University of Wisconsin – Madison

Syllabus: Econ 570 [Fall 2023]
Fundamentals of Data Analytics

Instructional mode: Delivered in person.
Meeting: Mondays and Wednesdays 4:00-5:15, Education L196

Professor: Kim J. Ruhl | ruhl2@wisc.edu
Office hours: Tu 9:00AM-10:00AM & Tu 3:30PM-4:30PM | Soc Sci 7444

Teaching assistant: Satyen Pandita // spandita@wisc.edu
Office hours: M 3:00PM-4:00PM | Soc Sci 6413

Teaching assistant: Mitchell Valdes Bobes // valdsbobes@wisc.edu
Office hours: Th 3:00PM-4:00PM | Soc Sci 7308

Grades and announcements are managed through Canvas.

Course description: This course introduces students to the data that underlie quantitative economic analysis. Students learn how to formulate a research question, access economic data sources, conduct preliminary and formal analysis, and report their findings in a professional manner.

While our focus is on quantitative economic analysis, we will also cover parts of the economist's toolkit such as python and pandas. These tools make accessing and manipulating economic data easier and allow for more powerful analysis than is possible in spreadsheets.

The course culminates in a student-defined research project that brings together the skills students have mastered over the semester.

Learning outcomes

Upon course completion, students will be able to:

  1. Understand the economist's research process: formulating a question; preliminary and formal analysis; and communicating results.
  2. Effectively communicate quantitative economic ideas through figures.
  3. Access and analyze panel datasets, such as the Panel Study of Income Dynamics and time series datasets, such as the National Income and Product Accounts.
  4. Use modern tools to effectively manage data and perform analysis.

Course attributes

Counts as Liberal Arts and Science credit in L&S; Advanced; Social science breadth

Requisites

ECON 310 and ECON 301

Course materials

  1. The course web site is the central clearing house for everything related to the course. Here you will find a week-by-week schedule.
  2. We will use canvas for announcements and to track grades.
  3. I recommend the following three books, but none are required. There are a lot of great resources online, including the documentation and tutorials that accompany the packages themselves.
    1. Python Data Science Handbook, by Jake VanderPlas, is a good general purpose data science book. VanderPlas has made the book available online for free.
    2. Python for Data Analysis (Third edition), by Wes McKinney, provides a more detailed look at the pandas package.
    3. The Visual Display of Quantitative Information by Edward R. Tufte. All of Tufte's writing on visualization is great, but this is the classic reference.

Technology

  1. You will need access to a computer and the internet. We will be writing and debugging code as part of class.
  2. We will be using the Social Science Computing Cooperative's winstat environment to provide a Python installation. You will receive an email with a user name and logon information for winstat shortly before the beginning of the semester.

Number of credits and how credit hours are met by the Course

This three-credit course has two 75-minute lectures per week. Students are expected to work approximately 8 hours per week outside of class to complete assignments and learn the relevant material.

Deliverables and grades

Your final grade is made up of

  1. One student survey and initial winstat logon. This very brief survey collects data on students' math and coding preparation.
  2. Five coding practice assignments. Each assignment is scored as a 'check minus', a 'check,' or a 'check plus.' I drop the lowest assignment and each of the remaining assignments is worth one percentage point in your final grade. Scores of check or check plus receive the full one percent. Scores of check minus receive no credit.
  3. Two in-class exams. The exams are meant to test your understanding of basic coding: The common things that you should be able to do with little assistance from a book or the internet. I will circulate a list of topics before each exam.
  4. A final project. The final project is a chance for you to develop a piece of data analysis that showcases the tools you have learned in class. The project deliverables are 1) a professional report of no more than three pages (1,000 words) that presents the results of your analysis as you would to a client or coworker who is interested in the results, but not necessarily the technical work behind it; and 2) a well-documented Jupyter notebook which lays out the technical details of your analysis. We will discuss the project in much more detail during class.
Deliverable Weight in final grade
Student survey/winstat logon 1%
Best four coding practices 4%
In-class exam 1 20%
In-class exam 2 30%
Project roster 1%
Project proposal 9%
Project 35%

Note that attendance and participation are not part of your final grade. Due dates are posted on the course website. Late assignments will not be accepted.

