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Econ8320 -- Tools for Data Analysis

Days and Times: TBA
Classroom: TBA

The course will cover basic principles of programming languages, as well as libraries useful in collecting, cleaning and analyzing data in order to answer research questions. e course will utilize basic Economic principles and Econo- metric methods as inspiration for assignments and projects throughout the duration of the course, and will do so in a way that is accessible to non-Economists. This course is intended to introduce the student to the Python programming language as a tool for conducting data analysis. While the course uses Python, the student should be able to move to other languages frequently used in data analysis using the principles taught in this course.

Office Hours

TBA

Materials

Grading

This course will be graded as follows:

  • 400 points of your grade will be based on the assignments that make up lab.
  • 100 points will be based on your participation in class discussions.
  • 250 points will be based on an in-class, two day midterm project/presentation. More details will be provided in class
  • 250 points will be based on an in-class, two day final project/presentation. More details will be provided in class
Final grades will be based on the total points you earn, and distributed according to the following scale.
Letter Percent
A 940-1000
A- 900-939
B+ 870 - 899
B 840 - 869
B- 800-839
C+ 770-799
C 740-769
C- 700-739
D+ 660-699
D 600-659
F < 600

Projects

The exams in this course will be two projects, and will be completed outside of class. The best way to learn is to do, and so we will focus on using the tools we learn in class through applied problems and exercises. These projects will make up half of your grade (alongside lab work) and will depend heavily on teamwork, so please make sure that you schedule time to remain for all of class each week. These projects must be done as part of a group.

Course Schedule

Week 1 - Data types and documentation

Week 2 - Conditions, loops and functions

Week 3 - Classes

Week 4 - Factoring and debugging

Week 5 - Regular expression

Week 6 - Pandas and Dataframes

Week 7 - Web Scraping

Week 8 - Web APIs

Week 9 - Numpy and numeric calculations

Week 10 - Scipy and optimization

Week 11 - Plotting

Week 12 - Natural language processing (NLP)

Week 13 - Statistical modeling

Week 14 - Multiprocessing

Weeks 15 and 16 - Project Presentations

ACADEMIC INTEGRITY

UNO’s requirements for Academic Integrity and Behavior All students are required to adhere to the highest standards of academic integrity and behavior and must satisfy the UNO Academic Integrity Policy http://www.unomaha.edu/student-life/student-conduct-and-community-standards/policies/academic-integrity.php and Student Code of Conduct http://www.unomaha.edu/student-life/student-conduct-and-community-standards/policies/code-of-conduct.php. It is the student’s responsibility to read, understand and abide by these policies.

If I find that you have plagiarized, been dishonest in completing your assignments, or cheated an an exam or assignment, then I reserve the right to award you no points on the entire exam, project, or assignment and to report the behavior to the university. If this behavior is repeated, I reserve the right to award a failing grade, independent of your score on other assignments. Academic integrity is essential to education, and I take it very seriously.

Using AI models such as ChatGPT to generate or clean or comment code without acknowledging their use is equivalent to plagiarism. These are legitimate tools and may be used, as long as you make it clear when and how they were used. Just remember, they can't replace a working knowledge of programming. If you let them solve all the problems, you have wasted your time. If you fail to cite AI coding models when you are using them (I can usually tell when you do), this is the same as cheating and will result in a failing grade.

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