Being recognized at a great pace from the beginning of 2000s, the concept of data science, the aim of which is to extend the application range of statistics, has become one of the most needed professional branches of the century in recent years.
Data performs a duty of clarifying mechanisms which are not yet well understood or acting as an important instrument in trend analysis. Especially, together with the design of a powerful experiment, it is the basic factor in the emergence of relations and interactions between objects/concepts in social sciences; in the formation of an hypothesis by taking the shape of the results of experiments in natural sciences; in risk assessment, formation of foresight concerning the future, understanding the mechanisms in cause and effect relations by measuring the results appearing as a consequence of the followed up policies in industry/finance/marketing sectors.
Together with the understanding of the importance of data, collection of data presents an indispensable source for most of the sectors for the purposes of work planning, policy-making, realization of foresight, understanding and explaining the existing situation. The optimum usage of the existing source, on the other hand, depends on the richness and deduction power of the statistical and mathematical methods. The design of the experiment that will lead the problem to be solved to the solution, the formation of the data by specifying its source and the calculation of the parameters of the model through statistical methods, availability to do the calculations about the suitability of the aforementioned model, compilation of the data depending on its size necessitate the usage of computers in calculation stages. For that reason, the field of data analysis requires the blending of the statistical and computer sciences for solving concrete problems.
The developments in data science are due to the gradual increase and proliferation of the technical opportunities concerning the access to and synthesis of data. It is possible even with personal computers to apply all kinds of statistical methods on “large” data collections by means of computers whose calculation capacity develop day by day. By this way the applications of the statistical methods on different subjects become widespread and make it possible to solve real world problems through innovative ways in various disciplines. Besides, the enormous size of the data, which already exists and whose generation process is ongoing, foresees the adaptation to the necessities of the epoch by reviewing the statistical analysis, storage, visualization techniques.
Although it is new, the data analysis, which is widely used in developed countries, is ready to offer opportunities that will substantially increase the activities of companies and public institutions in our country as well. It is an indisputable fact that the integration of the data based information to the business plan increases the efficiency and effectiveness. The training of the human source that will realize this, on the other hand, is crucial and urgent.
The aim of the TED University, Applied Data Science Master Program is to train practitioners and researchers who will be able to:
1) know statistical concepts and methods, and especially new machine learning algorithms, which are partially new but have many applications,
2) model, selecting of models and interpret outputs of statistical applications which are proper to the data,
3) design experiments, construct data pipeline, and know how to analyze and visualize data obtained from automated systems,
4) use software such as Python, R, Stata, which are subsidiary for mathematical and statistical problems,
5) individualize and adapt widespread data analysis methods that depend on the data and problem,
6) collect data from the existing sources and construct data bases (Mysql, etc.),
7) know parallel computing methods that enable processes on large data and use related software (spark, hadoop),
8) do the all processes starting from data access and collection up to its analysis.
Due to its interdisciplinary character, the lectures of the program are carried out jointly with the departments of Mathematics, Statistics, Computer Engineering, Business Administration, Industrial Engineering and Economics.
The students who want to gain experience and knowledge in application fields will be able to make research in specific application based graduate level subjects.
The elective courses offered together with the must courses give an opportunity to the students to learn subjects from alternative expertise fields through their interests and choices.
This program is carried out in English.