uwescience/ds4ad

Name: ds4ad

Owner: UW eScience Institute

Description: Data Science for Administrative Data

Created: 2018-04-06 16:00:04.0

Updated: 2018-04-26 09:11:34.0

Pushed: 2018-04-26 09:11:32.0

Homepage: https://uwescience.github.io/ds4ad/

Size: 1553

Language: HTML

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README

Data science for administrative datasets

This course is somewhat based on parts of the Software Carpentry curriculum.

Setup:

• Get a Github account at: https://github.com
• Get an Azure account at: https://notebooks.azure.com

Day 1: Foundations

9 - 10: Introduction (Ariel)

10 - 12: Foundations of programming in Python (Jose)

• Introduction
• Loops
• Storing Values
• Making Choices
• Functions

Noon - 1 PM lunch

1 - 4 : git and GitHub (Bryna)

• Intro to the shell
• Navigating the file system in the shell
• Introducing version control
• Configuring git
• Setting up a repository
• Tracking changes
• Exploring history
• Ignoring things
• Remotes on Github
• Collaborating on Github
• Merge conflicts and conflict resolution

Homework day 1:

Set up your own GitHub project

• Start a repository on GitHub
• In this repository, solve the following code challenges

Code Challenges

• If the variable s refers to a string, then s[0] is the string?s first character and s[-1] is its last. Write a function called outer that returns a string made up of just the first and last characters of its input. A call to your function should look like this:

print(outer('helium'))

• “Adding? two strings produces their concatenation: 'a' + 'b' is 'ab'. Write a function called fence that takes two parameters called original and wrapper and returns a new string that has the wrapper character at the beginning and end of the original. A call to your function should look like this:

print(fence('name', '*'))

e*

Bring a use-case

Tomorrow, tell us about a data use-case that you have in mind for your work:

1. What is the data?
2. What are some questions you would like to answer with these data?
3. How is the data currently stored?

Day 2: Data munging

9 - noon : Introducing Pandas (Bryna)

• Using Libraries
• Reading data from csv files
• Indexing in Pandas DataFrames
• Split-apply-combine with Pandas DataFrames

Noon - 1 PM: lunch

1 - 4 PM: Manipulating data with Pandas (Ariel)

• Introduction
• Filtering
• Combining data
• Record linkage

Homework day 1:

Set up your own GitHub project

Day 3: Data analysis and data visualization

9 - noon : Computations and statistics with Pandas DataFrames (Ariel)

• Introduction
• Arithmetic
• Counting
• Correlations
• Modeling

Noon - 1 PM: lunch

Afternoon:

1 PM - 2:30 Visualizing data with Matplotlib (Jose)

• Introduction
• Customizing
• Plotting from Pandas

2:30 - 4 Next steps – where do we go from here? (Bryna + Jose (+ Ariel))