The idea for the class is to take students through a series of exercises to motivate and illustrate key concepts in Economics with examples in Python Jupyter notebooks. The class will cover concepts from Introductory Economics, MIcroeconomic Theory, Econometrics, Development Economics, Environmental Economics 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 and possible thesis work.
Students should have taken or are currently taking Data 8.
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 on Thursdays from 11am to 12pm in Hearst Field Annex B64. The Connector Assistants hold office hours on Mondays from 2pm to 4pm in TBD. You are welcome to show up to any office hours.
The weekly sessions will consist largely of two portions: a beginning lecture-based portion in which the concepts of the week are laid out, and a second lab-based portion in which the concepts are applied in a group setting.
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. You are required to meet Dr Van Dusen during at least one office hour during semester. We need your feedback on our materials in order to improve them.
If you are unable to attend class, email the instructor to have your absence excused 24 hours before the lecture. Students are allowed 2 excused absences during the semester. If you are unable to attend lecture without notice, please post privately on Piazza so that the course staff are aware.
Weekly homework assignments are a required part of the course. Each student must submit each homework independently, but is allowed to discuss problems with other students and course staff. Assignments will be due Mondays at 11:59 PM See the "Learning Cooperatively" section below.
Data science is about analyzing real-world data sets, and so a series of three projects involving real data are a required part of the course.
Students are allowed to submit assignments late for a 50% penalty until Friday at 11:59 PM, after which they will receive no credit. Scores for assignments will be released Monday nights and the corresponding regrade requests will be due on Wednesdays at 11:59 PM for all assignments.
Grades will be assigned using the following weighted components:
| Activity | Grade |
|---|---|
| Attendance & Participation | 20% |
| Problem Sets | 35% |
| Projects | 45% |
Every assignment is weighted equally in its category. There are 8 homework assignments, of which 7 will be graded. For example, there are 3 projects, so each project is worth $\frac{45}{3} = 15 \%$ of your grade.
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 share 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.