Information Package / Course Catalogue
Data Science For Social Scientists
Course Code: BFN527
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 5
Objectives of the Course

The aim of this course is to provide graduate students conducting research in social sciences with basic knowledge and skills about data science tools and approaches. Students are expected to effectively manage the processes of data collection, analysis and visualization.

Course Content

This course aims to introduce the basic concepts and applications of data science to graduate students working in the field of social sciences. The course covers the processes of data collection, cleaning, analysis and visualization. Applied data analysis is performed using open source software such as Python and/or R. We will work with data types commonly encountered in the social sciences, such as survey data, text data, and social network data. In addition, the contribution of computational methods to social scientific research is discussed along with ethical and open data debates.

Name of Lecturer(s)
Learning Outcomes
1.Explains data science concepts in a social scientific context.
2.Select and prepare appropriate data sources.
3.Produces results using basic data analysis techniques.
4.Present data using visualization tools
5.Performs small-scale analysis with open source tools.
Recommended or Required Reading
1.Salganik, M. J. (2017). Bit by Bit: Social Research in the Digital Age.
2.Wickham, H., & Grolemund, G. (2016). R for Data Science.
3.VanderPlas, J. (2016). Python Data Science Handbook.
Weekly Detailed Course Contents
Week 1 - Theoretical
Introduction: Data and Digitization in the Social Sciences
Week 2 - Theoretical
What is Data Science? Basic Concepts
Week 3 - Theoretical
Python and R Introduction: Installation and Basic Commands
Week 4 - Theoretical
Data Types and Cleaning Techniques
Week 5 - Theoretical
Descriptive Statistics and Data Analysis with Pandas
Week 6 - Theoretical
Data Visualization (Matplotlib, Seaborn, ggplot2)
Week 7 - Theoretical
Working with Survey Data
Week 8 - Intermediate Exam
Midterm
Week 9 - Theoretical
Introduction to Text Mining
Week 10 - Theoretical
Fundamentals of Social Network Analysis
Week 11 - Theoretical
Introduction to Machine Learning
Week 12 - Theoretical
Application Workshop I: Research Question - Data Set Matching
Week 13 - Theoretical
Application Workshop II: Analysis and Interpretation
Week 14 - Theoretical
Student Presentations and Discussions
Week 15 - Final Exam
Final Exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142370
Individual Work152030
Quiz19110
Midterm Examination114115
TOTAL WORKLOAD (hours)125
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
OÇ-1
5
4
4
5
5
4
5
5
5
OÇ-2
5
5
5
5
5
5
5
5
4
OÇ-3
4
4
5
5
4
4
4
4
5
OÇ-4
4
5
4
3
4
5
3
5
5
OÇ-5
4
4
5
4
4
4
3
4
5
Adnan Menderes University - Information Package / Course Catalogue
2026