Data Analytics for Economists [Fall 2018]
Monday and Wednesday 4:00PM-5:15PM @ Biochemistry 1120
Teaching Assistant: Dennis McWeeny
Office hours: M 9:30AM-11:30AM @ Sco Sci 6470
Professor: Kim J. Ruhl
Office hours: T&R 2:30PM-3:30PM @ Soc Sci 7444
August 14, 2018Welcome! This page is a work in progress; more coming soon. I am looking forward to meeting all of you, but until then, here are a few things you can do before we get started to smooth your transition into class.
- You will need a laptop. This is a hands-on course: we will be writing and debugging code together during class periods. If you do not have access to a laptop, please contact me immediately and we can work something out.
- You will need to be connected to the internet during class. Make sure your UWNet login is working.
- This course is about using data to answer questions. Start looking at some examples of people doing exactly this. FiveThirtyEight is a nice read, as is The Economist. On Twitter, check out @nytgraphics.
This is the big-picture. We will add a lot more detail as we progress. We will also dip in and out of topics as we go. For example, we will do some basic plotting throughout the entire course, but we will not dig into the details until later.
In the readings, McKinney refers to the book Python for Data Analysis (Second edition) by Wes McKinney. There is a lot more in this book than we will cover, but it is a good reference.
McKinney, parts of chapter 2
Using data to answer questions
Working on Winstat
Creating, opening, and saving our work
Documenting our work
McKinney, parts of chapters 2 and 3
Syntax, assignment, calculation
Data structures, types
Mckinney, parts of chapters 5-8
Working with data:
The pandas package
Loading and cleaning data, missing values
Merging and joining data sets
McKinney, parts of chapter 9
The matplotlib package
Scatter, bar, and line plots
Labeling and annotating plots
This will depend on timing, but we might cover map visualizations, time series analysis, web APIs, machine learning basics, web scraping, formal econometrics...other ideas?
This week-by-week schedule will be constantly updated. Some topics may take longer than scheduled (and others may take less) but exam, coding practice, and project due dates will not.
Installing and using winstat
McKinney Ch. 2.2 parts: "Running the Jupyter Notebook," "Tab Completion," "Introspection"
McKinney Ch. 2.3 up to "Control Flow": Everything except "Duck Typing" and "Bytes and Unicode"
Week 1: September 10 & 12
Winstat // Juypter notebooks // Markdown
Assignment // Calculation // Types // Strings
Week 2: September 17 & 19
Lists // Tuples // Dicts // More on types
Loops // Slicing // Conditionals // Functions
McKinney Ch 5 pgs: 123-151
Jupyter notebooks (right click, save link as):
Week 3: September 24 & 26
Pandas: Series // Dataframes // Indexing
Selecting data // Calculations on dataframes
Due September 24: Coding practice #1
Print out and bring to class
Week 4: October 1 & 3
Week 5: October 8 & 10
Due October 8: Coding practice #2
Week 6: October 15 & 17
Due 1:00PM, October 15: Exam #1
Week 7: October 22 & 24
Week 8: October 29 & 31
Due October 29: Coding practice #3
Week 9: November 5 & 7
Week 10: November 12 & 14
Due November 12: Coding practice #4
Week 11: November 19 & 21
Due 1:00PM, November 19: Exam #2
Week 12: November 26 & 28
Due November 26: Project proposal
Week 13: December 3 & 5
Week 14: December 10 & 12
Due December 10: Coding practice #5
Due Midnight, December 14: Final project files