling: Applies programming knowledge by modeling experiments and cognitive processes specific to psychology.
Competence to Work Independently and Take Responsibility
1) Designs and Implements Experiments: Can code and implement psychology experiments with programs such as PsychoPy or OpenSesame.

Ders Akış Planı

Week	Subject	Related Preparation
2)	Python Basics: Variables, data types, loops, conditional statements	VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Fawcett, T. (2016). Data Science for Business. O'Reilly Media.
3)	Python for Data Analysis: NumPy, Pandas and data manipulation	VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Fawcett, T. (2016). Data Science for Business. O'Reilly Media.
4)	Using Programming in Psychology Research: Experiment design and data collection	VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Fawcett, T. (2016). Data Science for Business. O'Reilly Media.
5)	Coding Psychological Experiments: Experiment design with tools like PsychoPy and OpenSesame	VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Fawcett, T. (2016). Data Science for Business. O'Reilly Media.
6)	Programming Visual and Auditory Stimuli: Graphics and sound processing	VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Fawcett, T. (2016). Data Science for Business. O'Reilly Media.
7)	Basic Statistical Analysis: Using Python for data analysis and hypothesis testing	VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Fawcett, T. (2016). Data Science for Business. O'Reilly Media.
8)	Machine Learning and Psychology: Basic machine learning concepts and applications with psychological data sets	VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Fawcett, T. (2016). Data Science for Business. O'Reilly Media.
9)	Sentiment Analysis and Natural Language Processing: Sentiment analysis on psychological data sets	VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Fawcett, T. (2016). Data Science for Business. O'Reilly Media.
10)	Course Projects and Practices: Small projects where students can apply what they have learned	VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Fawcett, T. (2016). Data Science for Business. O'Reilly Media.
10)	Course Projects and Practices: Small projects where students can apply what they have learned	VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Fawcett, T. (2016). Data Science for Business. O'Reilly Media.

Sources

Course Notes / Textbooks:	VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Fawcett, T. (2016). Data Science for Business. O'Reilly Media.
References:	VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media. Grus, J. (2019). Data Science from Scratch: First Principles with Python. O'Reilly Media. Fawcett, T. (2016). Data Science for Business. O'Reilly Media.

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	4	3	5
Program Outcomes
1) Students will have advanced theoretical knowledge in the fields of developmental and experimental psychology and will consolidate this knowledge through scientific research and professional practice.
2) The program provides students with the skills to conduct independent research, use scientific data analysis techniques and produce academic publications.
3) Graduates gain the competence to work responsibly in line with the ethical rules and professional standards in the field of psychology.
4) Graduates will be able to take an active role in multidisciplinary projects by collaborating with professionals in different fields related to psychology such as health, education and business.
5) The program trains graduates as experts who contribute to society by raising awareness of taking part in projects for the protection and improvement of psychological health at the individual and community level.

Ders - Öğrenme Kazanımı İlişkisi

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Students will have advanced theoretical knowledge in the fields of developmental and experimental psychology and will consolidate this knowledge through scientific research and professional practice.
2)	The program provides students with the skills to conduct independent research, use scientific data analysis techniques and produce academic publications.
3)	Graduates gain the competence to work responsibly in line with the ethical rules and professional standards in the field of psychology.
4)	Graduates will be able to take an active role in multidisciplinary projects by collaborating with professionals in different fields related to psychology such as health, education and business.
5)	The program trains graduates as experts who contribute to society by raising awareness of taking part in projects for the protection and improvement of psychological health at the individual and community level.

Öğrenme Etkinliği ve Öğretme Yöntemleri

Individual study and assignment
Lesson
Lab
Homework
Application (Modeling, Design, Prototypes, Simulation, Experimentation, etc.)

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Written Exam (Open-ended questions, multiple choice, true/false, matching, fill-in-the-blanks, sequencing)
Oral examination
Homework
Practical

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 40
Semester Final Exam	1	% 40
Quiz	1	% 20
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	16	6	96
Laboratory	2	1	2
Application	2	5	10
Study Hours Out of Class	2	6	12
Presentations / Seminar	2	5	10
Project	2	6	12
Homework Assignments	2	1	2
Quizzes	2	5	10
Midterms	1	3	3
Paper Submission	2	2	4
Final	1	6	6
Total Workload			167