School of Applied Sciences
Data Science and Analytics
Applications open
Tuition and study format
Academic year: 2026/27
Intakes and application dates
- 2026/2027 Ön BaşvuruApplications openJanuary 1, 2026 — August 31, 2027
Programme information package
The programme's definition and learning outcomes, as the university publishes them in its official information package.
Programme definition
History
The Department of Data Science and Analytics was established in 2025 and operates under the School of Applied Sciences. The department will admit its first cohort of students in the 2025–2026 academic year.
Qualification Awarded
Degree: Bachelor’s Degree in Data Science and Analytics
Level of Qualification (Short Cycle , First Cycle , Second Cycle, Third Cycle)
Bachelor’s Degree (EQF-LLL Level 6)
Specific Admission Requirements
Turkish Citizens: Admission to the program is based on central placement (ÖSYS) using the SAY (Quantitative) score type. Students placed in the program by ÖSYM must complete their financial obligations and complete final registration within the announced registration dates. International Students: International students are accepted based on the criteria determined by the University Senate. Please refer to the relevant web page for detailed information: Transfer Students: Students who have begun their education in an equivalent program, either in Türkiye or abroad, may apply for transfer admission. Each application is individually evaluated before the start of the semester, taking into account the student’s qualifications and the degree program applied for. Please review the relevant regulations: - Nişantaşı University Directive on Undergraduate Transfer Admissions - Regulation on Transfers Between Associate and Bachelor’s Degree Programs, Double Major, Minor, and Inter-Institutional Credit Transfer Exchange Students: Students from Türkiye or abroad may enroll in courses offered by the program within the scope of student exchange programs approved by the university and defined by formal agreements.
Specific Arrangements For Recognition Of Prior Learning (Formal, Non-Formal and Informal)
Students’ prior learning and achievements may be recognized in accordance with the "İstanbul Nişantaşı University Directive on the Recognition of Prior Learning, Course Equivalency, and Adaptation Principles." Based on this directive, students may be exempted from certain courses as necessary. To access the directive, please visit the link below: 🔗
Qualification Requirements and Regulations
The qualification requirements and regulations are outlined in the "Graduation Requirements" section.
Profile of The Programme
What is Data Science and Analytics? Data Science and Analytics is an interdisciplinary field that focuses on analyzing large data sets to extract meaningful insights and applying these insights to support strategic decision-making. The program provides students with a comprehensive education in data collection, processing, analysis, and predictive modeling. In short, it teaches how to transform raw data into valuable insights. Curriculum and Courses The Data Science and Analytics program covers a wide range of subjects. Students gain in-depth knowledge in areas such as statistics, data mining, machine learning, artificial intelligence, programming, data visualization, and big data technologies. Additionally, topics like data ethics and data security are integral parts of the curriculum.
Occupational Profiles of Graduates With Examples
Graduates of the Data Science and Analytics program have access to a wide range of career opportunities across many industries. They play an active role in the processes of data collection, cleaning, analysis, and reporting. Key positions include: - Data Scientist: Extracts knowledge from data, identifies and classifies current trends, and develops predictive models for the future. - Data Analyst: Analyzes and interprets company data to generate reports that support effective decision-making. - Business Analyst: Understands business processes and needs, and proposes data-driven solutions. - Machine Learning Engineer: Designs, develops, and implements machine learning algorithms. - Artificial Intelligence Specialist: Works on the design and development of AI systems. Graduates can pursue careers in a variety of sectors, including banking, healthcare, e-commerce, telecommunications, manufacturing, finance, and marketing.
Access to Further Studies
Bachelor’s degree graduates may apply to relevant master’s and doctoral programs.
