Description:

The course focuses on data analysis of (large) datasets. It explores methods for data visualization and exploratory data analysis. The course also covers data transformation and basic hypothesis testing. Finally, we will cover the fundamentals of machine learning (logistic regression; regularization and shrinkage; classification techniques such as k-nearest neighbor, Naive Bayes, Decision Trees; methods to evaluate and improve performance of machine learning algorithms). 

Learning Targets:

Upon successful completion of this module, students will be able to:
• Conduct, understand, and evaluate data analyses independently,
• Perform data manipulations, critically examine them, and understand them,
• Independently answer empirical questions using appropriate datasets. 

Outline:

1. R Basics
2. Data visualization
3. Exploratory data analysis
4. Data transformation
5. Linear regression
6. Introduction to machine learning

Literature:

1. Wickham, Hadley & Grolemund, Garrett (2017): R for Data Science. O’Reilly, Beijing.
2. Lander, Jared P. (2017): R for Everyone, 2nd ed. Addison-Wesley, Boston.
3. Nwanganga, Fred & Chapple, Mike (2020): Practical Machine Learning in R, Wiley, Indianapolis.

Material:

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