Institute of Graduate Studies and Research

Data Science (MSc)

Duration 3 Years
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About the Progeam

The Data science master program will provide an understanding of key technologies in Data science and business analytics, providing in-depth knowledge and experience in data mining, machine learning, visualization techniques, predictive modeling, statistics, problem analysis, and decision making. It is aimed to train experts who gain practical, hands-on experience with statistical programming languages and big data tools through coursework and applied research experiences. The goal of the program is to train competent professionals in the field of data science by applying innovations in high-performance computing hardware and software, providing quality and value-based education, and conducting quality research that contributes to the scientific world.

Education Opportunities

Program is designed to meet the growing workforce and researcher demand in data management, big data, and data analytics. The aim of the program is for students to develop knowledge and skills related to using a wide variety of tools, techniques, and methods for working with and conducting research about big data. A number of specializations offered in the Data Science master program are Data Management, Data mining and statistical analysis, Database management, Machine learning, Data visualization, Business Intelligence, and Data Analytics.

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Career Areas

Data science-related careers are expected to have strong job opportunities. That means there is a greater demand for data scientists than there is supply, which is fantastic news for students and professionals in the field. As a result of this scarcity, you'll discover that a data science profession can take many different directions. A position in data science offers a variety of career possibilities in addition to being one of the most paid tech occupations. As data scientists advance in their careers, their options become increasingly numerous, and many senior data scientists are given the decision of where they want to specialize. Some data scientists aspire to lead teams. Others opt to concentrate on a single field, such as marketing, or to focus on a specific skill, such as machine learning.

Contact

Institute of Graduate Studies and Research
Education and Graduate Sciences Center, GE106
Tel: +90 392 671 1111 Extension: 2214-2228
Institute E-mail: ciu-institute@ciu.edu.tr

Compulsory modules

First Semester
RESEARCH METHODS

Course code

BASC501

Credit

3

Theoretical

3

Practical

0

Ects

This course introduces students to research methods and contemporary issues related to research in a university setting. Students will be introduced to research proposal development, scientific literature reviews, measurement analysis, statistical data analysis, and research planning techniques, good research practice, and oral and written research communication. Ethics and intellectual property topics related to research will also be covered. During this course, students will evaluate the broad impact of their engineering research and relevant constraints and data analysis skills. Also students will research, plan, execute and evaluate a self-defined research project. Research will focus on the Engineering Themes of Energy, Water, Health or Security.
DATA SCIENCE CONCEPTS AND PRACTICES

Course code

DASC501

Credit

3

Theoretical

3

Practical

0

Ects

8
The concepts of data science will be covered throughout the course from a variety of angles, including conceptual formulation and properties, solution algorithms and their applications, data visualization for exploratory data analysis, and the appropriate presentation of modeling outcomes. With the use of real-world examples, students will understand the purpose, effectiveness, and constraints of models. Upon completion of the course, students will be able to comprehend the contemporary data science landscape and technical terminology, identify key concepts and tools in the field of data science and determine when they can be applied effectively. Students will also be able to recognize the significance of curating, organizing, and wrangling data, explain uncertainty, causality, and data quality and anticipate the effects of data use and misconduct.
AREA ELECTIVE

Course code

DASC5X1

Credit

3

Theoretical

3

Practical

0

Ects

8
AREA ELECTIVE
AREA ELECTIVE

Course code

DASC5X2

Credit

3

Theoretical

3

Practical

0

Ects

8
AREA ELECTIVE
AREA ELECTIVE

Course code

DASC5X4

Credit

3

Theoretical

3

Practical

0

Ects

8
AREA ELECTIVE
Second Semester
SEMINAR

Course code

BASC590

Credit

0

Theoretical

0

Practical

0

Ects

Seminar course is designed to promote research interest in various areas of Electrical and Electronic Engineering. Students are expected to further advance and deepen their knowledge regarding research methods through discussions of research results made in their fields of specialization. Students will make presentations on the progress of their research and will hold discussions with teachers to expand the range of their research. An additional objective of the research seminars is to nurture global IT specialists by having students make presentations at national or international conferences. Students are required to attend both research seminars and conferences for developing their research ability. Master students must register and fulfill departmental requirements of the seminar.
ALGORITHMS FOR DATA SCIENCE

Course code

DASC502

Credit

3

Theoretical

3

Practical

0

Ects

8
This course covers the algorithmic techniques and approaches required to handle various types of structured, semi-structured and unstructured data. The goal of the course is to teach algorithmic methods that serve as the cornerstones for handling and analyzing large datasets in a variety of formats. The course specifically covers how to pre-process big datasets, store big datasets effectively, design quick algorithms for big datasets, and evaluate the performance of designed algorithms. Algorithms for sorting, searching and matching as well as graph and streaming algorithms will be introduced. Upon completion of this course, students will have a broad knowledge of different algorithms for pre-processing, organizing, manipulating and storing different data types. Students will also be able to carry out performance analysis of each algorithm.
AREA ELECTIVE

Course code

DASC5X3

Credit

3

Theoretical

3

Practical

0

Ects

8
AREA ELECTIVE
Third Semester
THESIS

Course code

DASC500

Credit

0

Theoretical

0

Practical

0

Ects

0
The aim of the thesis study is to develop the ability of the graduate students to search the literature on the thesis subject with the support of the thesis supervisor, to organize the information based on the literature, to use and develop the data collection tool, to collect the research data and analyze the data, to tabulate the research findings and to interpret the results from the research findings, to be able to draw conclusions, make suggestions, report the research and defend the research. At the end of the study, the graduate student is expected to present the thesis in front of the jury determined by the department.

Optional modules

Students who are interested in pursuing advanced graduate studies leading to a master’s, doctoral degree, or professional doctorate degree for the Fall and Spring semesters every year. Applicants can directly apply online to our graduate programs using the application portal.

TRNC Applicants- Required documents:

  • Bachelor’s Degree Diploma
  • Bachelor’s Degree transcripts for each completed academic term/year.
  • Documents to prove English proficiency for English language departments,
  • Scanned copy of passport or identity card.

Click for detailed admission requirements information.

Students who are interested in pursuing advanced graduate studies leading to a master’s, doctoral degree, or professional doctorate degree for the Fall and Spring semesters every year. Applicants can directly apply online to our graduate programs using the application portal.

International Applicants- Required documents:

  • Bachelor’s Degree Diploma
  • Bachelor’s Degree transcripts for each completed academic term/year.
  • Evidence of English Language competence: TOEFL (65 IBT) or IELTS (5.5). Students without these documents will take the CIU English proficiency exam on campus following arrival.
  • Scanned copy of international passport/birth certificate
  • CV
  • Fully completed and signed CIU Rules and Regulations document (which can be downloaded during the online application)

Click for detailed admission requirements information.

Cyprus International University provides academic scholarships for its students as an incentive for success, with most students benefiting from 50%, 75% or 100% scholarships or discounted tuition fees. Click for more information.

Fee pe​r course     € 350,00
Fee for thesis     € 1.050,00
Fee for seminar     € 120,00 
Scientific Foundation per course     € 150,00
Registration and other fees* € 245,00
Student Union fee € 50,00    
  VAT Exc.

*Applies to 1st. Year students. € 195,00 for others.