From the rise of the Internet to this day, the Web has evolved from being a one-way communication environment to many-to-many communication and collaboration environment. A series of transformations has led to today’s Web. In the 1990s, the Web served lots of static HTML pages created by a small set of people at select institutions and news agencies. From the beginning of 21st century, the number of contributors and the amount of information has skyrocketed with the rise of platforms that enable rapid collaboration and personal contribution. With so much data around, we are riding a wave of the Web in-transit from version 2.0 to its next version, Web 3.0. The premise of the newest version of the Web is the welcoming of machines understanding, generating, and consuming information just like any of us can do now.
From 2005 to 2010, the digital universe grew from 130 exabytes (EB) to 800 EB. The digital universe will double every two years from now till 2020. In 2020, it will be 40,000 EB, i.e., 40 trillion gigabytes, which is more than 5,200 gigabytes for every man, woman, and child in 2020. In almost every subsystem currently in use, there is a cyclical process that starts with the (i) acquisition of raw data.
It is followed by (ii) processing and transforming of this raw data into information so that we can (iii) drive new insights. With more insights, we are better equipped to (iv) make new and informed decisions. These are the decisions that determine whether customers buy what they need, producers design the right products, city officials deploy the right solutions for bettering urban life, our crops and fields return better yields, and whether we live better. With data playing a central role in advancing civilization, it is appropriate to say that “the data has become the new oil”. The 4-step cyclical process we described is shaped by Data Science. The idea is to find interesting ways to visualize and present raw data in such a way that enables rapid insight discovery. Instead of verbose text and raw data, vivid visuals are more powerful and useful in the process of deriving new and actionable insights.
Data scientists are inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes. Rather than looking at data from a single source, a data scientist will explore and examine data from multiple disparate sources. The data scientist will sift through all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address a pressing business problem. A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data. Armed with data and analytical results, a toptier data scientist will then communicate informed conclusions and recommendations across the organization.
From clustering and regression to classification and probabilistic inference, and to data enrichment and visualization, data scientists need to have a solid foundation in computer science and applications, modeling, statistics, analytics, and mathematics. In order to explore exabytes of data and do “what-if” analysis, data scientists require powerful back-end systems (data science platforms) to crunch raw data. Furthermore, the platforms have to provide an interactive mode of data analysis, which is required due to the iterative and inquisitive nature of performing data science.
In order to fully grasp the opportunities present in the current environment awash with data, we designed a targeted graduate program for Data Science. Our institutional partnership with IBM will help us better this new graduate program going forward. Besides systems and tooling support, enhancing the class experience with guest lectures from IBM personnel expert in the areas pertinent to course content will make sure that practical implications and real problems to solve will always be thought of and taught up-front in the academia.
MS PROGRAM in DATA SCIENCE (DS)
The maximum duration of study in the program with thesis option is six semesters. The students are required to finish their coursework in the first four semesters. Their registration will be canceled otherwise.
The maximum duration of study in the programs without thesis option is three semesters.
- You are required to complete at least 90 ECTS to graduate from the programs without thesis option and at least 120 ECTS from the program with thesis option.
ADMISSION REQUIREMENTS AND SCHOLARSHIPS
GPA, GRE/ALES scores, TOEFL scores; letters of references; and statement of purpose letters are all taken into consideration during the assessment of applicants for admission.
A limited number of scholarships are offered through a merit-based assessment of all applicants.
Scholarships change from a partial tuition-wavers to a full scholarship including a monthly stipend, housing & meal support and health insurance.
Students can obtain additional support as research assistants in sponsored research projects.
Educational Mission of the Masters Program in Data Science
Masters Program in Data Science (MS in DS) accepts students with a BS degree in Engineering or Natural Sciences. A select set of courses address the following two aspects:
- How to apply scientific methods, scientific approaches, and problem-solving techniques on big data (in volume, variety, and velocity) problems are taught.
- How to use data engineering tools for coming up with added-value services and products are demonstrated and proven.
While advancing their knowledge in topics related to science, mathematics, and engineering, students learn how to transform raw data into new information and to new knowledge and understanding. They learn how to apply their learned skills to problems faced in the industry.
Students learn how to conduct research on their own, and how to learn new skills for their careers and their life-long learning. Students apply data-driven processes and skills in their everyday life for a more productive life experience.
Educational Objectives of MS in DS Program
Graduates of the DS program will:
A. Engage in professional practice in academia, industry, or government;
B. Promote innovation in the design, research and implementation of products and services in the field of DS through strong communication, leadership and entrepreneurial skills;
C. Engage in life-long learning in the field of DS.
Note: Program educational objectives are those aspects of engineering that help shape the curriculum; achievement of these objectives is a shared responsibility between the student and İstanbul Şehir University.
Program Outcomes in MS in DS Program
Graduates of the DS program will have:
- An ability to apply knowledge of mathematics, science, and engineering,
- An ability to design and conduct experiments, as well as to analyze and interpret data,
- An ability to design a data-driven system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety,
- An ability to function on multidisciplinary teams,
- An ability to identify, formulate, and solve data engineering problems,
- An understanding of professional and ethical responsibility,
- An ability to communicate effectively,
- The broad education necessary to understand the impact of data engineering solutions in a global, economic, environmental, and societal context,
- A recognition of the need for, and an ability to engage in life-long learning,
- A knowledge of contemporary issues,
- An ability to use the techniques, skills, and modern engineering tools necessary for data engineering practice.
This school offers programs in:
Last updated February 8, 2018