73-265: Economics & Data ScienceProfessor John GasperFall 2018Updated: August 27, 2018Course Time / Location:Tues/Thur 3:00-4:20pm;Room: TPR 2612Instructor: John GasperOffice: TPR 4202Email: [email protected]Office Hours: Tues/Thur: 4:30-5:30pm; Wednesday 2 - 3pm; by appointmentIn general, I have an open door policy: if my door is open and I’m not meeting withsomeone, you are welcome to come in and meet with me.If my door is closed, I am notavailable (out of the office, working, etc).I know finding office hour times that work foreveryone is difficult. Consider these a suggested meeting time, but I highly encourage you toschedule an appointment. Please, don’t interpret a closed door as me not wanting to meetwith you. I do, but just send me an email to schedule some time.Teaching Assistants: Mason PaccioneOffice hours: TBARequired materials:•Clicker(either the device or app for your phone)•R for Data Science, Grolemund and Wickham•Storytelling with Data, Knaflicwebsite for book•Mastering Metrics: the Path from Cause to Effect, Angrist and PischkeSuggested Supplemental Texts:•Introductory Econometrics: A Modern Approach, Wooldridge.•Mostly Harmless Econometrics: An Empiricist’s Companion, Angrist and Pischke1
Course Description and GoalsThis course is at the intersection of economic analysis, computing, and statistics. It developsfoundational skills in these areas and provides students with hands-on experience in identi-fying, analyzing, and using data to solve real-world problems in economics and business.In this course we will be using the statistical programming language R, which is a pow-erful open-source language that is widely used. In this very hands-on, data-centric, course,students will learn the basics of data manipulation, how to visualize, present and interpretdata related to economic and business activity by employing statistical analysis and variousvisualization techniques.Through many interactive exercises, students will develop a foundation for data-drivendecision making. This course also provides a solid base for future courses such as Econo-metrics I and II (73-274/275) that provide a rigorous treatment of more advanced methodsused for business, economics, and public policy. The exact data sets we will use will varyand depend on student interest, but the underlying objectives of the course will remain:•Develop competence in the statistical programming language R and manipulating data.•Develop competence in visualizing data using R.•Perform various statistical analyses used in economic analysis•Interpret the estimates from these statistical analyses.•Develop communication skills to effectively describe and visualize findings for technicaland non-technical audiences.