Data visualization

Motivating questions:

What makes a data graphic better or worse? What choices do data graphic designers make, and how do they make them? How do you create good data graphics? What are the principles of data graphic design? How are those principles reflected in code?

Learning goals:
  • To make informative, appropriate, and compelling data graphics
  • To recognize potentially problematic ethical issues in data science
Standards:
  • Slide decks 14
  • Screencasts 12
  • Lectures 7
  • Labs 5
  • Book chapters 4
  • Mini-project 1

Weekend, Jan 9–10

Data wrangling

Motivating questions:

How can we transform data from one arrangement to another? What are the basic set of operations that are common to many kinds of data transformation? Are there theories that govern the process by which data is transformed?

Learning goals:
  • To further sharpen data wrangling and data visualization skills
  • To learn how to collaborate using modern workflows
Standards:
  • Screencasts 8
  • Slide decks 8
  • Lectures 7
  • Labs 5
  • Book chapters 3
  • Mini-project 1

Weekend, Jan 16–17

  • Begin MP2

Mon, Jan 18 (mlk)

  • MLK DAY – NO CLASS

Thu, Jan 21 (MP2 workshop)

Fri, Jan 22 (MP2 presentation)

Weekend, Jan 23–24

  • Complete MP2

Programming with data

Motivating questions:

How can we perform operations many times without copying-and-pasting code? How can we generalize our pipelines into functions? What are ethical considerations of data science products?

Learning goals:
  • To use iteration to perform tasks more than once
  • To consider the ethical implications of data science products
Standards:
  • Screencasts 10
  • Slide decks 6
  • Labs 5
  • Lectures 5
  • Book chapters 2
  • Mini-project 1

Thu, Jan 28 (MP3 workshop)

Fri, Jan 29 (MP3 presentation)

Weekend, Jan 30–31

  • Complete MP3

Working with geospatial data

Motivating questions:

What makes geospatial data special? What data structures are available for geospatial data? How does space and location affect the way we draw data graphics? What aesthetic considerations are specific to geospatial data?

Learning goals:
  • To use spatial data to make informative maps in R
  • To use spatial data to inform recommendations for policy changes
Standards:
  • Screencasts 10
  • Slide decks 10
  • Lectures 5
  • Labs 4
  • Book chapters 2
  • Mini-project 1

Weekend, Feb 6–7

  • Complete MP4

Database querying

Motivating questions:

How can we retrieve data from a database? How does our understand of dplyr inform our understanding of SQL? What are keys and indices?

Learning goals:
  • To write database queries in SQL
Standards:
  • Slide decks 8
  • Screencasts 7
  • Labs 2
  • Book chapters 1
  • Mini-project 1

Wed, Feb 10 (MP5 workshop)

Thu, Feb 11 (MP5 presentations)

  • MP5 hard deadline Feb. 11 at midnight!