Institute of Graduate Studies and Research

Data Science (MSc)

Duration 2 Years

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About
Curriculum
Entry Requirements
Fees & Scholarships

About the Progeam

The Data Science Master's program at Cyprus International University, School of Applied Sciences, offers a comprehensive and in-depth education in data science and business analytics. The program focuses on providing students with advanced knowledge in data mining, machine learning, visualization techniques, predictive modeling, statistics, problem analysis, and decision-making. The program trains experts in statistical programming languages and big data tools and develops skilled professionals. These professionals can apply innovations in high-performance computing and conduct impactful research, contributing to the scientific community.

Education Opportunities

The Data Science Master's program is tailored to meet the growing demand for professionals and researchers in data management, big data, and data analytics. Students enrolled in the program will develop knowledge and skills in using a wide variety of tools, techniques, and methods for working with and conducting research on big data. The program offers a range of specializations, including Data Management, Data Mining and Statistical Analysis, Database Management, Machine Learning, Data Visualization, Business Intelligence, and Data Analytics. This diverse curriculum allows students to tailor their education to their specific interests and career goals.

Career Areas

Graduates of the Data Science Master's program can expect strong job opportunities in a variety of sectors, as the demand for data scientists continues to outpace the supply. This favorable job market enables diverse career paths and opportunities for advancement in the field. Data science professionals can choose to specialize in a specific sector, such as marketing, or focus on a particular skill, such as machine learning. As they progress in their careers, data scientists have the option to take on leadership roles, managing teams and overseeing data-driven projects. Some of the many career possibilities for data science master's graduates include positions in data analysis, machine learning, big data management, data visualization, and business intelligence, among others.

Contact

Institute of Graduate Studies and Research
Graduate Sciences and Education Center, GE106
Tel: +90 392 671 1111 Extension: 2776
Institute E-mail: ciu-institute@ciu.edu.tr

Compulsory Courses

First Semester

RESEARCH METHODS

Course code

BASC501

Credit

Theoretical

Practical

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

Theoretical

Practical

Ects

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

Theoretical

Practical

Ects

AREA ELECTIVE

Course code

DASC5X2

Credit

Theoretical

Practical

Ects

AREA ELECTIVE

Course code

DASC5X4

Credit

Theoretical

Practical

Ects

AREA ELECTIVE

Second Semester

SEMINAR

Course code

BASC590

Credit

Theoretical

Practical

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

Theoretical

Practical

Ects

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

Theoretical

Practical

Ects

AREA ELECTIVE

Third Semester

THESIS

Course code

DASC500

Credit

Theoretical

Practical

Ects

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.

Elective Courses

ADVANCED DATABASE MANAGEMENT SYSTEMS

Course code

DASC522

Credit

Theoretical

Practical

Ects

The course covers a variety of advanced topics in database management systems and contemporary database applications using a problem-based learning method. Advanced concurrency control techniques, relational database query processing and optimization strategies, advanced indexing techniques, parallel and distributed database systems, next-generation data models, data mining on large databases, data on the web, and topics in data security and privacy are just a few of the specific topics covered. Upon completion of the course, students will have a broad understanding of relational systems, including data models, database designs, and database manipulations. Students will also learn about the newest and most recent advancements and trends in database management systems, such as the internet database environment and data warehousing.

DATA MINING AND BUSINESS INTELLIGENCE

Course code

MISY542

Credit

Theoretical

Practical

Ects

NUMERICAL MATHEMATICS FOR DATA SCIENCE

Course code

DASC550

Credit

Theoretical

Practical

Ects

This course provides an understanding of numerical mathematic applications in data science. The floating-point representation of real numbers, truncation and round off errors, iterative approaches, and convergence are some of the main points in numerical mathematics that are covered in this course. Students will study the most basic and crucial algorithms for the fundamental numerical mathematics problems, such as the solution of algebraic equations, numerical estimation of derivatives and integrals, solution of differential equations, approximation of functions by polynomials and Fourier series and solution of systems of linear algebraic equations. Upon completion of this course, students will be able to formulate and solve problems using mathematical methods and tools, identify, understand, and solve algebraic equations and develop experience with numerical and symbolic mathematical software.

