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 and GitHub
  • Markdown
  • Python Fundamentals
  • Clarity and Documentation

Object-Oriented Python

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

  • Object-Oriented Python
  • Attributes and Methods
  • JSON
  • Logging and Debugging
  • Tables of Objects
  • Pandas, Numpy and Scipy
  • Newton's Method

Dataset Development and Analysis

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

  • Filtering and Selecting
  • Sorting
  • Hierarchical Data
  • Grouping and Aggregating
  • Stacking and Unstacking Data
  • Pivot Tables in Pandas
  • Working with String Data
  • Converting Between Data Types
  • Merging and Joining Tables
  • Handling Missing Data
  • Appending Data to a Table
  • Basics of SQL
  • Database Normalization
  • 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

Basics of GIS

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

  • Vector Layers
  • Shape Files
  • Federal FIPS Codes
  • Census TIGER/Line Files
  • Choropleth Mapping
  • Projections
  • Centroids
  • Filtering and Selecting
  • Clipping
  • Joining Attribute Tables
  • Single Buffers
  • Ring Buffers
  • Spatial Joins
  • Geopackage Files
  • Exporting Results

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
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Peter J Wilcoxen, The Maxwell School, Syracuse University
Revised 07/15/2020