Marchant calculating machine  By internet archive book images

Marchant calculating machine By internet archive book images

Data Analytics for Economists [Fall 2019]

Monday and Wednesday 4:00PM-5:15PM @ Biochemistry 1120

Professor: Kim J. Ruhl // ruhl2@wisc.edu
Office hours: T 2:30PM-3:30PM & R 10:00AM-11:00AM @ 7444 Social Science Bldg.

Grades and announcements are managed through Canvas.


Data sets and other resources


Weekly schedule

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.  

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. 


Week 0: September 4
Introduction // Using data to communicate ideas

Reading:
Syllabus
Introduction slides

Jupyter notebooks (right click, save link as):
introduction.ipynb [view]

Examples
Election results // college majors
Jobs //gapminder

Due September 6: Student survey
Follow the link to complete the survey. Then log into canvas and complete the assignment.


Reading:
Installing and using winstat
My notes on winstat
Markdown cheatsheet
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"    

Jupyter notebooks (right click, save link as):
notebooks_and_markdown.ipynb [view]
python_basics_1.ipynb [view]

Week 1: September 9 & 11
Winstat // Juypter notebooks // Markdown
Assignment // Calculation // Types // Strings

Examples:
Export maps
 


Reading:
McKinney Ch 3.1 (skim the stuff on tuples and sets, pay close attention to slicing)
McKinney Ch 3.2 pgs 69-74

Jupyter notebooks (right click, save link as):
python_basics_2.ipynb [view]
python_basics_3.ipynb [view]

Week 2: September 16 & 18
Lists // Tuples // Dicts // More on types
Loops // Conditionals


Reading:
McKinney Ch 5 pgs: 123-151

Jupyter notebooks (right click, save link as):
python_basics_4.ipynb [view]
pandas_1.ipynb [view]

Week 3: September 23 & 25
Slicing // Functions //
Pandas:
Series // Dataframes // Indexing
Selecting data // Calculations on dataframes

Due September 27 (midnight): Coding practice #1
Submit through canvas


Reading:
McKinney Ch 9.1 pgs 253-269
McKinney Ch 6.1 pgs 167-176

Jupyter notebooks (right click, save link as):
pandas_2_input.ipynb [view]
matplotlib_1.ipynb [view]

Data:

Week 4: September 30 & October 2
Matplotlib:
Figures and axes // Plot types // Styles
Pandas: Reading and writing files


Reading:
McKinney Ch 11 pgs 319-328, 350, 354, 356-360
Installing packages on Winstat

Jupyter notebooks (right click, save link as):

Data:

Week 5: October 7 & 9
Time series data // APIs

Due October 11 (midnight): Coding practice #2
Submit through canvas


Reading:
Project information
McKinney Ch 8.1 & 8.3 (skip 8.2)

Jupyter notebooks (right click, save link as):

Data:

Week 6: October 14 & 16
Thinking about projects // Bar and scatter plots
MultiIndex // Stack and unstack

October 16: In-class exam #1


Reading:
McKinney Ch 8.2


Jupyter notebooks (right click, save link as):

Data:

Week 7: October 21 & 23
Reshaping // Merge // Join


Reading:
McKinney Ch 7
McKinney Ch 10.1 & 10.2

Jupyter notebooks (right click, save link as):

Data:

Week 8: October 28 & 30
Cleaning data // Transforming data
Groupby

Due November 1 (midnight): Coding practice #3
Submit through canvas


Reading:
Geopandas plot tools

Jupyter notebooks (right click, save link as):

Data:

Week 9: November 4 & 6
Seaborn // Maps

Due November 8 (midnight): Project preproposal
Submit through canvas


Reading:
McKinney Ch 13.1-13.3

Jupyter notebooks (right click, save link as):

Data:

Week 10: November 11 & 13
Maps // Statsmodels // OLS

Due November 15 (midnight): Coding practice #4
Submit through canvas


Reading:

Jupyter notebooks (right click, save link as):

Data:

Week 11: November 18 & 20
Instrumental variables //

November 20: In-class exam #2


Reading:

Jupyter notebooks (right click, save link as):

Week 12: November 25 & 27
Probit & logit // AR(p) // No class on 11/27

Due November 29: Project proposal
Submit through canvas


Reading:
An Introduction to Statistical Learning
Ch: 2.1 & 2.2, except 2.1.5 & 2.2.3
Ch: 5.1 except 5.1.5

Jupyter notebooks (right click, save link as):

Data:

Week 13: December 2 & 4
December 4: No class (project office hours)
Intro to machine learning // cross validation

Due December 6: Coding practice #5
Submit through canvas


Reading:
An Introduction to Statistical Learning, pgs. 214-224

Jupyter notebooks (right click, save link as):

Data:

Week 14: December 9 & 11
December 11: No class (project office hours)
Shrinkage methods // Where to go from here


Due Midnight, December 13: Final project files