The information and program qualifications related to the Big Data and Business Analytics Master of Science Program, under the Department of Data Engineering and Business Analytics of the Graduate School, are summarized below.
Information About The Program
In the era of digital transformation, data has become one of the most valuable strategic assets for organizations. Today, companies, public institutions, and research centers generate vast amounts of data daily, and the ability to effectively analyze this data has become a key factor in gaining a competitive advantage. Big Data and Business Analytics emerge as a multidisciplinary field that focuses on extracting meaningful, actionable, and strategic insights from large and complex datasets.
The Big Data and Business Analytics Program at Istanbul Technical University brings together concepts and methods from data science, statistics, computer science, machine learning, artificial intelligence, and decision analysis in order to equip students with strong data-driven analytical skills. Within the scope of the program, students gain both theoretical knowledge and practical experience in areas such as data acquisition, data preprocessing, data management, data visualization, machine learning, large-scale data processing systems, and business intelligence. The program aims to educate professionals who are capable of applying advanced analytical techniques and big data technologies to solve complex real-world problems.
Today, data analytics plays a critical role not only in technology companies but also across a wide range of sectors, including finance, healthcare, energy, manufacturing, e-commerce, telecommunications, logistics, and public administration. Through the effective use of big data analytics, organizations can better understand customer behavior, optimize operational processes, and make more accurate strategic decisions for the future.
Data analysts and data scientists contribute to organizations in several key ways, including:
Supporting data-driven decision making: Analytical insights enable managers to make informed strategic decisions based on empirical evidence rather than intuition.
Improving operational efficiency and reducing costs: Data analytics helps identify inefficiencies and optimize processes, enabling more effective use of resources.
Forecasting future trends: Predictive analytics methods allow organizations to anticipate market trends, customer behavior, and potential risks.
Enhancing competitive advantage: Organizations that leverage data-driven strategies are better positioned to adapt to rapidly changing market conditions.
Improving organizational performance: Data analysis provides valuable insights that can enhance employee performance, customer satisfaction, and overall institutional effectiveness.
The ITU Big Data and Business Analytics Program not only provides students with strong technical knowledge but also develops their abilities in analytical thinking, problem solving, and evidence-based decision making. Graduates of the program are well prepared for a variety of career paths, including data scientist, data analyst, business analytics specialist, machine learning engineer, data engineer, and business intelligence specialist.
As the volume and importance of data continue to grow rapidly, the demand for professionals who can transform data into strategic value is increasing worldwide. The ITU Big Data and Business Analytics Program offers a strong academic and practical learning environment for students and professionals who wish to play an active role in this data-driven transformation.
Click here for the program's website.
Active Student Count
Program Profile
The objective of the Big Data and Business Analytics Program is to educate professionals who can transform large and complex datasets into meaningful insights that support strategic decision making. The program aims to equip students with strong analytical, computational, and problem-solving skills by integrating concepts from data science, statistics, computer science, and business analytics. Graduates are expected to develop and apply advanced data-driven methods to analyze real-world problems, generate actionable knowledge, and contribute to innovation and competitiveness in data-intensive environments.
Registration Requirements
Application Term : Previous Application / 2025-2026 Spring Semester
Application Requirements (For T.C Nationality) ALES Numerical must be minimum 85 (old system GRE Quantitative minimum 740, new system GRE minimum 158 ). Undergraduate GPA must be minimum 2.5/4 (65/100).
Quota (For T.C Nationality): 10
Application Requirements (For International Students) ALES Numerical must be minimum 85 (old system GRE Quantitative minimum 740, new system GRE minimum 158 ). Undergraduate GPA must be minimum 2.5/4 (65/100).
Quota (For International Students): 3
Click here for quotas and conditions for all semesters. Application Page Graduate Education and Training Regulation Senate Principles
Application requirements, application dates, and quotas are updated in real-time from the Application System.
Program Learning Environments
The Big Data and Business Analytics Program offers a comprehensive learning environment that provides students with both a strong theoretical foundation and an application-oriented educational experience. Courses are conducted in modern classroom settings, while practical components are carried out in computer laboratories. This structure enables students to reinforce their knowledge in areas such as data analysis, statistical modeling, machine learning, and big data technologies through hands-on applications.
Within the scope of the program, students have the opportunity to work with widely used software tools and programming environments employed in big data analytics and data science applications. Furthermore, project-based studies involving large-scale datasets aim to enhance students’ analytical thinking and problem-solving skills.
The program’s learning environments are not limited to local computer laboratories; they also include access to high-performance computing resources such as the Amazon Web Services (AWS) cloud infrastructure and the National High Performance Computing Center (UHeM). Through these infrastructures, students gain practical experience in large-scale data processing and computationally intensive applications using advanced computing environments.
