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Fall 2023(Classes starting end of September) Admissions Are Open. CLICK HERE TO APPLY
Fall 2023(Classes starting end of September) Admissions Are Open. CLICK HERE TO APPLY

Master of Science – Data Science (Non-Thesis) at Yeditepe University: Tuition: $15000 USD Full Program (Scholarship Available)

Educational Objectives The
data science master's program aims to train scientists who can control, change and shape large-scale data, and research which mathematical, statistical or machine learning method can better examine the data.

Outcomes of the Master's Program Graduates of the
Data Science “Master's Programme” are expected to have the following competencies:

1. Reaches the information broadly and in depth by doing scientific research in the field of Data Science, evaluates, interprets and applies the information.
2. Complements and applies knowledge with scientific methods using limited or incomplete data; integrates knowledge from different disciplines.
3. Constructs Data Science problems, develops methods to solve them and applies innovative methods in solutions.
4. Develops new and/or original ideas and algorithms; develops innovative solutions in system, part or process designs.
5. Has comprehensive knowledge about current techniques and methods applied in Data Science and their limitations.
6. Designs and implements analytical, modeling and empirical research; analyze and interpret the complex situations encountered in this process.
7. Communicate verbally and in writing using a foreign language (English) at least at the B2 General Level of the European Language Portfolio.
8. Leads multi-disciplinary teams, develops solution approaches in complex situations and takes responsibility.
9. Presents the processes and results of Data Science studies in a systematic and clear way in written or verbal form in national and international environments in or outside the field.
10. Observes social, scientific and ethical values ​​in the stages of data collection, interpretation, announcement and in all professional activities.
11. Be aware of new and emerging applications of Data Science, examine and learn them when necessary.
12. Describes the social and environmental dimensions of Data Science applications.

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