Computational Social Sciences MA with Thesis

Application Requirements​

Applicants are expected to hold an undergraduate degree. Students, who are still studying and are expected to graduate before the Fall Term, can also apply.

In addition to the graduates of social science majors, students who have received a bachelor’s degree from other fields can also be admitted to the Computational Social Sciences Master’s Program with Thesis.

After the admittance process, students may be requested to take additional courses before or during the program.

Application documents for the Computational Social Sciences Master’s Program with Thesis include:

  • CV
  • Official Transcripts (Undergraduate graduation minimum 2.50 / 4.00 GPA)
  • English Language Proficiency: TOEFL (Test of English as a Foreign Language): IBT or PBT minimum 80 points or YDS / e-YDS (minimum 80 points) or YÖKDIL (minimum 80 points) certificate will be required. If the English Language Proficiency levels of the candidates who fulfill the other admission requirements are below the admission criteria, these candidates may be given the right to take a remedial year.
  • ALES (Academic Personnel and Postgraduate Education Entrance Exam) Equal Weighted or Verbal minimum 70 points
  • GRE (Graduate Record Examination): Required from candidates who are not Turkish citizens. Numerical minimum of 153 points
  • 2 reference letters
  • Letter of Intent 
  • Interview
In order to graduate from the Computational Social Sciences Master’s Program, it will be necessary to take a total of minimum 21 credits.

The list of courses should include:

  • one compulsory and non-credit seminar course (CSSM 590),
  • one compulsory and non-credit thesis course (CSSM 595), 
  • one compulsory and 3-credit introduction to computational social sciences (CSSM 501),
  • one compulsory and 3-credit programming course (CSSM 502),
  • one compulsory and 3-credit theory course (offered by the institute of social sciences from sociology, psychology, international relations, etc.)
  • minimum two 3-credit electives in computational social sciences and minimum one 3-credit elective outside the field of computational social sciences. These out-of-field electives should be from social sciences or computer programming.

 

1. Semester (Fall)CreditECTS2nd Semester (Spring)CreditECTS
CSSM 501: Introduction to Computational Social Sciences

 

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36CSSM 590 Seminar Course06
CSSM 502: Programming (Compulsory)36Elective course36
SOCI 503: Classical Social Theory (Compulsory)

 

Or the program by be approved another a theory course (see : Compulsory Social Theory Courses table )

36Elective course36
Elective course36Elective course36
TEACH 500 Teaching Assistant02TEACH 500 Teaching Assistant02
KOLT 500 Teaching Assistant Workshop02ENGL 500 Academic English04
ETHR 500 Ethics Course02   
Total Credit1230Total Credit930

 

 

3. Semester (Fall)CreditECTS4th Semester (Spring)CreditECTS
CSSM 595 High Degree Thesis030CSSM 595 High Degree Thesis030
TEACH 500 Teaching Assistant02TEACH 500 Teaching Assistant02
Total Credit032Total Credit032
Program Total Credit21124

 

COMPULSORY COURSES

1) CSSM 590: Seminar

Developing their research by presenting and rewriting their own work in a seminar where students develop their computational social science research, reading, writing and presentation skills.

2) CSSM 595: Thesis

Examining and discussing the research, reading and writing works of the students during the thesis writing process with their advisors.

3) CSSM 501: Introduction to Computational Social Sciences

This course provides an applied, non-technical introduction to the methods and ideas of Computational Social Sciences. It discusses how new online data sources and the methods used to analyze them can shed new light on old social science questions and asks brand new questions. It also examines some of the ethical and privacy challenges of living in a world where big data and algorithmic decision making are becoming more common.

4) SOCI 503: Classical Social Theory

This course examines the main themes and most important figures of classical sociological theory closely. The course analyzes in detail the work of Marx, Weber and Durkheim , which establish the classical roots of sociological theory and help students develop a sociological perspective through extensive theoretical discussions .

Alternative Theory Courses (Subject to Program Approval)

ARHA 506: Archaeological Methods and Theory

This course covers theoretical approaches and methods used in the design and implementation of archaeological fieldwork and data analysis. It focuses on the principles used by archaeologists to explain human cultural development from the material records of the past.

ARHA 508: Advanced Historiography and Theory

This course examines the concepts and methodology of the study of art history, including the historical and philosophical foundations of contemporary criticism and theory, and their application to visual arts in different periods and regions.

