Chairman’s Welcome Message
Dear Students,
It is my great pleasure to welcome you to the Department of Data Analytics Engineering at Near East University, established in 2024 under the Faculty of Artificial Intelligence and Informatics.
We live in an era where data is often described as the new oil—a resource that drives innovation, competitiveness, and societal development. Yet, data alone is not enough; it is the ability to engineer systems that can collect, process, analyse, and transform vast data streams into meaningful knowledge that truly makes the difference. This is where our department stands out.
Unlike traditional data science or business analytics programs, our Data Analytics Engineering program equips students with both deep analytical skills and robust engineering competencies. Our curriculum integrates mathematics, statistics, programming, machine learning, and artificial intelligence with big-data architectures, distributed systems, cloud computing, IoT, and information engineering. In addition, compulsory industrial practice and senior capstone projects ensure that our graduates not only master theory but also gain the hands-on experience required to tackle real-world problems.
Our mission is to educate professionals who are capable of designing and managing end-to-end data analytics systems that are ethical, sustainable, and impactful. Our vision is to position the department as a regional and international leader in data analytics engineering, producing graduates who can innovate across various industries, including finance, healthcare, telecommunications, e-commerce, and smart cities.
I warmly invite you to join our growing academic community, take advantage of the opportunities offered by our program, and contribute to shaping a future where data and engineering work together to create meaningful solutions for society.
Welcome to the Department of Data Analytics Engineering, where we transform data into value and engineers into innovators.
With best regards,
Assoc. Prof. Dr. Seren Başaran
Head of the Department of Data Analytics Engineering
E-mail: [email protected]
Courses
1st Semester 2nd Semester 3rd Semester 4th Semester 5rd Semester 6th Semester 7th Semester 8th Semester ELECTIVE COURSE
CODE
COURSE NAME
C/E
T
P
C
E
MTH113
LINEAR ALGEBRA
C
3
0
3
5
AII102
PROGRAMMING AND PROBLEM SOLVING
C
3
2
4
5
ENG101
ENGLISH I
C
3
0
2
5
MTH101
CALCULUS I
C
4
0
4
5
PHY101
GENERAL PHYSICS I
C
3
2
4
5
CAM100
CAMPUS ORIENTATION
C
2
0
0
5
CHM101
GENERAL CHEMISTRY I
C
3
2
4
5
Total
21
6
19
30
CODE
COURSE NAME
C/E
T
P
C
E
AII104
DISCRETE STRUCTURES
C
3
0
3
5
ENG102
ENGLISH II
C
3
0
2
3
MTH102
CALCULUS II
C
4
0
4
6
PHY102
GENERAL PHYSICS II
C
3
2
4
6
AII108
OBJECT ORIENTED PROGRAMMING
C
2
2
3
6
YIT101
TURKISH FOR FOREIGNERS I
C
2
0
2
2
GEC351
21st CENTURY SKILLS
C
2
0
0
2
Total
19
4
18
30
CODE
COURSE NAME
C/E
T
P
C
E
DAE001
ADVANCED ALGEBRA AND CALCULUS
C
3
2
4
6
AII201
DATA STRUCTURES & ALGORITHMS
C
3
2
4
6
DAE003
INFORMATION THEORY
E
3
0
4
4
MTH 201
DIFFERENTIAL EQUATIONS
C
4
0
4
6
DAE002
INTRODUCTION TO AUDIOVISUAL PROCESSING
E
3
0
3
4
AIT 103
ATATÜRK PRINCIPLES AND REFORMS I
C
2
0
2
2
YIT102
TURKISH FOR FOREIGNERS II
C
2
0
2
2
Total
20
4
23
30
CODE
COURSE NAME
C/E
T
P
C
E
AII202
