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The idea for the class is to take students through a series of exercises to motivate and illustrate key concepts in economics with using empirical data and data science techniques. The class will cover concepts from Introductory Economics, Microeconomic Theory, Econometrics, Development Economics, Macroeconomics, and Public Economics. The course will give data science students a pathway to apply Python programming and data science concepts within the discipline of economics. The course will also give economics students a pathway to apply programming to reinforce fundamental concepts and to advance the level of study in upper division coursework, research, and possible thesis work.
You must have taken Data 8 or be currently enrolled in Data 8 to take this course. That being said, we are able to make exceptions if you have prior programming or data science experience; please email the course staff if you have any questions. Prior economics knowledge may be helpful but is not necessary.
You are not alone in this course; the staff and instructors are here to support you as you learn the material. It's expected that some aspects of the course will take time to master, and the best way to master challenging material is to ask questions. For online questions, use Piazza. We will also hold office hours for in-person discussions.
Dr. Van Dusen holds office hours by appointment; please reach out to him at ericvd@berkeley.edu to schedule one. The course staff will hold office hours on Fridays from 9 AM to 11 AM PDT via Zoom. Connector assistants will also hold office hours; see @42 on Piazza for more information. You are welcome to show up to any office hours.
The weekly sessions will consist generally of two portions: a lecture-based portion in which the concepts of the week are laid out, and a lab-based portion in which the concepts are applied in a small-group setting.
We do not expect you to complete the lab in class; you are responsible for the completion of the lab in your own time and will take the place of homework assignments. Unlike Data 8, labs will be graded on accuracy and not just completion.
The class will be run as much like a seminar as a regular class. Your participation is necessary to make this work. We will be expecting you to discuss during class, participate on Piazza, and come to Office Hours. We need your feedback on our materials in order to improve them.
Attendance will be factored into your grade. There are two ways of earning attendance credit: attend the synchronous lecture Zoom call or watch the asynchronous recording and take a short Gradescope quiz. The quizzes will be 3 to 5 multiple choice questions in length and a minimum score of 80% is required to earn the attendance credit. All students will attempt the lecture quiz, but students who attend synchronous lecture will automatically be given a 100% as long as they complete the quiz.
Grades will be assigned using the following weighted components:
| Activity | Grade |
|---|---|
| Tests | 25% |
| Labs | 30% |
| Projects | 45% |
Labs will be released in lecture and due on Fridays. They will be graded on accuracy but your lowest two scores will be dropped. Projects are released after lectures and are due on the second Monday after being released. There are 12 labs and 5 projects. Labs and projects are weighted equally in their categories. For example, there are 5 projects, so each project is worth $\frac{45}{5} = 9 \%$ of your grade.
There will be two 1-hour tests that are delivered asynchronously. The first will be released on Oct 6 and due on Oct 9. The other will take place during finals week. These tests will be weighted such that the one you score better on will be worth 15% of your grade and the other 10%. These tests will only be cumulative insofar as the second half of the course builds on the first half, but the second test will not explicitly cover material from the first half.
Attendance will be taken in class and will serve as a grading boundary for the course. The attendance quizzes, for those not attending synchronous lecture, will be due on Wednesdays. While attendance will not factor into your percentage score, we will use attendance as a boundary for assinging grades. The table below describes the minimum number of lectures required for a grade. There are 13 lectures total.
| Grade | Lectures |
|---|---|
| A- or greater | 12 |
| B- or greater | 11 |
| C or greater (a passing grade) | 10 |
Attendance is a necessary, but not sufficient, condition for passing.
Students are allowed to submit projects late for a 50% penalty until the Wednesday after it is due at 11:59 PM, after which they will receive no credit. Labs may be submitted late for a 50% penalty until the Monday after it is due at 11:59 PM. When scores for assignments are released, regrade windows will be open for two days.
We encourage you to discuss course content with your friends and classmates as you are working on your weekly assignments. No matter what your academic background, you will definitely learn more in this class if you work with others than if you do not. Ask questions, answer questions, and share ideas liberally.
You must write your answers in your own words, and you must not plagiarize your completed work.
Make a serious attempt at every assignment yourself. If you get stuck, read the supporting code and lab discussion. After that, go ahead and discuss any remaining doubts with others, especially the course staff. That way you will get the most out of the discussion.
You are also not permitted to turn in answers or code that you have obtained from others. Not only is such copying dishonest, it misses the point of the assignments, which is not for you to find the answers somewhere and send them along to the staff. It is for you to figure out how to solve the problems, with the support available in the course.
Please read Berkeley's Code of Conduct carefully. Penalties for cheating at UC Berkeley are severe and include reporting to the Center for Student Conduct. They might also include a F in the course or even dismissal from the university. It's just not worth it.
Go on Piazza and discuss with other students or the CAs. We expect that you will work with integrity and with respect for other members of the class, just as the course staff will work with integrity and with respect for you.