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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. Learn more in the Applied Data Science programme at TED University.
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