DATABASE MANAGEMENT SYSTEMS
C
4
0
4
5
DAE004
DATA ENGINEERING THEORY
E
3
2
3
5
DAE005
SIGNALS AND SYSTEMS
C
4
0
4
4
MTH251
PROBABILITY AND STATISTICS
C
3
0
3
6
AIT104
ATATÜRK PRINCIPLES AND REFORMS II
C
2
0
2
2
AIE299
SUMMER TRAINING I
C
0
2
0
6
CHC100
CYPRUS HISTORY AND CULTURE
E
2
0
0
2
Total
18
4
16
30
CODE
COURSE NAME
C/E
T
P
C
E
DAE007
PROBABILITY AND STATISTICS 2
C
3
0
3
5
DAE006
MACHINE LEARNING FOR SATELLITE IMAGERY
E
2
2
3
5
ENG201
ORAL COMMUNICATION SKILLS
C
3
0
3
4
DAE008
PRESCRIPTIVE ANALYTICS I
E
3
0
3
5
AII439
OCCUPATIONAL HEALTH AND SAFETY I
C
2
0
2
4
DAE009
SYSTEMS ENGINEERING
E
3
0
3
5
CAR100
CAREER PLANNING
C
2
0
0
2
Total
18
2
17
30
CODE
COURSE NAME
C/E
T
P
C
E
DAE011
MACHINE LEARNING 1
E
3
0
4
5
DAE012
MATHEMATICAL OPTIMIZATION
E
3
2
4
5
DAE013
PARALLELISM AND DISTRIBUTED SYSTEMS
E
3
0
4
5
DAE014
ADVANCED DATABASES
E
4
2
4
5
AIE399
SUMMER TRAINING II
C
0
0
0
5
DAE010
PRESCRIPTIVE ANALYTICS II
E
3
0
4
5
Total
16
4
20
30
CODE
COURSE NAME
C/E
T
P
C
E
DAE015
MACHINE LEARNING 2
E
3
0
3
5
DAE016
ENTREPRENEURSHIP AND INNOVATION
E
3
0
3
4
DAE018
SENIOR ADVANCED DESIGN PROJECT I
C
4
2
4
6
TE
TECHNICAL ELECTIVE
E
3
0
3
5
TE
TECHNICAL ELECTIVE
E
3
0
3
5
DAE017
INFORMATION RETRIEVAL AND ANALYSIS
E
3
2
4
5
Total
19
4
20
30
CODE
COURSE NAME
C/E
T
P
C
E
AII429
ENGINEERING ETHICS
C
3
0
3
5
DAE019
SENIOR ADVANCED DESIGN PROJECT II
C
3
0
3
5
DAE020
INFORMATION VISUALIZATION
E
3
0
3
4
TE
TECHNICAL ELECTIVE
E
3
0
3
5
TE
TECHNICAL ELECTIVE
E
3
0
3
5
DAE021
IMAGE PROCESSING AND MACHINE VISION
E
3
0
3
4
AII440
OCCUPATIONAL HEALTH AND SAFETY II
C
2
0
3
2
Total
18
0
21
30
Total No. of Courses 53 Total No. of Electives 21 Total No. of Credits 156 Percentage of Electives 39 Total ECTS 240 Previous Total No. of Credits 163 New Total No. of Credits 158 Previous Total ECTS 240 New Total ECTS No change C/E: Compulsory/Elective T: Hours of Theoretical Study P: Hours of Practice/Lab E: ECTS C: Credits
CODE
COURSE NAME
T
P
C
E
AII419
IMAGE PROCESSING
2
2
3
5
AII415
DECISION MAKING
2
2
3
5
AIE411
ADVANCED DATA ANALYSIS
2
2
3
5
AIE412
INFORMATION RETRIEVAL AND WEB SEARCH
2
2
3
5
AIE413
HUMAN-ROBOT INTERACTION
2
2
3
5
AIE414
DEEP REINFORCEMENT LEARNING AND CONTROL
2
2
3
5
AIE415
MOBILE ROBOT PROGRAMMING
2
2
3
5
AIE416
AUTONOMOUS AGENTS
2
2
3
5
AIE417
INTRODUCTION TO QUANTUM COMPUTING
2
2
3
5
AIE418
COMPUTER ANIMATION & VISUALIZATION
2
2
3
5
AIE419
ALGORITHMIC GAME THEORY AND ITS APPLICATIONS
2
2
3
5
AIE420
FUZZY SYSTEMS
2
2
3
5
AIE458
AI AND INTERNET OF THINGS
2
2
3
5
AIE457
AI AND CLOUD COMPUTING
2
2
3
5
DAE022
BIG DATA SYSTEMS
2
2
3
5
DAE023
DATA SECURITY AND PRIVACY FOR ANALYTICS
2
2
3
5
DAE024
BUSINESS ANALYTICS
2
2
3
5
DAE025
HEALTH INFORMATICS
2
2
3
5
Mission – Vision
Mission
The mission of the Data Analytics Engineering Program is to educate highly qualified engineers with strong foundations in mathematics, statistics, computer science, and engineering who can design, implement, and manage data-driven solutions. The program aims to equip students with analytical thinking, ethical awareness, and practical skills in data analytics, artificial intelligence, and decision support systems, enabling them to address complex problems in industry, research, and society through innovative and responsible use of data.
