Academics
Please click here for the 2024-2025 Academic Year Associate Degree-Undergraduate Academic Calendar.
Please click here for the 2023-2024 Academic Year Associate Degree-Undergraduate Academic Calendar.
Please click here for the 2022-2023 Academic Year Associate Degree-Undergraduate Academic Calendar.
Please click here to access the content of our Department's Management Information Systems Undergraduate Program.
Please click here to access the course catalog of our department.
Please click here to access the courses in the Istanbul University Education Information System.
2024-2025 Fall Semester Syllabus
2023-2024 Spring Semester Syllabus
2023-2024 Fall Semester Syllabus
Istanbul University Institute of Social Sciences Management Information Systems Master's Program (Thesis); aims to train academic staff specialized in informatics, business, and economy, who will take part in the collection, processing, selection, and development of software required for data collection, data analysis, and who know, develop and manage artificial intelligence and machine learning processes, as well as experts needed by both academia and the industry.
The program is with a thesis, and the language of instruction is 100% English. Applicants must get at least 75 points from YÖKDİL/YDS or an equivalent foreign language exam and prove this.
Students started to be accepted to the program in the Fall semester of 2023-2024. Students will be accepted only in the Fall Semester. Please click here to see the 2023-2024 Academic Year curriculum.
The general quota for national students is 10, and the general quota for international students is 1 for Fall 2024-2025.
Applications to the program are carried out by Istanbul University Institute of Social Sciences. Click here to access the Graduate Application Guide for the Fall Semester of 2024-2025 Academic Year announced by the Institute.
Please click here for the 2023-2024 Spring Semester Syllabus.
| Date | Hour | Course | Faculty Member |
|---|---|---|---|
| Wednesday | 09:00 - 11:20 | Programming for Data Science | Assoc. Prof. Zeki Özen |
| Wednesday | 11:30 - 12:30 | Seminar | Prof. Rasim Özcan |
| Wednesday | 14:00 - 16:20 | Data Structures and Management | Assoc. Prof. Emre Akadal |
| Thursday | 09:00 - 11:20 | Algorithmic Thinking and Design | Assoc. Prof. Elif Kartal |
| Friday | 14:50 - 17:10 | Project Management | Assist. Prof. Mian Waqar Badshah |
| Friday | 18:00 - 20:20 | Scientific Research Techniques and Ethics | Prof. Murat Ustaoğlu |
Please click here for the application conditions and quota information for Double Major and Minor Programs.
Please click here for the 2024-2025 academic year fall semester In-House Horizontal Transition application guide.
Please click here for the 2024-2025 academic year fall semester Non-institutional Horizontal Transition application guide.
Please click here for the 2023-2024 academic year fall semester In-House Horizontal Transition application guide.
Please click here for the 2023-2024 academic year fall semester Non-institutional Horizontal Transition application guide.
Project Name: Using the p-Adic Metric with the k-Nearest Neighbor Classifier
Project Type: TÜBİTAK 1002 - Quick Support / Quick Support - A
Project Period: July 2023-July 2024
Dr. Elif KARTAL (Executive) & Assoc. Dr. Zeki ÖZEN (Researcher)
In the project, one of our department faculty members Assoc. Dr. Zeki Özen, Faculty of Science Mathematics Department faculty members Assoc. Dr. FATMA Çalışkan and Assoc. Dr. Beyaz Başak Eskişehirli will contribute as a researcher. The project is about improving the performance of the k-Nearest Neighbor Algorithm using the supervised learning strategy of machine learning. In the project, the use of p-adic metrics in the mathematical infrastructure of the algorithm will be investigated.
Project Name: Circular Economy Innovative Initiative in Textiles for an Entrepreneurial Europe-CITE
Project Type: EU EIT Horizon
Project Period: July 2022-June 2024
Assoc. Prof. Gökhan Övenç (Researcher)
The European Union is a Horizon project within the scope of innovative technologies. The project consortium includes organizations from five countries: Turkey, Germany, Belgium, Netherlands, and Lithuania. The project with a total budget of 1 million 200 thousand euros, Istanbul University, Hogeschool Gent (Belgium), Rheinisch-Westfaelische Technische Hochschule Aachen (Germany), Saxion University of Applied Sciences (Netherlands), Plate-forme Technologique Européenne pour le Futur du Textile et de l'Habillement a.i.s.b.l. (Belgium) and Vilnius University of Applied Sciences (Lithuania). The CITE project will last for two years and aims to create a new dynamic and agile ecosystem between selected partners and higher education institutions to increase their innovation and entrepreneurship capacities. The project focuses on innovative practices and initiatives that will strengthen the recycling ecosystem, especially in the textile industry.
