This course introduces graduate students of Economics to Data Science and Machine Learning methods and tools. This field course (1637) introduce the concepts. Additional technical aspects and advanced topics are covered in the complimentary seminar (1709) mainly using R.

The focus of both classes is on practical applications of a wide range of useful methods within the field of Data Science.

Both the field course and the seminar will cover the following topics:

• Introduction & Organization
• Data Wrangling and Exploratory Data Analysis
• (Interactive) Data Visualization
• Introduction into Supervised Learning and Cross Validation
• Random Forest and Boosted Regression Trees
• Introduction to Unsupervised Learning
• Mixture Models
• Principle Component Analysis and Factor Models
• Natural Language Processing and Text Classification

After completing this course students will have a “Data Science Toolkit” at their disposal. They will be able to describe, characterize and apply key concepts and methods of data science and machine learning as outlined in the course contents. In addition, students will be able to use statistical software to perform data analysis using data science and machine learning methods.

For this course participation is obligatory. Students are allowed to miss a maximum of 2 units .

The course content is covered and presented in lectures and tutorials. Understanding of the concepts is assessed by two written exams. Students apply data science and machine learning methods covered in the course in a Machine Learning Competition where a dataset is provided and students pre-process and analyze the dataset to come of with a good predictive model. Students present their model and the predictive performance is assessed and compared.

The final grade is composed of: 

• Written Exam 1 (40%)
• Written Exam 2 (40%)
• Machine Learning Competition: Presentation & predictive performance (20%)

Grading scheme:

• > 90%: Excellent
• (80%, 90%]: Good
• (70%, 80%]: Satisfactory
• (60%, 70%]: Sufficient
• [0%, 60%]: Not sufficient

Programming skills on an intermediate level are required (e.g. in R or Python). Although this is an applied class, basic understanding of probability, statistics, linear algebra and calculus is necessary.

For an in-depth coverage of the content it is recommended to attend this course in combination with the Research & Policy Seminar (2329).

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