Data Science

EO1: To provide students with advanced theoretical knowledge and practical skills in data science, machine learning, statistical modeling, and big data technologies.

EO2: To enhance students' ability to identify, analyze, and solve complex data problems, thereby strengthening their predictive analytics and data-driven decision-making skills.

EO3: To train researchers capable of developing innovative solutions in the field of data science and contributing to academic knowledge by utilizing high-performance computing and scientific research methodologies.

EO4: To equip students with competencies in effective teamwork, leadership, communication, and project management within multidisciplinary teams.

EO5: To cultivate individuals who are sensitive to the ethical dimensions of data science and who possess lifelong learning skills in a constantly evolving technological and informational landscape.

The Master’s Program in Data Science is an interdisciplinary and advanced academic program offered under the School of Applied Sciences. The program aims to train qualified professionals who can lead scientific analysis, strategic decision-making, and digital innovation processes in today’s data-driven world. By integrating mathematics, statistics, computer science, and business analytics, the program focuses on developing experts capable of working in big data environments, generating value from data, and creating data-driven solutions.

This graduate program equips students with in-depth knowledge and practical skills in areas such as data mining, machine learning, statistical modeling, predictive analytics, artificial intelligence applications, data visualization, and algorithm development. It also emphasizes the effective use of modern programming languages and big data technologies, including Python, R, SQL.

The curriculum is designed to foster critical thinking, research competence, and technical depth in data-driven decision-making, modeling, and analysis. Students gain a comprehensive learning experience through applied projects, sector-specific case studies, and lab work structured to apply academic theories to real-world problems. Interactive course content, research seminars, academic workshops, and industry collaborations help reinforce theoretical knowledge while building practical expertise.

In the thesis track, students conduct independent research under faculty supervision and produce original work that contributes to the field of data science. The non-thesis track, on the other hand, focuses on professionally oriented coursework that supports career advancement. Both tracks aim to enhance students' competencies in critical analysis, technical reporting, project management, and teamwork.

The Data Science Master’s Program aims to develop leaders who are committed to ethical data use, open science, sustainable technology, and lifelong learning. The program offers a rich academic environment for continuous growth through access to digital libraries, academic data resources, and advanced software systems.

Graduates of this program are equipped to take on strategic roles as data analysts, data scientists, business intelligence specialists, AI engineers, or academics in a wide range of sectors including healthcare, finance, manufacturing, public administration, marketing, energy, and technology.

In order to graduate from the master’s program, it is necessary to succeed in all of the courses listed in the curriculum of the program by getting the grade at least C/ S with a minimum 120 ECTS (21 in national credits) and to have a Cumulative Grade Point Average (CGPA) of 3.00 out of 4.00

1.   Completion of a minimum of 21 credits (120 ECTS) of coursework,

2.   Successful completion of all courses in the program, with a minimum letter grade of "C" or "E"/"T" for credit-bearing courses and "S" or "E"/"T" for non-credit courses,

3. A Cumulative Grade Point Average (CGPA) of 3.00 or above on a 4.00 scale.

TR Applicants

TR Students who are successful in the exams conducted by the Higher Education Council Student Selection and Placement Center (ÖSYM) and are entitled to enroll in our university in line with their preferences can complete the registration process with the necessary documents for registration from our Registration and Liaison Offices throughout Turkey or from the Registrar's Office on campus.

TRNC Applicants

TRNC citizens and TR citizen candidate students who have completed their entire high school education in TRNC. They are placed in undergraduate programs in line with their success in the CIU Student Placement and Scholarship Ranking Exam and the programs they prefer.

Students who are successful in the exam can register from the TRNC Marketing Office.

International Applicants

International Applicants can directly apply online to our undergraduate programs using the application portal.

In a data-driven and digitally transforming global economy, the demand for professionals with advanced analytical competencies who can generate strategic value from data is steadily increasing. Graduates of the Master’s Program in Data Science are equipped with the skills to analyze large and complex datasets, extract meaningful insights, and optimize data-supported decision-making processes. They gain deep expertise in areas such as artificial intelligence, machine learning, statistical modeling, and big data technologies, enabling them to transform data science applications into strategic outcomes.

Thanks to this specialized education, Data Science graduates are prepared to take on active roles across various sectors, including private enterprises, public institutions, research centers, healthcare organizations, financial services, technology firms, and non-governmental organizations. They can pursue careers in fields such as data analytics, AI applications, big data management, predictive modeling, decision support systems, data engineering, and business analytics.

Graduates may assume roles such as data scientist, data analyst, machine learning engineer, big data specialist, AI application developer, data engineer, business intelligence analyst, research data specialist, or analytics consultant. Those aiming for academic careers can pursue doctoral studies and contribute to research and teaching in higher education.

The interdisciplinary structure of the program enables graduates to develop effective solutions at the intersection of statistics, computer science, and business. As a result, they become individuals capable of leading digital transformation, data strategy, and AI-driven projects both in Turkey and globally, making meaningful contributions to scientific and technological advancements.

The measurement and evaluation methods applied for each course are defined in the "Syllabus" prepared by the relevant faculty member/s and included in the Information package. Regarding exams and course grades, relevant articles of Cyprus International University Associate Degree, Undergraduate Education and Examination Regulations are applied. (https://sis.ciu.edu.tr/regulations-and-forms/regulations/146?mcHNAuhRyuQ3KjFvSUTKzpDsClxIbTtGVhVU)

The Structure of Observed Learning Outcomes (SOLO) Taxonomy is a widely used framework for systematically categorizing the progressive complexity of students' understanding of a subject. It serves as a tool for educators to assess cognitive development, design effective learning activities, and structure assessments that align with increasing levels of comprehension and critical thinking.