Project Name: Blockchain and Data Science Research Project
Project Type: Scientific Research Project Supported by Higher Education Institutions (Istanbul University SRP)
Project Period: April 2022-April 2023
Assoc. Prof. Emre Akadal (Executive), Assoc. Prof. Gökhan Övenç (Researcher), Assist. Prof. Elif Kartal (Researcher), Assist. Prof. Zeki Özen (Researcher)
It is aimed to improve the education and research capabilities of our Department with the multi-purpose informatics laboratory MIS-LAB, which is planned to be established in our Department, which was established in 2021. The aims of the project can be listed as follows: to contribute to the processes of academic publication, to initiate blockchain-based research, to increase smart contract-based research in the logistics and foreign trade sectors, to initiate research in the context of the digital economy, to increase educational opportunities, to develop outsourced projects, to provide services for the private sector. All objectives are planned to produce human resources for areas that require competence and are of a nature to provide the infrastructure for the realization of activities in this direction within the project's scope and afterward.
Project Name: Building Virtual Learning Platform for Environmentally-Friendly Digital Transformation Management
Project Type: Erasmus+
Project Period: June 2021-May 2023
Assoc. Prof. Gökhan Övenç (Researcher)
It is a European Union project in the context of strategic partnership development in Erasmus+K226-supported higher education institutions. This project, which aims to create innovative educational contents and curricula per the digital transformation strategy, covers 24 months between 1 June 2021 and 31 May 2023. While the project is carried out under the coordination of Istanbul University Faculty of Economics, in partnership with the University of Bedfordshire (England), Greater Manchester Chamber of Commerce (England) and Bielefeld University (Germany) Istanbul Mineral Metal Exporters' Association-Immib, and also Bizpark Teknoloji A.Ş (Turkey) is the technology contractor of the project.
Project Name: Analysis of Productivity and Foreign Trade Performance of SMEs Operating in the Manufacturing Industry
Project Type: Istanbul Chamber of Commerce
Project Period: 2021 March and 2022 March
Assoc. Prof. Gökhan Övenç (Researcher)
This project, which aims to create some policy recommendations by analyzing large datasets on SMEs operating in Turkey over the last ten years and comparing them with some selected EU countries, lasted between March 2021 and March 2022. In this project, which the Istanbul Chamber of Commerce supported, it was determined that the enterprises had serious shortcomings in access to finance, efficiency, and global markets, and some policy recommendations were included.
Project Name: Developing a Decision Support System in Determining the Intensive Care Need of the Patient after the Surgery
Project Type: TÜBİTAK 1002
Project Period: November 2019-September 2021
Assist. Prof. Elif Kartal (Researcher), Assist. Prof. Zeki Özen (Researcher)
In this project, it has been examined whether a patient needs intensive care after surgery and, if so, which level of intensive care is required for the patient. Accordingly, a decision support system has been developed that predicts intensive care for patients using machine learning techniques. In this context, 4218 patient data were collected from Izmir Health Sciences University Tepecik Training and Research Hospital, and the necessary data set for machine learning analysis was prepared. Intensive care prediction models were created with different machine learning algorithms such as Naive Bayes Classifier, C4.5 Decision Tree Algorithm, C5.0 Algorithm, Classification and Regression Trees, and Random Forest Algorithm. The best model is available as a web application (https://zekiozen.shinyapps.io/icuprediction/).
Project Name: Developing a Turkish Journal Suggestion System with Text Mining
Project Type: Scientific Research Project Supported by Higher Education Institutions (İstanbul University SRP)
Project Period: January 2018-November 2018
Assist. Prof. Elif Kartal (Researcher), Assist. Prof. Zeki Özen (Researcher)
One of the critical steps in transforming a scientific study into an article is the selection of a suitable journal for the study. Suppose this selection process is not done correctly. In that case, the study may be rejected by the journal editors because the content of the study is incompatible with the journal, even before the study is sent to the referees. Therefore, before the appropriate journal submits the study to a journal, it is necessary to look at the journal articles. This situation causes a lot of time to be spent between magazine websites. Journal recommendation systems such as "Elsevier Journal Finder" and "Springer Journal Suggester" have been developed. Still, a journal recommendation system focused entirely on Turkish publications and journals published in Turkish has not been found. This project aims to develop a Turkish journal recommendation system using data mining methods. In this context, the data set needed for text mining analysis was created with the journals selected from DergiPark Akademik and the publications of these journals. The Cross-Industry Standard Process Model for Data Mining (CRISP-DM) was used to carry out the data mining processes of the project in a systematic way. At the end of the project, a web application that recommends the most suitable journal(s) for the studies of DergiPark Academic researchers was developed and made available to researchers (https://zekiozen.shinyapps.io/dergioner/).
