The aim of this course is to familiarize incoming and current Wharton PhD students with the basic technical skills and tools required for empirical research. This includes publicly available and open source tools (e.g., AWS, Python, R) and Wharton-specific resources (e.g., Wharton grid computing cluster, WRDS). The course is primarily concerned with acquiring, cleaning, managing, and analyzing data. It will provide hands-on experience using variety of computing tools, including intro-level machine learning and natural language processing techniques. At the end of this short-term course, students will have a better understanding of what tools are most appropriate for different data analysis tasks at hand.
There is no prerequisite for this course. Feel free to attend the sessions selectively. Auditing is welcome. The format will be roughly a 60-min lecture followed by a 30-min lab session, where you are encouraged to work on exercises. There is no exam. Please bring your own laptop for this course.
All the notes and slides for the course can be downloaded at the course’s repository on GitHub.
Mon 31 July, Room F55
Wed 2 Aug, Room F55
Fri 4 Aug, Room F55
Mon 7 Aug, Room F70
Wed 9 Aug, Room F70
Fri 11 Aug, Room F70
Mon 14 Aug, Room F70
Wed 16 Aug, Room F70