The field of Data Science is a new field of research created from the integration of existing fields of research, while creating common principles for dealing with large-scale data. The attached diagram describes the existing research fields in the world, whose combination forms the new field of data science and information science. The Technion’s Faculty of Industrial Engineering and Management has world-class research in all these areas. Three leading examples are: 1) Information retrieval and search engine, a field that has been researched since the 1940s. 2) Natural language processing area 3) Algorithmic game theory (EC) field.
In light of the ongoing increase in data volumes in the world and the wide range of data-based applications in medicine, social media, finance, urban planning, smart cities and more, there is a growing need for researchers in data science and information. These researchers will require the skills to develop scientific solutions to the various challenges involved in working with large and varied amounts of data that change frequently, with varying degrees of certainty, and in a variety of applications and fields of knowledge.
The Program in Data Science places the emphasis on experimenting with research methods in the scientific and technological fields dealing with the collection, management, analysis and presentation of big data. Research relies on knowledge in math, computer science, performance research, statistics, computational learning, psychology, and more.
Graduates of the program will be able to integrate in academic and industrial research and development activities, utilizing the knowledge and research skills they have developed during their participation in the program. During the course of the study, the student will be able to develop new principles and new methods in the treatment of Big Data. The student in the program is required to have high-level analytical capabilities and come with a solid knowledge infrastructure in statistics and machine learning, software engineering and algorithms. Ideally, familiarization with these areas is done within the qualification studies (for example, the bachelor’s degree in data engineering and information).
During the studies, advanced courses and subjects in data science will be given, as well as subjects with research emphasis that will be dedicated to acquaintance with the cutting edge knowledge in the field. In particular, courses are offered in areas where the faculty is conducting research in which students can integrate. Therefore, in many cases, these courses and subjects will form the foundations for forthcoming research work.
To complete their master’s degree, students who have graduated from a four-year undergraduate program are required to complete graduate-level courses for a total of 20 academic credits, including core courses in statistics, operations research, machine learning, algorithms, game theory, and programming big data systems. In addition, these graduates are required to complete a research project including a thesis.
Graduates of three-year degree programs are required to gain a total of 30 academic credits, of which 10 can be from advanced undergraduate-level courses.
The degree program provides practical experience in research methods in data and information science, and equips students with development skills and with experience in the development and use of tools and principles for data collection, management, analysis, and presentation.
Supplementary Course Requirements:
As part of the admissions process, a list of supplementary courses will be set for each student as necessary. Students required to complete supplementary courses (“supplementary students”) must achieve a grade of at least 78 in each subject and an overall average grade of at least 80 in all their supplementary studies in order to gain full student status in the program.
Core Courses, Required Courses, and Elective Courses (20 credits)
The program of study places an emphasis on courses in core areas of statistics and probability, machine learning, optimization, game theory, and algorithms, and on data-rich courses. Students must select one course from each course list, with the exception of the list of data courses, from which two courses must be selected as mandatory courses. It is recommended that students select additional courses from the course lists as elective courses, as detailed below. The program director has the authority to permit students to take a different combination of courses, as long as at least two course are taken from the list of data courses.
List of Courses:
Program graduates will be required to display the ability to use and develop tools for data collection, management, and analysis. These skills are usually gained during students’ work on their thesis or via active participation in relevant laboratory courses. In particular, this requirement can be completed by (a) completing a project as part of the thesis; (b) taking data courses and gaining approval based on projects in these courses; (c) completing a project in industry; (d) participating in “datathon” competitions; or (e) completing a data project under individual supervision.
As a condition for completing their degree, students must receive confirmation of their data skills from the head of the program, authorizing that the tasks completed by the student meet the requirements.
The main component of the master’s degree program is the completion of a research study and thesis, carrying 20 academic credits. The thesis should be submitted around 24 months after beginning the program. In accordance with the regulations of the Technion Graduate School, permission may be granted to complete a final project carrying 12 credits instead of a research study or research project. In these special cases in which a final project is approved, the student will be required to complete additional courses, subject to their supervisor’s approval, for a total of 8 credits.
Following admission to the program, each student will have a temporary supervisor appointed, who will be one of the directors of the data science and engineering program.