INTL 503: Global International Relations Theory

This course will focus on the historical analysis of globalization processes and their impact on world politics, with a special focus on the changing nature of the nation state, sustainable economic development and democratic global governance. While drawing attention to the globalization debate in the fields of international relations, sociology, economics and management, an interdisciplinary framework will be presented for an in-depth analysis of change in international relations.

INTL 540: Comparative Politics

In this course, the dissemination and problems of democracy as a political system, democratic consolidation, economic restructuring politics, governance and nationalism in the age of economic globalization, and current issues of importance for comparative politics such as intercultural conflict will be examined.

INTL 550: Modern Political Thought

H igh license at this level course which is communism, liberalism, fascism, anarchism, conservatism, feminism, environmentalism, mainly fundamentalist and modern political ideologies are discussed.

HIST 501: Historiography

This course examines the importance of primary sources such as purpose in historiography from the 18th to the 21st century, the professionalization of history as a discipline, archives, and key concepts such as causality, truth, interpretation and objectivity in historiography. In this context, it handles applications and methodologies in a global framework.

PSYC 542: Advanced Social Psychology

This course is an introductory level course that includes a discussion of some central theories and models and a review of the latest approaches and research in social psychology. Another aim of this course is to provide students with an overview of the methods and paradigms used by social psychologists.

SOCI 506: Modern Social Theory

Examines the contemporary theoretical approaches that emerged after the Second World War as a continuation of classical sociological theories. The course presents different theoretical approaches, including structuralism, post-structuralism, post- modernism , feminism, post-Marxism, and sub- studies, by reading and discussing the work of the most important figures in these fields . 

AREA ELECTIVE COURSES (With credit)

A sample list and explanations of the field elective courses that students can take are given below.

1) CSSM / COMP 341: Introduction to Artificial Intelligence

This introductory course aims to teach students what artificial intelligence is and enable them to explore the use cases and applications of artificial intelligence. In addition , understanding of various artificial intelligence concepts, machine learning, deep learning and acquiring familiarity with terms such as neural networks are among the aims.

2) CSSM / COMP 546: Algorithm Design and Analysis

This course is a graduate algorithm course that focuses on data structures, algorithms and their advanced calculations. Asymptotic measures of complexity, graphic representations, topological order and algorithms, network flow problems are among the topics covered.

3) CSSM / COMP / DASC 521: Introduction to Machine Learning

This course aims to provide a general introduction to machine learning, covering regression, classification, clustering and dimension reduction methods. Topics covered include audited and uncontrolled models, linear and non-linear models, parametric and non-parametric models, combinations of multiple models, comparison of multiple models, and model selection.

4) CSSM 510 / ARHA 567 : Geographic Information Systems

Geographic Information Systems (GIS) is a computer system designed to collect, manage, organize, analyze and present spatial information. This graduate-level course, mapping and social events that will allow them to use the system to examine students in GIS introduces the basic concepts. V input melt, manage, Geographic Information Systems to process and display (GIS) technical training on how to use the software provides . It encourages students to think both spatially and critically , while addressing the theoretical and practical frameworks in which GIS is applied . Social sciences and humanities in the sciences geo important questions as -uzamsal order to investigate for the GIS analytical tools (eg, archeology, history, art history, sociology, migration studies) are emphasized.

5) CSSM 511 / INTL 318 : Social Network Analysis

This course provides a graduate level introduction to theoretical concepts as well as data collection and analysis methods for social networks. The first parts of the course are devoted to definitions, terminologies, effective models, and their importance in experimental collection of network data. Subsequently, various research samples (data) will be examined and appropriate research questions and research design requirements in the data collection process will be discussed.

6) CSSM 512 / INTL 450 : Advanced Data Analysis with Python

This course is a postgraduate course designed to introduce the student to Python 3 and advanced data analysis techniques in a broad sense . Basic programming concepts using Python applicable to programming will be covered. These include syntax, data types, functions, loops, recursion, classes, and inheritance. Then database management, creation, processing and visualization will be covered. A brief overview of Bayes statistics with an emphasis on practical use in the programming language Stan , named via Python, will be presented, followed by an introduction to the most common machine learning methods.

7) CSSM 513: AutoText Analysis

The Autotext Analysis course for Social Sciences aims to teach the analysis of written texts collected from various sources and media using modern techniques such as natural language processing, machine learning, topic modeling and big data analysis. At the end of the course, students will be able to analyze documents obtained from internet platforms or digitalized print sources . In the course , tasks such as text classification, document clustering, topic modeling and detailed information detection will be learned in a way that supports social sciences studies .