Project Name: Developing a Decision Support System for the Prediction of Post-operative Chronic Pain (POCP)
Project Type: Scientific Research Project Supported by Higher Education Institutions (Istanbul University SRP)
Project Period: February 2017-November 2017
Assist. Prof. Elif Kartal (Researcher), Assist. Prof. Zeki Özen (Researcher)
Postoperative Chronic Pain (POCP) was defined as pain lasting at least two months, independent of preoperative pain, operative site inflammation, or disease recurrence. This project aims to contribute to minimize the problem of POCP by developing a Decision Support System (DSS) to determine the probability of AHP in patients who will undergo surgery. Data mining techniques were used in the development of DSS. The dataset consists of approximately 300 patients from the anesthesiology and reanimation units of İstanbul University-Cerrahpaşa Cerrahpaşa Faculty of Medicine, Health Sciences University İzmir Tepecik Education and Research Hospital, Istanbul Training and Research Hospital, and İstanbul Ümraniye Training and Research Hospital. In the development of DSS, a database was designed and developed for the access and storage of this data set by researchers.
Project Name: Developing a Talent Wizard for Guiding Gifted Students to Diagnosis
Project Type: Scientific Research Project Supported by Higher Education Institutions (Istanbul University SRP)
Project Period: December 2016-October 2017
Assist. Prof. Elif Kartal (Researcher), Assist. Prof. Zeki Özen (Researcher)
Both parents and educators show sensitivity in identifying gifted students. Awareness of this issue is increasing, but at this point, it is important to start the identification process correctly and identify gifted students. In other words, it is necessary to direct the students correctly for diagnosis, to be nominated correctly in the classroom, and to be guided correctly. Because the general behavior patterns of students and especially their success at school can change depending on whether they are gifted or not. It is believed that by directing students towards diagnosis, parents will better understand their children, educators will better understand their students, and they will better fulfill the tasks required for their education and development. This project aims to develop an online talent wizard to guide gifted students to diagnosis. Machine learning, one of the sub-fields of artificial intelligence, was used for the talent wizard. The data set required for machine learning techniques was prepared in line with the information of gifted and undiagnosed students from Science and Art Centers and schools affiliated with the Ministry of National Education. Analyzes were performed in RStudio with the R programming language. As a result of the analyzes, the model with the highest performance in guiding the student to be diagnosed as gifted was selected and integrated as the inference infrastructure of the talent wizard. With this skill wizard developed, it aims to support parents and educators in guiding gifted students to diagnosis.
Project Name: Construction, Economic Development, and Planning Policies
Project Type: British Academy/Newton Fund
Project Period: March 2015-March 2017
Assoc. Prof. Gökhan Övenç (Researcher)
Funded by the British Academy/Newton Fund, a partnership of the University of Sheffield and Istanbul Technical University, between March 2015 and March 2017, the main purpose of this project is to empirically analyze the relationship between the construction industry and economic development in Turkey.
Project Name: TRB2 Region Call Center Industry Analysis
Project Type: Eastern Anatolia Development Agency (DAKA)
Proje Period: December 2014-May 2015
Assoc. Prof. Gökhan Övenç (Executive)
In the light of the data collected as a result of interviews and field studies with the representatives of the public institutions, educational institutions, and private sector in the provinces of Muş, Bitlis, Van, and Hakkari, which are included in Eastern Anatolia Development Agency region, economic and financial feasibility analyzes of the call centers planned to be established in these provinces are made. The needed infrastructure and superstructure were revealed. The possible effects of the call center, especially on youth employment and regional development, were also analyzed. The project took a total of 6 months, including the field studies.
Project Name: Machine Learning Techniques based-on Classification and an Application
Project Type: Scientific Research Project Supported by Higher Education Institutions (Istanbul University SRP)
Project Period: December 2014-July 2015
Assist. Prof. Elif Kartal (Executive)
The aim of this project; is the ability to determine the patient's vital risk during or shortly after heart surgery using classification-based machine learning techniques. Risk factors of EuroSCORE (The European System for Cardiac Operative Risk Evaluation), which is used to estimate the risk of death of the patient during or shortly after cardiac surgery, were used. Different models were created with R language using Naive Bayes Classifier, k-Nearest Neighbor Algorithm, Logistic Regression Analysis, ID3, and C4.5 Decision Tree Algorithms. Selected model(s) have been made publicly available on the web via Shiny (shinyapps.io) (https://elifkartal.shinyapps.io/euSCR/).