Vision
The vision of the Data Analytics Engineering Program is to become a leading and internationally recognized program in data analytics and artificial intelligence education, known for its interdisciplinary approach, applied research orientation, and strong alignment with global technological developments. The program aspires to graduate competent, innovative, and socially responsible data analytics engineers who contribute to scientific advancement, digital transformation, and sustainable development at national and international levels.
Program Information
Qualification Awarded
The students who successfully complete the program are awarded the degree of Bachelor of Science in Data Analytics Engineering.
Level of Qualification
This is a First Cycle (Bachelor’s Degree) program.
Specific Admission Requirements
In the framework of the regulations set by Higher Education Council of Turkey (YÖK), student admission for this undergraduate program is made through a university entrance examination called YKS. Following the submission of students’ academic program preferences, Student Selection and Placement Center (ÖSYM) places the students to the relevant program according to the score they get from ÖSYS.
International students are accepted to this undergraduate program according to the score of one of the international exams they take such as SAT, ACT and so on, or according to their high school diploma score.
Exchange student admission is made according to the requirements determined by bilateral agreements signed by NEU and the partner university.
Visiting students can enroll for the courses offered in this program upon the confirmation of the related academic unit. Additionally, they need to prove their English language level since the medium of instruction of the program is English.
Qualification Requirements and Regulations
The students studying in this undergraduate program are required to have a Cumulative Grade Points Average (CGPA) of not less than 2.00/4.00 and have completed all the courses with at least a letter grade of DD/S in the program in order to graduate. The minimum number of ECTS credits required for graduation is 240. It is also mandatory for the students to complete their compulsory internship in a specified duration and quality.
Recognition of Prior Learning
At Near East University, full-time students can be exempted from some courses within the framework of the related bylaws. If the content of the course previously taken in another institution is equivalent to the course offered at NEU, then the student can be exempted from this course with the approval of the related faculty/graduate school after the evaluation of the course content.
Profile of the Program
The Data Analytics Engineering undergraduate program is a multidisciplinary four-year degree designed to educate professionals who can collect, manage, analyze, interpret, and transform data into actionable knowledge for decision-making. The program combines foundations in mathematics, statistics, computer science, artificial intelligence, machine learning, data engineering, database systems, and optimization with practical applications in business, industry, healthcare, finance, smart systems, and public services. It aims to equip students with the ability to design end-to-end data-driven solutions, develop analytical models, build intelligent decision support systems, and communicate results effectively through visualization and reporting. In addition to technical competencies, the program emphasizes ethical responsibility, data privacy, cybersecurity awareness, sustainability, interdisciplinary teamwork, problem solving, innovation, and lifelong learning. Graduates are expected to work as data analysts, data engineers, business intelligence specialists, machine learning practitioners, and analytics consultants, or continue to postgraduate studies in related fields.
Program Outcomes
Graduates of the 4-year Data Analytics Engineering program will be able to:
Knowledge (TYYÇ 6.1)
PO1. Demonstrate comprehensive knowledge of mathematics, statistics, computer science, and engineering fundamentals to model, analyze, and solve data-driven problems.
PO2. Explain and apply core concepts of data engineering, databases, information theory, and big data systems for designing scalable data pipelines and architectures.
PO3. Describe advanced methods in machine learning, artificial intelligence, and prescriptive/predictive analytics, including their theoretical foundations and practical applications.
Skills (TYYÇ 6.2)
PO4. Collect, clean, manage, and integrate heterogeneous data sources, ensuring data quality and reproducibility.
