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When & Where
Date: 
Fri, September 6, 2019 - 9:00 AM to 12:00 PM
Location: 
Barrows 356B: D-Lab Convening Room
Description
Type: 

This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

Part 2 Topics:

  • Lists
  • Loops
  • Conditionals
  • Functions
  • Scope

Knowledge requirements: Python Fundamentals: Part 1 or equivalent prior knowledge

Registration note: To participate in multiple parts of this series, please be sure to register for each day separately.

Technology requirements: 

  • Laptop required. 
  • WindowsWindows machines please download and go through the installer for GitBash. Then download Anaconda. Click the download for Python 3.6. Run the installer.
  • Mac Mac machines please download Anaconda. Click the download for Python 3.6 and the Graphical Installer. Run the installer.
  • Please also download the Github repository with the materials for this workshop. Click here, click on the green button labeled "Clone or download", and select "Download ZIP." Then unzip the downloaded file and place the folder on your desktop.
Primary Tool: 
Python
Details
Training Host: 
D-lab Facilitator: 
Evan Muzzall
Format Detail: 
hands-on, interactive
Participant Technology Requirement: 
Laptop

Basic Competency

These workshops are designed for participants with beginner fluency. They already have a little coding, tool or method experience but need to learn more intermediate applications such as conditional subsetting and appropriate data visualizations for their research. 

Examples: Introduction to Pandas, R-wrang, Data Visualization with Python, R-graphics, Survey Sampling, Weighting Data, Introduction to Qualtrics, Finding Health Statistics and Data, Data Viz Theory and Best Practices, Python Machine Learning, Machine Learning in R, Intro to Computational Text Analysis, Geospatial Fundamentals in Python/sf/QGIS/ArcGIS, Intermediate Tableau

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