Data Informatics M.S. (with thesis)

Data Informatics department website


The current wind of digital transformation in the world has increased the volume, variety, and velocity of the data that can be turned into benefit and has made it necessary to use data and data-driven decision-making for today's organizations. The trend of increasing productivity, quality, and product diversity with automation and data-centered approaches, commonly referred to as Industry 4.0, can be seen as a result of gains in technologies such as the Internet of Things (IoT), cyber-physical systems, big data, cloud computing, and artificial intelligence. These changes in the industry naturally cause the transformation of society as well. In Society 5.0, which refers to the next stage after hunter-gatherer, agricultural, industrial, and information societies in human history, technologies such as the Internet of Everything (IoE), artificial intelligence, and block-chain affect and shape all areas of life.

Data Informatics emerges as a wide field of study that covers the acquisition, storage, security, integration, distribution, and management of data along with processing it and obtaining useful information from it to increase the efficiency of organizations in operational processes. Data informatics has an interdisciplinary nature. This master's program aims to offer the foundations and skills that will provide this interdisciplinary perspective to the students.

Program Structure

The master's program consists of a deficiency program and the main program. The deficiency program aims to ensure all admitted students have the necessary academic foundation. Students can be exempted from some of the deficiency courses with their advisors and the academic board's decision based on their previously taken courses and grades. The deficiency program can be completed in a maximum of two terms.

Deficiency Courses: 

  • IS 503 Database Concepts and Applications / CENG 302
  • IS 545 Object Oriented Programming and Data Structures / MI 545
  • DI 591 Probability and Statistics for Data Informatics / BIN 502 / CENG 222
  • DI 592 Mathematics for Data Informatics

The main program, as indicated below, consists of 2 compulsory and 5 elective courses that total to at least 21 credits along with non-credit thesis, research methods, and seminar courses: 

  • 2 credit and compulsory courses 
  • At least 5 elective courses 
  • DI 590 Seminar course 
  • DI 599 Thesis course 
  • DI 500 / MI 500 / CSEC 500 / COGS 500 Research Methods and Ethics

Credit Compulsory Courses: 

  • DI 501 / IS 509 Introduction to Data Informatics  
  • DI 502 Data Informatics Project  

Non-Credit Compulsory Courses: 

  • DI 500 / MI 500 / CSEC 500 / COGS 500 Research Methods and Ethics 
  • DI 590  Seminar course 
  • DI 599  Thesis course 

In addition to the compulsory courses, students will be able to take the technical elective courses listed below to be counted towards the required credit hours: 

Elective Courses:

  • DI 504 / MMI 727 Foundations of Deep Learning
  • DI 514 / IS 580 Data Mining and Knowledge Discovery 
  • DI 520 / IS 786 Data Driven Organizations 
  • DI 521 / IS 788 Digital Transformation: Management, Technology and Organization 
  • DI 515 / IS 787 Big Data  
  • DI 544 / IS 782 Spatial Data Analysis 
  • DI 710 Data Engineering
  • DI 722 Spatio-temporal Data Mining
  • MMI 701 Multimedia Signal Processing 
  • MMI 702 Machine Learning for Multimedia Informatics 
  • MMI 706 Reinforcement Learning 
  • MMI 711 Sequence Models in Multimedia
  • MMI 712 Machine Learning Systems Design and Deployment
  • MMI 726 Multimedia Standards 
  • IS 533 Decision Support Systems 
  • IS 535 Regulatory and Legal Aspects of Information Systems 
  • IS 543 Information Retrieval 
  • IS 547 Cloud Computing: Technology and Business 
  • IS 566 Image Processing Algorithms 
  • IS 585 Social Network Analysis 
  • IS 710 Energy Informatics 
  • IS 748 Mobile and Pervasive Computing 
  • IS 768 Applications of Audio Information Systems 
  • IS 777 Technology Entrepreneurship and Lean Startups 
  • IS 783 Social Media Analytics 
  • IS 784 Deep Learning for Text Analytics 
  • COGS 515 Artificial Intelligence for Cognitive Science 
  • COGS 516 Introduction to Probabilistic Programming 
  • COGS 566 Probabilistic Models of Cognition 
  • MI 535 Biological Signal Analysis

Application Requirements

Application requirements for Graduate School of Informatics