Master's Degree Program for Professionals
Big data is paving the way to empower businesses to make better decisions: With the amount of digital data increasing at an enormous rate, rigorous research is carried out in an effort to extract value from the massive data sets, to turn them into smarter decisions for improving business results. The emerging field of Data Analytics holds the key to unleashing that potential.
Data Analytics is considered to be a relatively new field which integrates state-of-the-art computational and statistical techniques to extract business value from a rapidly expanding volume of data. Many consulting firms claim that Data Analytics will be one of the key skills of the 21st century. Most critical issue, however, is the shortage of analytical talent that could turn the high-volume data into useful information that will be used for better decision making.
In a business world in which the gap between winners and losers is narrowing down, companies are increasingly turning to data analytics to gain a competitive advantage in productivity, profitability and sustainable manufacturing processes for better products and better services. To be able to do that, companies need trained workforce skilled in Data Analytics, who are equipped to manage, understand and model the data, interpret the outcome and communicate the results for business use. Professionals holding a degree in Data Analytics will be well positioned to help their organizations gain a competitive advantage in a data-driven world. This program is designed to help our participants develop the skill set needed for creating and maintaining the added competitive edge that innovative companies are trying to establish.
Our curriculum will help you develop skills required for data-driven decision-making with a wide variety of courses such as: Introduction to Data Analytics using Python, Data management and processing, Data mining, Machine learning, Practical case studies in Data Analytics, Statistical models for data analysis, Optimization, Decision modeling, Exploratory data analysis and visualization, Social network analysis, Data privacy, security and forensic discovery, Information security law and data ethics, Project Management and Business communication, and a Capstone project.
A unique professional network
You will have the opportunity to build your network and learn from your full-time and part-time instructors along with other business professionals who take part in your classes as well as your fellow classmates in your cohort and the alumni network of Sabanci University. This is a unique professional network that will allow you to interact with a broader community.
A convenient schedule for professionals
This unique one-year graduate program for Data Analytics is designed for working professionals. Our weekday classes are conducted in the evening hours. Weekend classes are run during the day on Saturdays. All lectures are held at Minerva Han in Karaköy, a part of university premises which is within easy reach from all around Istanbul.
Professional Master's Degree in Data Analytics is a 30-credit program that can be completed in one academic year. The courses are distributed across three consecutive semesters (Fall-Spring-Summer), each of which lasts 14 weeks. Students take 10 courses (excluding the Term Project) in total from various areas. The Term Project is a non-credit course.
The curriculum will help you develop skills required in all aspects of data analytics, with flexibility to allow different interests. Courses offered during each semester are listed below. Associated links with the course titles provide brief information about the course content, the mission and scope of the course, and the skills acquired by the participant upon the completion of the course. Toward fulfillment of the degree requirements, students are expected to select 4 courses out of 6 electives in the Spring term, and 2 courses (in addition to the Term Project) out of 3 electives in the Summer Term.
- DA 501 Introduction to Data Analytics
- DA 503 Applied Statistics
- DA 505 Introduction to Data Modeling and Processing
- DA 507 Modeling and Optimization
- DA 510 Data Mining (Elective)
- DA 512 Big Data Processing using Hadoop (Elective)
- DA 514 Machine Learning (Elective)
- DA 516 Social Network Analysis (Elective)
- DA 518 Exploratory Data Analysis and Visualization (Elective)
- DA 520 Data Privacy and Security (Elective)
- DA 515 Practical Case Studies in Data Analytics (Elective)
- DA 522 Information Law and Data Ethics (Elective)
- DA 525 Project Management and Business Communication (Elective)
- DA 592 Term Project (Non-credit)
The courses will be given by Sabancı University faculty members who work in many different areas of Data Analytics, together with senior executives, managers and leaders from related business areas to improve the knowledge-base and practical skills the participants need.
Who should apply
Professional Master’s Degree in Data Analytics is designed for working professionals who are looking into developing their analytical skills with no interruption on participants’ careers. The expected participant profile:
- Graduates of disciplines with a solid quantitative background (e.g. computer science, engineering, mathematics, physics, statistics, economics and other fields with a quantitative focus), or
- All professionals who have ample work experience in a data-analytics-related area and are seeking in-depth training in Big Data Analysis.
- Diagnose, understand, measure and evaluate data to enable better decision making within the organization.
- Define and apply appropriate methodologies for complex business problems.
- Interpret findings, present and communicate the results.
Graduates can find work as data analysts, data managers, data modelers and data scientists in the financial institutions, healthcare industry, insurance industry, telecommunications industry, marketing and media firms, retail industry and government agencies.
Applications for non-thesis master’s programs are evaluated by the assigned Admission Jury. Suitable candidates are invited to a personal interview. Admissions are finalized by the approval of the related Graduate School Board upon the recommendation of the Jury and are announced to the applicants. Application periods can be found in the Academic Calendar of Sabanci University.
Your registration will be completed upon the approval of Turkish Higher Education Council regarding the equivalence of the last graduated higher institution and the course of study.
- Online Application: A print-out of the completed Application Form
- Program Application Form: DA Program Application Form
- Official Transcript: Must be issued and sealed by the Student Resources/Affairs of the applicant's university indicating the courses and grades taken.
- Statement of Purpose: Document explaining your objectives and your motivation for pursuing a graduate degree in the field of DA.
- Curriculum Vitae (CV): Candidates must submit an up-to-date Curriculum Vitae (resume).
- Photograph: One recently taken photograph
- Two Letters of Recommendation: The letters of recommendation could be submitted by the referencers online or mailed by the applicant in a sealed envelope.
- Degree Requirements: An undergraduate degree is required in order to apply for a graduate program. The applicant should have an undergraduate or a graduate diploma in order to be admitted to a graduate program. Applicants must have completed their previous degree programs by the enrollment date for the Fall Term at Sabanci University.
- English Proficiency Exam Result:
- TOEFL Internet-based test (IBT): Min. score 78
- PTE Min. score 48
- CAE Min. score B
- CPE Min. score C
- KPDS, ÜDS, YDS, e-YDS Min. score 65
- ELAE Candidates must obtain a satisfactory score in the Sabancı University English Language Assessment Examination (SU-ELAE).
The application deadline for the Academic Year 2017-2018 Fall Term:
- Early Application Period: January 2nd - June 2017
- Interviews: June 2017
- Application Period: August 2017
- Interviews: July and August 2017
This school offers programs in:
Last updated February 21, 2017