Econ8320 -- Tools for Data Analysis
Days and Times: 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.
- Python Lectures from [Quant-Econ.net](Quant-Econ.net)
- Course slides from [Github](https://github.com/dustywhite7/Econ8320)
- [Data Science from Scratch](http://shop.oreilly.com/product/0636920033400.do) (ISBN: 978-1-4919-0142-7)
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
|B+||870 - 899|
|B||840 - 869|
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.
Data Types and Documentation --
Classes, Part 1 --
Classes, Part 2 --
Numpy and Scipy, Part 1 --
Numpy and Scipy, Part 2 --
Pandas (as well as pandasql and sqlite3) --
Plotting Libraries (matplotlib, bokeh, plotly) --
Statsmodels (regression analysis) --
Scikit-Learn (machine learning) --
Regular Expression (regex) --
Scrapy, JSON --
Geolocation API's --
Twitter API's --
Dash (web applications) --
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.