Applied data analysis and visualization 1 for economists

Course goals

Learning objectives
After successfully completing this course, you will be able to:

• Understand and explain the different approaches to data analysis;
• Given a practical data science problem, select appropriate techniques to tackle this problem;
• Apply various (supervised) data analysis techniques, including regression, trees, classification, clustering, etc. in R;
• Implement generic Data Science tools such as train/validation/test sets, crossvalidation, and error evaluation in R ;
• Interpret and evaluate the results of such analyses;
• Explain these evaluations in layman's terms;
• Understand and explain the basic principles of data visualization and the grammar of graphics;
• Construct appropriate visualizations in connection with each of the data analysis techniques in R.

Content

This course is part of the minor Applied data science for Economists, and will be taught in parallel with the course applied data analysis and visualization, part of the UU wide minor Applied data science.  
 
What puts former criminals on the right track? How can we prevent heart disease? Can Twitter predict election outcomes? What does a violent brain look like? How many social classes does 21st century society have? Are hospitals spending too much on health care, or too little?
Data analysis is the art and science of tackling questions like these by looking at data. Just as cartographers make maps to see what a country looks like, data analysts explore the hidden structures of data by creating informative pictures and summarizing relationships among variables. And just as doctors diagnose sick patients and advise healthy ones on how to stay healthy, data analysts predict important events and variables so we can act on this knowledge. Methods from statistics, machine learning, and data mining play an important part in this process, as well as visualizations that allow the analyst and other humans to better understand what we can conclude from the available facts.
 
During this course, you will actively learn how to apply the main statistical methods in data analysis and how to use machine learning algorithms and visualizing techniques. The course has a strongly practical, hands-on focus: rather than focusing on the mathematics and background of the discussed techniques, you will gain hands on experience in using them on real data during the course and interpreting the results.
 
This course covers both classical and modern topics in data analysis and visualization:
1.   Exploratory data analysis (EDA);
2.   Supervised machine learning and statistical learning;
3.   Basic unsupervised learning techniques;
4.   Visualization (throughout the course).
 
Effort requirements
Attendance of at least 80% of the practicals.
 
Language of instruction
English
 
Courses that build on this course
·        Applied data analysis and visualization II
 
Entrance
For UU students only, no exchange students this year.

In case online access is required for this course and you are not in the position to buy the access code, you are advised to contact the course coordinator for an alternative solution. Please note that access codes are not re-usable meaning that codes from second hand books do not work, as well as access codes from books with a different ISBN. Separate or spare codes are usually not available.