Course ObjectiveThe student learns how to translate their econometric, data science and
numerical skills in solving real-life data problems and developing
practical solutions by working in a team. Given the limited number of
days available for the work for each case, the students also learn how
to work in a group, in an efficient and organized way.
Course ContentThis course presents a series of practical methods for regression and
forecasting under circumstances where much data need to be handled
quickly. At the start of each week, the basic methods are introduced and
a Case Study is presented. Groups of 3-4 students are working on a
practical case during the week with one or two follow-up meetings. Some
data sets may be provided by a company or organization. The background
and relevance of each Case Study are also provided. Support and guidance
are offered and deliverables are clearly formulated. A short report need
to be delivered at the end of the week. Three Case Studies are carried
in three weeks. A short exam is held in the last week, the fourth week.
Teaching MethodsOne part of this course consists of lectures where new methods and
techniques are introduced. In the other part we let students work in
groups to implement the practical cases. Each week starts with a
two-hour lecture, followed by one or two tutorial meetings with Q&As, to
help and assist the groups in their work. In the last week, a short
written exam takes place.
Method of AssessmentWritten exam (individual) plus a report (groups).
Explanation CanvasFurther information and all communications: see Canvas page.
Recommended background knowledgeStatistics and Econometrics I
|Language of Tuition||English|
|Faculty||School of Business and Economics|
|Course Coordinator||prof. dr. S.J. Koopman|
|Examiner||prof. dr. S.J. Koopman|
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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