The information and program qualifications related to the Big Data and Business Analytics Master of Science Without Thesis Secondary Education Program, under the Department of Data Engineering and Business Analytics of the Graduate School, are summarized below.
Information About The Program
The Big Data and Business Analytics program within Istanbul Technical University possesses a highly flexible and multi-layered architecture designed to accommodate diverse professional objectives, time constraints, and expectations for academic depth. This academic framework comprises a thesis-based master's program structured for individuals pursuing in-depth research and development activities; a non-thesis master's program (evening education) designed to accelerate the career advancement of active professionals without interrupting their professional lives; and specialized certificate programs tailored for individuals seeking to acquire highly specific, targeted competencies. This diversity is a direct reflection of the university's strategy to simultaneously nurture both the academic sphere and the real sector.
The graduate degree programs are conducted under the umbrella of the ITU Graduate School (LEE), which serves as the central coordination hub for all postgraduate academic activities. The interdisciplinary nature of these programs necessitates the highly synchronized utilization of resources, laboratories, and faculty infrastructures across academic units with distinct areas of expertise, such as the ITU Faculty of Management and the Faculty of Computer and Informatics Engineering. The specific structuring of the non-thesis master's program in an evening education format—catering to industry professionals seeking education outside standard working hours—stands as one of the most concrete manifestations of industry-academia collaboration and the vision of lifelong learning.
As a robust reflection of the institution's vertical academic integration strategy, the program is integrated with the innovative "MasterBee" initiative, which offers high-achieving ITU undergraduate students the opportunity to take graduate-level courses at an early stage. Reaffirming the organic connection between data analytics, industrial system design, and business architecture, the primary target audience eligible to enroll in courses from this program under the MasterBee framework consists of students from the ITU Industrial Engineering and Management Engineering departments (encompassing both Turkish and English undergraduate programs). This integration ensures that analytical talents are identified at the undergraduate level and swiftly incorporated into the complex problem-solving processes characteristic of graduate studies.
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Registration Requirements
Active Student Count
Program Profile
ITU Big Data and Business Analytics programs is not merely the storage or reporting of high-volume, high-velocity, and diverse unstructured data masses, but the extraction of strategic insights capable of generating corporate, industrial, or social value through the systematic processing of this data. In this context, the primary objective is to cultivate an ecosystem designed to train data engineers and data scientists, as well as analytics managers specializing in applied artificial intelligence and business applications, across core functional domains of the business world such as marketing, finance, and manufacturing. This vision represents a hybrid approach that diverges from the strictly technical framework of traditional computer science programs, instead positioning technology as a strategic instrument for the optimization of business processes.
At the core of the program's educational objectives lies the simultaneous cultivation of student proficiency across the three fundamental stages of data analytics. The first of these stages, descriptive analytics, focuses on comprehending the significance of historical data alongside generative business intelligence reporting; the second, predictive analytics, entails forecasting future outcomes by leveraging statistical modeling and machine/deep learning methodologies; and the final stage, prescriptive analytics, aims to present organizations with optimal decision-making strategies through the application of simulation and optimization techniques. Furthermore, the curriculum addresses contemporary imperatives by specifically focusing on the business applications of generative artificial intelligence and agents.
By striving to impart the key analytical methodologies and modern software tools essential for large-scale data analysis through industrial case studies rather than confining them to a theoretical framework, the program aims to cultivate data leaders who do not merely manipulate data, but who are capable of interrogating it with strategic foresight, formulating scientific hypotheses, and articulating their findings through financial and operational metrics readily comprehensible to C-level executives.
Program Learning Environments
The success of Big Data and Business Analytics education is predicated not merely on the transmission of theoretical concepts within traditional classrooms, but rather on the provisioning of an industry-standard cloud computing environment where high-volume data can be processed, complex simulations can be executed, and robust hardware and software integration is achieved. In this context, ITU directly channels the infrastructural advantages derived from its profound engineering heritage into the learning environments of the Big Data and Business Analytics program.
As one of the program's principal operational pillars, the ITU Faculty of Management located at the Maçka Campus essential for supporting students in algorithm development, business intelligence modeling, and simulation processes—as a service from globally recognized cloud computing platforms. Through this approach, the faculty delivers firsthand, applied instruction that is specifically tailored to concrete business use cases.
Program Fees
English Proficiency
Regulations and Guidelines
Academic Calendar
Course Plans
Course Schedules
Course Adjustment and Exemption Procedures
Program Educational Objectives
-To teach the basic concepts and tools used in data analytics
-To teach the different technologies used in the analysis of large and small scale data, use-case based real sector applications
-To provide the ability to apply data analytics technologies and tools to business problems, generative business intelligence applications
Measurement and Evaluation
Evaluating Student Success
The student success is evaluated considering Articles 56, 57, 58, and 59 of the Istanbul Technical University Graduate Education and Training Regulation Senate Principles.
