Course ObjectiveTo build on the knowledge of data science methods and the effective
communication of results from big data analyses into written reports.
Course ContentThe rapid growth of digitalized data and the computer power available to
analyze it has created immense opportunities for econometrics,
statistics and machine learning. This course treats advanced data
science methods. Topics include cross validation, regression trees,
random forest, support vector machines, boosting and bagging. The course
emphasizes on the foundations of the methods, the selection of
appropriate methods and justification of choice, and use of programming
for implementation of the method.
Teaching MethodsEach week, 4 hours lectures and 2 hours tutorials.
Method of AssessmentWritten Exam plus Assignments.
Recommended background knowledgeCourses in Statistics and Econometrics.
|Language of Tuition||English|
|Faculty||School of Business and Economics|
|Course Coordinator||dr. ing. S.A. Fries|
|Examiner||dr. ing. S.A. Fries|
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Lecture, Study Group|
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