The Data Science Master's Program with Thesis, which will begin admitting students in the Fall semester of the 2022–2023 academic year, has Turkish as its language of instruction.
Since the establishment of the first data center in the 1960s, it has been an undeniable reality that data is one of the most valuable assets for humanity. Especially since the 2000s, the concept of artificial intelligence has become increasingly dominant; however, recent examples have shown that AI applications without sufficient data can turn into empty systems lacking meaningful outcomes. In fact, more than 50% of companies that invested in artificial intelligence in 2019 failed to achieve successful results despite having strong infrastructures. Studies revealed that the primary reason for this failure was the neglect of data-related processes and the lack of sufficient importance given to data itself. According to the World Economic Forum’s Future of Jobs Report, professions related to data science have ranked first for the last two years, while artificial intelligence has fallen to second place. This clearly demonstrates the growing importance of data science worldwide. However, global statistics indicate a significant shortage of skilled human resources capable of processing and analyzing data. Although this shortage appears to be primarily technical, the broad scope of data science reveals bottlenecks in multiple dimensions.
Project-based, hardware-related, and legal processes involved in data management can lead to critical managerial errors in organizations that rely on data-driven artificial intelligence. Similarly, machines that learn from data have produced results containing discriminatory biases based on language, religion, race, gender, and similar factors, potentially harming social cohesion. This demonstrates that, in addition to technically skilled individuals, there is also a need for professionals capable of integrating data into management and business processes while anticipating and mitigating the potential social, cultural, and societal implications of machine-generated outcomes.
In 2018 and 2019, leading institutions, organizations, and thought leaders—particularly in Silicon Valley—emphasized that data literacy would rank among the top ten essential competencies of the future. These statements, made long before the pandemic, were further reinforced by the massive amounts of data and data-driven applications generated during the pandemic period, firmly establishing the central role of data science. This global transformation has also been reflected in Türkiye’s National Artificial Intelligence Strategy Document (2021). The Data Science Master’s Program aims to approach the concept of data as a scientific discipline and present it through both its technical and non-technical dimensions. The program is designed not only to include application-oriented courses but also to explore the role and impact of data in society, business, law, economics, political structures, and social systems.
Upon successful completion of coursework and the thesis process in accordance with the conditions specified in the Istinye University Graduate Education and Teaching Regulations, students are awarded the Master’s Degree in Data Science (with Thesis).
The primary objective of the Data Science Master’s Program is to train specialists capable of addressing the global and national shortage of expertise in data-driven fields. To overcome this gap, the program prioritizes hands-on experience through field studies, case-based analyses, applied projects, and sector-oriented solutions. In its technical components, the program aims to educate theorists, developers, and practitioners who can build sequential pipelines in the following areas and possess strong mathematical and coding foundations:
Data preprocessing
Feature engineering
Bias mitigation
Machine and deep learning applications
Natural language processing
Computer vision
Deepfake applications
Expert systems
Automated Machine Learning (AutoML)
In its non-technical components, the program aims to train specialists capable of analyzing processes, outcomes, and long-term implications, constructing theoretical pipelines, and interpreting the social, cultural, and business-related impacts of data-driven results in the following areas:
Explainable Artificial Intelligence (XAI)
Bias and algorithmic fairness
Personal data, privacy, and legal frameworks
Data-driven organizational transformation and management
Data-based challenges and decentralization in international relations
The metaverse and the value of generated data
Reputation and value threats in deepfake applications
Sustainability and data science
The use of data science in the context of social engineering