ARTICLE 56 - Before the enrollment for the courses begins, the faculty member who offers the course informs the Program Executive Committee about the types, number and contribution percentage to the final grade of the studies within the semester, as well as requirements for a right to take the semester final exam. These requirements shall be finalized by approval of the Program Executive Committee and approval of the chair of the department, who declares them to the student and informs the Graduate School.
ARTICLE 57 - A student may appeal the final grade of a course within one week following the announcement of the grades. Appeals must be submitted to the Graduate School in writing. The relevant faculty member shall re-evaluate the student's success status and submit the result to the Graduate School within one week. Appeals not submitted within the prescribed time frame shall not be considered by the Graduate School.
ARTICLE 58 - Courses in graduate programs shall be evaluated according to the following grading system.
| Grade Description |
Grade |
Scale |
| Excellent |
AA |
4.00 |
| Very good plus |
BA+ |
3.75 |
| Very good |
BA |
3.50 |
| Good plus |
BB+ |
3.25 |
| Good |
BB |
3.00 |
| Conditional Pass |
CB+ |
2.75 |
| Conditional Pass |
CB |
2.50 |
| Conditional Pass |
CC+ |
2.25 |
| Conditional Pass |
CC |
2.00 |
| Fail |
FF |
0.00 |
| Fail(No Exam) |
VF |
0.00 |
ARTICLE 59 - Students who wish to improve their cumulative grade point average may retake courses during the course-taking period. The most recent grade will be counted for the repeated courses.
Internship
There is no internship in this program.
Graduation Requirements
The students should take 12 courses / 36 credits and prepare a term project for graduation. Minimum GPA requirement for graduation is 3.0.
The Awarded Degree and Title
Degree : Master of Science Without Thesis Title : -
Program Employment Opportunities
Graduates possess robust employment prospects, with opportunities to secure positions as data engineers and data scientists within corporate IT, Artificial Intelligence, and Big Data units, as well as across Research and Development (R&D), marketing, finance, and manufacturing departments.
Number of Graduates
Graduate Statistics (Last Five Years)
| Year | Number of Graduates |
| 2021 | 27 |
| 2022 | 44 |
| 2023 | 26 |
| 2024 | 29 |
| 2025 | 25 |
Program Outcomes
P.O.1 The ability to use the theoretical and practical knowledge acquired in the Big Data and Business Analytics area, developing and intensifying those knowledge, based upon the competency gained in the undergraduate level (knowledge), Interpreting and forming new types of knowledge by combining the knowledge from the Big Data and Business Analytics area and knowledge from various other disciplines
P.O.2 Using the knowledge and the skills for problem solving and/or application (which are processed within the Business Analytics and Big Data area) in inter-disciplinary studies (Area specific competency), grasping the inter-disciplinary interaction related to the Big Data and Business Analytics area (knowledge)
P.O.3 Solving the problems faced in the Big Data and Business Analytics area by making use of the research methods , developing new strategic approaches to solve the unforeseen and complex problems arising in the practical processes of the Big Data and Business Analytics area and coming up with solutions while taking responsibility , assessing the specialistic knowledge and skill gained through the study with a critical view and directing one’s own learning process , the ability to carry out a specialistic study related to the Big Data and Business Analytics area independently
P.O.4 Fulfilling the leader role in the environments where solutions are sought for the problems related to the Big Data and Business Analytics area , ability to see and develop social relationships and the norms directing these relationships with a critical look and the ability to take action to change these when
necessary
P.O.5 Paying regard to social, scientific, cultural and ethical values during the collecting, interpreting, practicing and announcing processes of the Big Data and Business Analytics area related data and the ability to teach these values to others, developing strategy, policy and application plans concerning the subjects related to the Big Data and Business Analytics area and the ability to evaluate the end results of these plans within the frame of quality processes
P.O.6 Using the computer software together with the information and communication technologies efficiently and according to the needs of the Big Data and Business Analytics area, proficiency in a foreign language and establishing written and oral communication with that language , the ability to present one’s own work within the international environments orally, visually and in written forms, systematically transferring the current developments in the Big Data and Business Analytics area and one’s own work to other groups in and out the Big Data and Business Analytics area; in written, oral and visual forms
Program Coordinator
Head of the Department
Application requirements, application dates, and quotas are updated in real-time from the Application System.
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