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Data Analytics aims to reveal the hidden information
within the data by means of various methods, which
would improve the decisions and the subsequent
actions in order to create value from the data. In this
process, there are various sub-processes such as
business understanding, data understanding, data
preparation, modeling, evaluation and deployment of
the model. Within the scope of this course, metrics and
methods that would be used to validate the models,
supervised learning techniques (i.e., regression and
classification), unsupervised learning techniques (e.g.
clustering, association rule mining, principal
component analysis) and feature engineering and
feature subset selection methods will be discussed and
various use cases in real life applications will be
presented.
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