INTRODUCTION TO PROGRAMMING

Course code

ITEC112

Credit

Theoretical

Practical

Ects

PATTERN RECOGNITION

Course code

CMPE546

Credit

Theoretical

Practical

Ects

CALCULUS I

Course code

MAT101

Credit

Theoretical

Practical

Ects

EXPERT SYSTEMS DEVELOPMENT

Course code

ITEC570

Credit

Theoretical

Practical

Ects

This course is intended to give an overview about artificial intelligence and explain the state of the art of the expert system technologies. Based on the main concepts, course will include the components of expert systems, knowledge acquisition and validation. In addition, different techniques of knowledge representation and the related programming languages as well as the related tools will be covered. Besides, inference processes, explanation and reasoning under uncertainty the course will examine the expert system architecture, classification of expert systems, and the phases of building an expert system. Moreover, searching techniques used in AI, A programming language in Prolog AI in the areas of application of expert systems will be covered in this course.

INFORMATION SECURITY

Course code

ITEC558

Credit

Theoretical

Practical

Ects

In our digital modern world, computer-based applications of internet, database and information technology has raised security related issues such as confidentiality, integrity and availability of information. Information security is concerned with identification, authentication and access control. In this course, a broad knowledge of information security technologies and tools is provided. Students shall be able to understand advanced concepts in assessing the strengths and limitations of information security, integrity and privacy techniques. By completion of this course, students will gain a broad understanding of various types of security incidents and attacks, as well as different techniques to prevent, detect and react such attacks. Students will also be able to evaluate the performance of different security systems, and assess each system’s limitations and vulnerabilities.

DECISION SUPPORT SYSTEMS

Course code

ITEC553

Credit

Theoretical

Practical

Ects

This course provides a detailed understanding of decision support systems through information that supports semi-structured and unstructured decisions in many organizations. This course covers decision support systems that focus on computer-based and user manipulation of source data extracted from an organization’s internal and external databases. This course expects students to understand decision support systems such as data warehousing and data marts, online analytic processing, Data Mining, and Geographic Information Systems. This course also covers the use of modern software, including SPSS, Clementine, and the popular GIS package MapInfo. This information is available to graduate students in both business and applied Statistics and also extends to other courses.

MACHINE LEARNING THEORY

Course code

DASC510

Credit

Theoretical

Practical

Ects

In this course, the concept of machine learning and its underlying probabilistic and statistical approaches will be covered extensively with a focus on the tools and techniques required for multivariate data analysis. Students taking this course are expected to have basic mathematics and Python programming background. Students completing this course will have the skills to apply regression, classification, clustering, dimensionality reduction and evaluation methods and techniques to handle uncertain data with probabilistic models. Upon completion of this course, students will be able to compare systems that can solve problems by coming up with their own rules via trial and error with systems that automatically identify patterns in data.

MANAGEMENT INFORMATION SYSTEMS

Course code

MISY523

Credit

Theoretical

Practical

Ects

PROGRAMMING LANGUAGES

Course code

ITEC501

Credit

Theoretical

Practical

Ects

This course is designed to equip students with advanced programming skills and a deep understanding of Object-Oriented Methodology. Students will gain expertise in topics such as threads, sockets, XML parsers, collections, and database operations. The course emphasizes practical implementation through the use of Java programming language and development tools like the Eclipse IDE. Key topics include advanced concepts in Object-Oriented Programming (OOP), graphical user interface (GUI) development with Swing, multithreading, synchronization mechanisms (threads, semaphores, and mutex), file handling (text, binary, and random access files), and class serialization. Additionally, the course covers design patterns, debugging techniques, and socket programming, preparing students for real-world application development. By the end of the course, students are expected to create robust and scalable Java applications, leveraging advanced programming concepts and tools for efficient software development.

COMPUTER NETWORKS AND COMMUNICATIONS

Course code

ITEC530

Credit

Theoretical

Practical

Ects

BIG DATA ANALYSIS

Course code

DASC567

Credit

Theoretical

Practical

Ects

In this course, students learn how to create computational tools and techniques that are effective and efficient for analyzing big data, which consists of text, image, video, sound, and other types of data that can occupy terabytes and petabytes of storage space. The aim of the course is to cover analytical techniques for big data extraction, integration, indexing, searching and processing. The course begins with an overview of big data and examines what it means to analyze enormous data as well as the associated technological, conceptual, and ethical problems. Big data processing tools like Hadoop are introduced and machine learning approaches like artificial neural networks are investigated. Upon completion of this course, students will have a broad knowledge of big data analytical tools and techniques.

INTRODUCTION TO PROBABILITY AND STATISTICS

Course code

MAT205

Credit

Theoretical

Practical

Ects

ADVANCED INFORMATION SYSTEM DESIGN

Course code

ITEC550

Credit

Theoretical

Practical

Ects

TRNC
International

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.

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.

Scholarships
Fees

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.

Tuition Fees are determined at the beginning of each academic year. Candidate students who are entitled to enroll in CIU can learn their fees in line with the Tuition Fee Calculation system.