| Course Type | Elective Course |
|---|
| Course Level | Master's Degree With Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 9 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Learn the methods and techniques of big data analytics.
- Learn the skills to apply big data analytics methods and techniques on psychological data.
- Evaluate ethical and privacy issues in analyzing psychological data.
- Conduct independent research using big data analysis tools for psychological research.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | This course aims to examine the use of big data analytics methods in clinical psychology. The course aims to introduce students to big data concepts, data collection and processing techniques, and advanced analytical tools necessary for the analysis of psychological data. By addressing the ethical and practical aspects of using big data in psychology, it enables students to gain competence in analyzing different types of data, from psychological test data to biometric data, genetic and social media data. This course aims to provide students with data analytics skills in clinical psychology practice and to enable them to effectively use analytical approaches related to big data in psychological assessment, treatment processes and research. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction and Basic Concepts |
|
2nd Week |
Data Types and Sources in Psychology |
|
3rd Week |
Statistical Foundations and Data Analysis Methods |
|
4th Week |
Data Cleaning and Preprocessing |
|
5th Week |
Data Visualization |
|
6th Week |
Machine Learning Fundamentals |
|
7th Week |
Deep Learning and Psychological Data |
|
8th Week |
Data Analytics Applications in Clinical Psychology |
|
9th Week |
Ethics and Privacy |
|
10th Week |
Analysis of Psycho-social Data |
|
11th Week |
Data Analytics in Psychological Testing |
|
12th Week |
Genetic and Biometric Data Analysis |
|
13th Week |
Clinical Data Analytics Project |
|
14th Week |
Final Presentations |
|
15th Week |
General Evaluation |
|
| Recommended or Required Reading | Woo, S. E. E., Tay, L. E., & Proctor, R. W. (2020). Big data in psychological research. American Psychological Association.
Bahga, A., & Madisetti, V. (2016). Big data science & analytics: A hands-on approach. |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Discussion
- Questions/Answers
- Other
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Project |
1 |
60 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
|
|
|
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
10 |
140 |
| Preparation for Final exam |
1 |
30 |
30 |
| Course hours |
15 |
3 |
45 |
| Preparation for Midterm exam |
|
|
|
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
3 |
3 |
| Homework |
|
|
|
| Project |
1 |
50 |
50 |
| Total Work Load | 268 |
| Total Work Load / 30 (h) | 8.93 |
| ECTS Credit of the Course | 9 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
