Data Exploration & Visualization

(DSC 385)

Request Info

In Data Exploration, Visualization, and Foundations of Unsupervised Learning, students will learn how to visualize data sets and how to reason about and communicate with data visualizations. Students will also learn how to assess data quality and providence, how to compile analyses and visualizations into reports, and how to make the reports reproducible. A substantial component of this class will be dedicated to learning how to program in R.

What You Will Learn

  • Data visualization
  • R programming
  • Reproducibility
  • Data quality and relevance
  • Data ethics and providence
  • Dimension reduction
  • Clustering

Syllabus

  • Introduction, reproducible workflows
  • Aesthetic mappings
  • Telling a story
  • Visualizing amounts
  • Coordinate systems and axes
  • Visualizing distributions I
  • Visualizing distributions II
  • Color scales
  • Data wrangling 1
  • Data wrangling 2
  • Visualizing proportions
  • Getting to know your data 1: Data providence
  • Getting to know your data 2: Data quality and relevance
  • Getting things into the right order
  • Figure design
  • Color spaces, color vision deficiency
  • Functions and functional programming
  • Visualizing trends
  • Working with models
  • Visualizing uncertainty
  • Dimension reduction 1
  • Dimension reduction 2
  • Clustering 1
  • Clustering 2
  • Data ethics
  • Visualizing geospatial data
  • Redundant coding, text annotations
  • Interactive plots
  • Over-plotting
  • Compound figures

Estimated Effort

10-12 Hours/week

Course Availability

  • Spring 2022

Meet Your Instructor

Take the Next Step

Advance your career with UT Austin's Master of Data Science Online.