MasterBee
English Proficiency
Regulations and Guidelines
Academic Calendar
Course Plans
Course Schedules
Course Adjustment and Exemption Procedures
Program Educational Objectives
Today, advances in data collection, storage, and processing technologies have significantly increased the volume and diversity of data that can be generated and analyzed. Large-scale datasets are produced through a wide variety of sources such as sensors, mobile devices, social media platforms, online transaction systems, enterprise information systems, and Internet of Things (IoT) infrastructures. When analyzed effectively, these data sources provide valuable insights that support strategic decision-making processes in both public and private sectors. Big data analytics enables organizations to transform raw data into actionable knowledge, thereby enhancing efficiency, innovation, and competitiveness.
Applications of big data and business analytics span a wide range of sectors. Some prominent application domains include:
Banking and Finance: Credit risk analysis, detection of credit card fraud, portfolio management, algorithmic trading, and financial risk modeling.
Telecommunications, Media, and Entertainment: Content recommendation systems, user behavior analysis, content performance measurement, and digital marketing analytics.
Healthcare and Biomedical Sciences: Early detection of epidemic diseases, improving healthcare service efficiency, patient data analysis, clinical decision support systems, and performance analysis of healthcare professionals.
Education: Student performance analysis, learning analytics, improvement of educational processes, and evaluation of teaching effectiveness.
Manufacturing and Industry: Supply chain optimization, production line efficiency improvement, quality control, process monitoring, and predictive maintenance.
Government, Public Administration, and Security: Smart city applications, optimization of transportation systems, data-driven prevention of crime and terrorism, and improving the efficiency of public services.
Insurance: Risk modeling, claim analysis, fraud detection, and policy pricing strategies.
Retail and E-Commerce: Customer behavior analysis, shelf placement optimization, promotion strategies, demand forecasting, dynamic pricing, and customer satisfaction analysis.
Energy and Smart Grids: Energy demand forecasting, energy consumption analysis, smart grid optimization, and sustainable energy management.
Logistics and Transportation: Route optimization, fleet management, traffic analytics, and improvement of transportation processes.
Agriculture and Food Technologies: Precision agriculture applications, crop yield prediction, and analysis of climate and soil data.
Marketing and Customer Analytics: Customer segmentation, customer lifetime value analysis, campaign optimization, and brand perception analysis.
Cybersecurity: Anomaly detection, network traffic analysis, intrusion detection, and prediction of potential security threats.
The Big Data and Business Analytics Master’s Program (With Thesis) aims to educate highly qualified professionals capable of working in these application domains. The program is designed to provide students with a strong theoretical foundation in big data technologies, data analytics methodologies, statistical modeling, machine learning, and decision analysis. In addition, the program seeks to develop researchers who are capable of analyzing complex real-world problems, designing data-driven solutions, and contributing to the scientific literature through interdisciplinary research and innovation.
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
Students are required to complete eight courses, at least four of which must be selected from the mandatory course group, as well as a seminar course. In addition, students must prepare and successfully defend a master’s thesis in order to fulfill the requirements of the program.
The Awarded Degree and Title
Degree : Master of Science Title : -
Program Employment Opportunities
Banking and Finance: Credit risk analysis, detection of credit card fraud, portfolio management, algorithmic trading, and financial risk modeling.
Telecommunications, Media, and Entertainment: Content recommendation systems, user behavior analysis, content performance measurement, and digital marketing analytics.
Healthcare and Biomedical Sciences: Early detection of epidemic diseases, improving healthcare service efficiency, patient data analysis, clinical decision support systems, and performance analysis of healthcare professionals.
Education: Student performance analysis, learning analytics, improvement of educational processes, and evaluation of teaching effectiveness.
Manufacturing and Industry: Supply chain optimization, production line efficiency improvement, quality control, process monitoring, and predictive maintenance.
Government, Public Administration, and Security: Smart city applications, optimization of transportation systems, data-driven prevention of crime and terrorism, and improving the efficiency of public services.
Insurance: Risk modeling, claim analysis, fraud detection, and policy pricing strategies.
Retail and E-Commerce: Customer behavior analysis, shelf placement optimization, promotion strategies, demand forecasting, dynamic pricing, and customer satisfaction analysis.
Energy and Smart Grids: Energy demand forecasting, energy consumption analysis, smart grid optimization, and sustainable energy management.
Logistics and Transportation: Route optimization, fleet management, traffic analytics, and improvement of transportation processes.
Agriculture and Food Technologies: Precision agriculture applications, crop yield prediction, and analysis of climate and soil data.
Marketing and Customer Analytics: Customer segmentation, customer lifetime value analysis, campaign optimization, and brand perception analysis.
Cybersecurity: Anomaly detection, network traffic analysis, intrusion detection, and prediction of potential security threats.
Number of Graduates
Graduate Statistics (Last Five Years)
| Year | Number of Graduates |
| 2024 | 8 |
| 2025 | 18 |
Program Outcomes
P.O.1 The ability to use the theoretical and practical knowledge acquired in the Big Data and Business Analytics area (skill), developing and intensifying those knowledge, based upon the competency gained in the undergraduate level , 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 , grasping the inter-disciplinary interaction related to the Big Data and Business Analytics area
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 (Learning competence), 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.
Turkish Qualifications Database
The qualification has not yet been incorporated into the TYÇ.
Program Coordinator
Head of the Department
Click for detailed student count.
Links