Examination Regulations, Assessment and Grading
Students who register for a course at the beginning of the semester must attend at least 70% of theoretical courses and at least 80% of practical courses in order to be eligible to take the final exam. Each course includes both a midterm exam and a final exam. The final course grade is calculated as follows: - 40% of the grade is based on the midterm (which may include assignments, lab exams, quizzes, etc.), - 60% is based on the final exam. For detailed information on the assessment and evaluation criteria for each course, please refer to the Curriculum section below and click on the relevant course name. You may also consult the following regulations: - Nişantaşı University Associate and Undergraduate Education and Examination Regulation: - Directive on the Exam and Grading System: - School of Foreign Languages – Education and Exam Directive: - Nişantaşı TÖMER – Turkish Language Teaching Center Education and Exam Directive:
Graduation Requirements
Graduation Requirements To be eligible for graduation, students must: - Successfully complete all courses in the curriculum, totaling a minimum of 240 ECTS credits, - Achieve a cumulative GPA of at least 2.00 out of 4.00 at the time of graduation. In addition, students must successfully complete their internship or capstone project as a graduation requirement. Please consult the relevant regulations for detailed information: Nişantaşı University Associate and Undergraduate Education and Examination Regulation: 8th Semester Workplace Practice (IMU) Directive: Directive on the Issuance of Diplomas, Diploma Supplements, and Other Academic Documents:
Mode of Study (Full-Time, Part-Time, E-Learning )
Tam Zamanlı
Address, Programme Director or Equivalent
Department Head: Assoc. Professor Hatice Nazan Çağlar Email: hatice.caglar@nisantasi.edu.tr Telephone: +90(212)2101010 Address: Neotech Kampüs, Maslak Mah., Taşyoncası Sok. No: 1V ve No: 1Y, 34398 Sarıyer/İstanbul
Facilities
Library, computer laboratories, and fully equipped classrooms
Programme outcomes
- Possesses fundamental knowledge of mathematics, statistics, and computer science.
- Acquires foundational theoretical knowledge in data science and analytics.
- Understands the core concepts of artificial intelligence, machine learning, and deep learning methods.
- Effectively conducts data collection, cleaning, analysis, and interpretation processes.
- Works with large-scale data using database management systems.
- Applies machine learning and statistical modeling techniques to develop decision support systems.
- Uses programming languages and software development tools in data science projects.
- Takes responsibility in data science projects either individually or as part of interdisciplinary teams.
- Develops verbal and written communication skills and conveys technical knowledge clearly.
- Possesses basic communication skills in both Turkish and English and can follow academic literature in the field.
- Effectively uses up-to-date tools, software, and technologies relevant to the field.
Programme outcomes and the Turkish Qualifications Framework
Each line is an item of the Turkish Qualifications Framework; the numbers beside it are the programme outcomes that meet it.
KNOWLEDGE
- 1
Possess advanced level theoretical and practical knowledge supported by textbooks with updated information, practice equipments and other resources
Programme outcomes1 · 2 · 3 · 4 · 5 · 6 · 7 · 9 · 11
SKILLS
- 1
Use of advanced theoretical and practical knowledge within the field
Programme outcomes1 · 2 · 3 · 4 · 5 · 6 · 7 · 8 · 11 - 2
Interpret and evaluate data, define and analyze problems, develop solutions based on research and proofs by using acquired advanced knowledge and skills within the field
Programme outcomes2 · 4 · 5 · 6 · 7 · 8 · 11
COMPETENCES ( Competence to Work Independently and Take Responsibility )
- 1
Conduct studies at an advanced level in the field independently
Programme outcomes5 · 8 · 9 · 11 - 2
Take responsibility both as a team member and individually in order to solve unexpected complex problems faced within the implementations in the field
Programme outcomes8 · 9 · 10 - 3
Planning and managing activities towards the development of subordinates in the framework of a project
Programme outcomes8 · 9 · 10
COMPETENCES ( Learning Competence )
- 1
Evaluate the knowledge and skills acquired at an advanced level in the field with a critical approach
Programme outcomes3 · 5 · 6 - 2
Determine learning needs and direct the learning
Programme outcomes1 · 10 - 3
Develop positive attitude towards lifelong learning
Programme outcomes1
COMPETENCES ( Communication and Social Competence )
- 1
Inform people and institutions, transfer ideas and solution proposals to problems in written and orally on issues in the field
Programme outcomes8 · 9 · 10 - 2
Share the ideas and solution proposals to problems on issues in the field with professionals and non-professionals by the support of qualitative and quantitative data
Programme outcomes8 · 9 - 3
Organize and implement project and activities for social environment with a sense of social responsibility
Programme outcomes8 - 4
Monitor the developments in the field and communicate with peers by using a foreign language at least at a level of European Language Portfolio B1 General Level
Programme outcomes10 - 5
Use informatics and communication technologies with at least a minimum level of European Computer Driving License Advanced Level software knowledge
Programme outcomes10
COMPETENCES ( Field Specific Competence )
- 1
Act in accordance with social, scientific, cultural and ethic values on the stages of gathering, implementation and release of the results of data related to the field
Programme outcomes4 · 5 · 6 · 7 · 11 - 2
Possess sufficient consciousness about the issues of universality of social rights, social justice, quality, cultural values and also, environmental protection, worker's health and security
Programme outcomes5 · 6 · 7 · 8 · 9 · 11