PAI 789 Advanced Policy Analysis > Course Outline

Computational Methods and Tools

This strand of the course broadly focuses on developing computational skills and experience in data analytics. It places particular emphasis on practices that produce clear, insightful, well-documented, and easily-reproducible analysis. It will cover some or all of the topics below depending on the interests of the class and the pace of the semester. Also, please note that the order of the topics will differ a bit from the list below: the visualization material, for example, will be scattered around through the second part of the semester.

Fundamental Tools and Practices

Core tools and techniques that will be used during the semester.

  • Version Control
  • Git, GitHub and GitHub Desktop
  • Markdown
  • Spyder and Vistual Studio Code
  • Python Fundamentals

Object-Oriented Python

A deeper dive into Python programming plus computational applications related to analyzing and visualizing uncertain variables.

  • Coding for Clarity and Documentation
  • Object-Oriented Python
  • Attributes and Methods
  • JSON
  • Pandas, Numpy and Scipy
  • Logging and Debugging
  • Tables of Objects
  • Newton's Method
  • Random Number Generation
  • Monte Carlo Analysis
  • Computing Confidence Intervals and Ellipsoids

Dataset Development and Analysis

The real workhorse part of the course: building, managing, and analyzing large or complex datasets.

  • Grouping and Aggregating
  • Merging and Joining Datasets
  • Filtering and Selecting
  • Appending to Datasets
  • Sorting
  • Hierarchical Data
  • Stacking, Unstacking and Pivoting Data
  • Bytes, Strings, Character Encoding, and Unicode
  • Working with String Data
  • Regular Expressions
  • Converting Between Data Types
  • Handling Missing Data
  • Basics of SQL
  • Data Storage Considerations
  • Working with Time Series Data
  • Working with APIs
  • Using Postman to Test API Queries
  • Web Scraping using Beautiful Soup
  • Scanning Very Large Files
  • Decompositions
  • Dissimilarity Indexes
  • Handling Directories and Files
  • Estimation

Basics of GIS

Using geographic information systems to visualize or analyze data with a spatial component.

  • QGIS
  • Geopandas
  • Vector Layers
  • Shape and Geopackage Files
  • Federal FIPS Codes
  • Census TIGER/Line Files
  • Choropleth Mapping
  • Projections
  • Centroids
  • Filtering and Selecting
  • Clipping and Dissolving
  • Joining Attribute Tables
  • Single Buffers
  • Ring Buffers
  • Spatial Joins
  • Exporting Results
  • Geocoding
  • Minimum Distance Calculations
  • Voronoi Polygons

Data Visualization

An introduction to a range of tools for visualizing large datasets.

  • Matplotlib
  • Seaborn
  • Box, Boxen, and Violin Plots
  • Category and Bar Plots
  • Histograms and Density Plots
  • Hex Plots, Heat Maps, and Contour Plots
  • Line Plots
  • Multi-Panel Plots
  • Plotting Relationships with Facet Plots
  • Regression Plots
  • Mosaic and Treemap Plots
  • Strip and Ridge Plots
  • Waterfall Plots
  • Tableau
Site Index | Zoom | Admin
 URL: https://wilcoxen.maxwell.insightworks.com/pages/4941.html
Peter J Wilcoxen, The Maxwell School, Syracuse University
 Revised 07/18/2023