Policy on generative AI (ChatGPT)

The policy is available at: badgerdata.org/pages/ai

Schedule (subject to revision)

A detailed schedule is available at: badgerdata.org/pages/econ-570

Economics Career Development Office

If you are interested in learning more about careers related to this course or careers for economics majors, you are encouraged to contact the Economics Career Development Office. This office is staffed by economics-specific career advisors who can help you throughout the job/internship exploration and application process. To learn more or make an appointment, visit their website

Misconduct statement

Academic Integrity is critical to maintaining fair and knowledge based learning at UW Madison. Academic dishonesty is a serious violation: it undermines the bonds of trust and honesty between members of our academic community, degrades the value of your degree and defrauds those who may eventually depend upon your knowledge and integrity.

Examples of academic misconduct include, but are not limited to: cheating on an examination (copying from another student's paper, referring to materials on the exam other than those explicitly permitted, continuing to work on an exam after the time has expired, turning in an exam for regrading after making changes to the exam), copying the homework of someone else, submitting for credit work done by someone else, stealing examinations or course materials, tampering with the grade records or with another student's work, or knowingly and intentionally assisting another student in any of the above. Students are reminded that online sources, including anonymous or unattributed ones like Wikipedia, still need to be cited like any other source; and copying from any source without attribution is considered plagiarism.

The Department of Economics will deal with these offenses harshly following UWS14 procedures:

  1. The penalty for misconduct in most cases will be removal from the course and a failing grade,

  2. The department will inform the Dean of Students as required and additional sanctions may be applied.

  3. The department will keep an internal record of misconduct incidents. This information will be made available to teaching faculty writing recommendation letters and to admission offices of the School of Business and Engineering.

If you think you see incidents of misconduct, you should tell your instructor about them, in which case they will take appropriate action and protect your identity. You can also choose to contact our department administrator, Tammy Herbst-Koel (therbst@wisc.edu), and your identity will be kept confidential.

For more information, refer to https://conduct.students.wisc.edu/misconduct/academic-integrity

Grievance procedure statement

The Department of Economics has developed a grievance procedure through which you may register comments or complaints about a course, an instructor, or a teaching assistant. The Department continues to provide a course evaluation each semester in every class. If you wish to make anonymous complaints to an instructor or teaching assistant, the appropriate vehicle is the course evaluation. If you have a disagreement with an instructor or a teaching assistant, we strongly encourage you to try to resolve the dispute with him or her directly. The grievance procedure is designed for situations where neither of these channels is appropriate.

If you wish to file a grievance, you should go to room 7238 Social Science and request a Course Comment Sheet. When completing the comment sheet, you will need to provide a detailed statement that describes what aspects of the course you find unsatisfactory. You will need to sign the sheet and provide your student identification number, your address, and a phone where you can be reached. The Department plans to investigate comments fully and will respond in writing to complaints. Your name, address, phone number, and student ID number will not be revealed to the instructor or teaching assistant involved and will be treated as confidential. The Department needs this information, because it may become necessary for a commenting student to have a meeting with the department chair or a nominee to gather additional information. A name and address are necessary for providing a written response.

Accommodations for students with disabilities

McBurney Disability Resource Center syllabus statement: 'The University of Wisconsin-Madison supports the right of all enrolled students to a full and equal educational opportunity. The Americans with Disabilities Act (ADA), Wisconsin State Statute (36.12), and UW-Madison policy (Faculty Document 1071) require that students with disabilities be reasonably accommodated in instruction and campus life. Reasonable accommodations for students with disabilities is a shared faculty and student responsibility. Students are expected to inform faculty [me] of their need for instructional accommodations by the end of the third week of the semester, or as soon as possible after a disability has been incurred or recognized. Faculty [I], will work either directly with the student [you] or in coordination with the McBurney Center to identify and provide reasonable instructional accommodations. Disability information, including instructional accommodations as part of a student's educational record, is confidential and protected under FERPA.'
http://mcburney.wisc.edu/facstaffother/faculty/syllabus.php

Diversity and inclusion

Institutional statement on diversity: 'Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals.

The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background—people who as students, faculty, and staff serve Wisconsin and the world.' https://diversity.wisc.edu