The course provides an introduction to business analytics for students with a background in economics. The following subjects are covered:

1) Regression methods familiar from econometrics classes are reviewed and applied to the analysis of selected business problems.

2) The analyst's toolbox is augmented by new methods such as machine learning and text mining.

3) The new tools and concepts are applied to a range of business problems.

4) In the final three units practitioners present actual business cases and discuss with students the use of data analysis in the private sector.

Students acquire skills

- to adapt econometric models for the analysis of business problems;

- to use machine learning methods and data mining methods;

- to set up a project for analyzing a business problem;

- to apply STATA and R for business analytics;

- to understand the use of business analytics within the context of actual business cases.

The attendance requirement is met if a student takes part in at least 80 percent of classes.

The courses relies upon a mix of teaching methods:

- Instructors present basic concepts and methods.

- Students work on problems to develop further their analytical skills.

- Research papers on business problems are discussed in class.

- Discussions of business cases with practitioners help to further widen and deepen the understanding of business analytics.

Throughout the course the emphasis is on applications of concepts and tools. Students may gain a deeper understanding of statisical foundations in the specialization "Data Science and Machine Learning" offered in the coming fall.

50 %: Coding Project

35 %: Essay on Research Paper

15 %: Class Participation

5 %: Bonus Points - Prediction Contest

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Solid knowledge in econometrics, e.g. as acquired in the course "Econometrics and Empirical Economic Research" is a prerequisite for this course. Knowledge of STATA or R is desirable.

The instructors are available after class. Individual meetings can be arranged upon demand. Email: florian.szuecs@wu.ac.at and ulrich.wohak@wu.ac.at