In recent years, the increasing role of social media and information technologies in our lives has changed the digital data perspective of many scientific fields. As in the past, the information technology tools designed by software companies can no longer adapt to the forms of the large-scale data that change daily and provide quick solutions to the demands of the changing analytical world. With rapidly developing information technologies, complex data can be analyzed with flexible programming systems that are easy to use and learn, and digital data that could not be stored in data warehouses before can be made available to users very quickly through cloud systems. As a result, it has become essential to use machine learning and mathematical methods developed in the so-called "data science" disciplines together, and the need for new scientists who can theoretically know and use these methods in a way that is compatible with the developing information technologies has increased.
The Data Science MSc program aims to train scientists who can control, manipulate, and shape large-scale data and investigate which mathematical, statistical, or machine learning method can better examine the data.
About the Program
Objectives
The master's program in data science aims to train scientists who can control, transform, and shape large-scale data and investigate which mathematical, statistical, or machine learning methods can better examine the data.
Master's Program Outcomes
Graduates of the "Master's Program in Data Science" are expected to have the following competencies:
- Expand and deepen their knowledge in Data Science by conducting scientific research, evaluating, interpreting, and applying the knowledge.
- Completes and applies knowledge using scientific methods with limited or incomplete data; integrates information from different disciplines.
- Conceptualizes Data Science problems develops methods to solve them and applies innovative methods in solutions.
- Develop new and/or original ideas and algorithms; develop innovative solutions in system, component, or process designs.
- Has comprehensive knowledge of the current techniques and methods applied in data science and their constraints.
- Designs and implements analytical, modeling, and experimental-based research; solves and interprets complex situations encountered in these processes.
- Communicates orally and in writing using a foreign language (English) at least at the B2 General Level of the European Language Portfolio.
- Leads multidisciplinary teams develops solution approaches in complex situations, and takes responsibility.
- Systematically and clearly conveys the processes and results of Data Science studies, either written or orally, in national and international environments, both within the field and outside.
- Observes social, scientific, and ethical values in data collection, interpretation, and announcement stages, as well as in all professional activities.
- Aware of new and emerging applications of Data Science, examines and learns them when necessary.
Application Requirements
The following conditions are expected during the application:
- Undergraduate degree
- The undergraduate diploma must be obtained from a domestic or internationally recognized higher education institution
- English proficiency, minimum 55 and above score from the YDS (Foreign Language Exam)
- Minimum 60 and above score from the ALES (Academic Personnel and Graduate Education Entrance Exam)
Additionally, the courses and course contents taken during the undergraduate education, the applicant's work area and topics in their professional life, will also be considered for admission. Students who need more basic programming and mathematics knowledge will take scientific preparation courses. It is recommended to take no more than six of the following courses in the scientific preparation program:
- MATH 131 Calculus I / MATH 133 Elementary Mathematics
- MATH 132 Calculus 2 / MATH 134 Advanced Mathematics
- MATH 221 Linear Algebra
- MATH 341 Probability and Statistics or equivalent
- CSE 211 Data Structures
- CSE 348 Database Management Systems
- ES 112 Algorithms and Computer Programming or equivalent (C or Python language)
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The Process
Curriculum
| Type | Code | Course Name | Credits | ECTS | |
|---|---|---|---|---|---|
| Mandatory | DATS 501 | Fundamentals of Data Science | 3 | 10 | |
| Mandatory | DATS 502 |
|
3 | 10 | |
| Mandatory | DATS 511 | Applied Statistics and Data Analysis | 3 | 10 | |
| Mandatory | DATS 600 | MSc Thesis | NC | 60 | |
| Mandatory | DATS 590 | Seminar | NC | 2 | |
| Mandatory | CSE 585 | Machine Learning | 3 | 10 | |
| Elective | Free Elective 1 | 3 | 10 | ||
| Elective | Free Elective 2 | 3 | 10 | ||
| Elective | Feee Elective 3 | 3 | 10 | ||
| Total | 21 | 132 |
