See the unit description on learn@wu (lower section).

After completing this course the student will have the ability to:

In addition, the student will be able to:

* recall advanced concepts of data and text mining

* apply state of the art data and text mining tools to the analysis

  of financial data

Apart from that, the course will contribute to the students' ability to:

* demonstrate effective team skills resulting in an appropriate

  contribution to the production of a group output

* work and communicate effectively in a team situation and to function

  as a valuable and cooperative team member

* participate in group discussions/team work

Moreover, after completing this course the student will have the ability to:

* adequately communicate the results of the application of data and text

  mining tools

* discuss empirical findings in the light of domain knowledge

* use the web to access and extract financial data

In addition, the student will be able to:

* use R for data and text mining

Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

This course is taught as a lecture combined with homework assignments and course projects.  In combination with the lecture, homework assignments and course projects will help students to consolidate and expand their knowledge and understanding by developing solutions to theoretical and applied problems, and have to be submitted via email to the lecturer.  Some course projects will be used for structured presentations and discussions.

Leistung(en) für eine Beurteilung:

    40% home assignment

    40% course projects

    20% presentations

The assessment of the homework assignments and course projects will be based on the correctness of results, the clarity and persuasiveness of each bit of work and the recognizable effort made. This implies an ability to work in teams.

Successful completion of at least 42 ECTS credits from the first year compulsory common courses.

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