PO5. Apply information systems, artificial intelligence, software, and data analysis tools effectively to design and implement solutions that address business and engineering problems.
PO6. Apply statistical modeling, mathematical optimization, and simulation techniques to extract meaningful patterns, make predictions, and support evidence-based decision making.
PO7. Visualize and communicate complex data and analytical results effectively using modern visualization tools and interactive dashboards.
PO8. Design and conduct experiments or projects—such as capstone and summer practices—integrating theoretical knowledge into real-world data analytics applications.
Competences
Personal & Occupational Competence (TYYÇ 6.3)
PO9. Work effectively in multidisciplinary and multicultural teams, demonstrating leadership, project management, and professional responsibility in engineering contexts.
PO10. Adhere to ethical principles, data privacy regulations (e.g., GDPR), and professional standards when collecting, processing, and analyzing data.
Learning Competence (TYYÇ 6.4)
PO11. Engage in lifelong learning by following emerging trends in AI, cloud computing, IoT, and other evolving technologies in data analytics.
PO12. Critically evaluate scientific literature, emerging tools, and industry best practices to update professional knowledge.
Communication & Social Competence (TYYÇ 6.5)
PO13. Communicate effectively in written and oral form, prepare professional documents, present analytical results, and interact confidently with diverse stakeholders in English and with intercultural awareness.
Field-Specific Competence (TYYÇ 6.6)
PO14. Integrate data analytics engineering solutions into domain-specific contexts (e.g., finance, healthcare, smart cities, satellite imagery, robotics) to create societal and economic value.
PO15. Recognize the societal, environmental, and sustainability impacts of data-driven technologies and contribute responsibly to sustainable development goals (SDGs).
Course and Program Outcomes Matrix
Occupational Profiles of Graduates
Graduates of the BSc in Data Analytics Engineering program are prepared for a wide range of engineering and analytics roles that require both advanced data analysis and large-scale system design skills.
Thanks to the program’s combined focus on mathematics, statistics, machine learning, and data engineering, they can pursue careers in both the private and public sectors, or continue to post-graduate studies.
Typical Job Titles
- Data Analytics Engineer / Big Data Engineer – designing and maintaining large-scale data pipelines and analytics infrastructures.
- Machine Learning Engineer / AI Engineer – building and deploying predictive and prescriptive models in diverse application domains.
- Business Intelligence (BI) Developer / Analyst – transforming enterprise data into actionable insights for strategic decision making.
- Data Platform Architect / Cloud Data Engineer – architecting and optimizing distributed databases, cloud and IoT data systems.
- Data Scientist – applying statistical and computational methods to complex business, scientific, or societal problems.
Fields of Employment
Graduates are highly sought after in industries such as:
- Finance and Banking (fraud detection, risk analytics, algorithmic trading)
- Healthcare and Health Informatics (clinical decision support, medical image analytics)
- Telecommunications and Networking (customer analytics, network optimization)
- E-commerce and Digital Marketing (recommendation systems, customer behaviour modelling)
- Manufacturing and Smart Cities (predictive maintenance, IoT data management)
- Government and Public Sector (policy analysis, smart infrastructure planning)
Further Academic Opportunities
The qualification also provides a strong foundation for admission to second-cycle (Master’s) programs and doctoral research in:
- Data Science
- Artificial Intelligence and Machine Learning
- Computer Engineering
- Information Systems and related fields.
Access to Further Studies
The students graduating from this program may apply to graduate programs.
Course Structure Diagram with Course Credits
Exam Regulations, Assessment and Grading
Graduation Requirements
In order to graduate from this undergraduate program, the students are required;
- to succeed in all of the courses listed in the curriculum of the program by getting the grade of at least DD/S with a minimum of 240 ECTS
- to have a Cumulative Grade Point Average (CGPA) of 2.00 out of 4.00
- to complete their compulsory internship in a specified duration and quality.
Mode of Study
This is a full time program.
Program Director (or Equivalent)
Assoc. Prof. Dr. Seren Başaran, Head of Department, , Near East University
Evaluation Questionnaires
- Evaluation Survey
- Graduation Survey
- Satisfaction Survey