DSCI_531_viz-1
DSCI 531: Data Visualization I
Effective static data graphics using R and python for exploratory data analysis.
Course website: https://ubc-mds.github.io/DSCI_531_viz-1/
Course Learning Outcomes
By the end of the course, students are expected to:
- Implement python libraries and R’s ggplot2 to generate static graphics.
- Understand the key components/encodings of a statistical graphic.
- Describe key modern plot types and their uses for EDA.
- Analyze and draw conclusions from data using EDA.
- Make effective design choices when producing a data graphic.
- Understand where EDA falls short (Eg. Automatic decision making)
Assessments
You’ll be evaluated as follows:
| Assessment | Weight | Due Date | Location | Covers |
|---|---|---|---|---|
| “Half Lab” Assignment 1 | 6% | Saturday, Oct 13 at 18:00 | Submit to Github | Lec 1 |
| Lab Assignment 2 | 12% | Saturday, Oct 20 at 18:00 | Submit to Github | Lec 2-3 |
| Quiz 1 | 20% | Tuesday, Oct 23, 15:00-15:30 | Your lab room; submit to canvas | Lec 1-3, Lab 1-2 |
| Lab Assignment 3 | 12% | Saturday, Oct 27 at 18:00 | Submit to Github | Lec 4-5 |
| Lab Assignment 4 | 12% | Saturday, Nov 3 at 18:00 | Submit to Github | Lec 6-7 |
| Quiz 2 | 20% | Wed, November 7, 14:00 – 14:30 | Your DSCI 512 (Wednesday) lab rooms | Lec 4-8, Lab 3-4 |
| Lec 8 Hackathon | 6% | Tuesday, Nov 6 at 18:00 | Submit to GitHub | NA |
| Peer Review of Hackathon | 5% | Submit to GitHub | NA | |
| Participation | 7% | NA | NA | Seven lectures |
Tip: Use the lecture learning objectives as beacons when studying for your quizzes!
Lecture Details
| Lecture | Topic | Pre-readings/Resources |
|---|---|---|
| 1 / complete worksheet | Basic plot “types” with ggplot2 |
r4ds: data-vis, esp. 3.6 |
| 2 / complete worksheet | EDA; aesthetic mappings in ggplot2 |
r4ds: data-vis, esp. 3.3 |
| 3 / complete worksheet | Dependence. Finish looking at ggplot2 as a tool. |
stat545: colors; Jenny’s theme tutorial |
| 4 / complete worksheet | Special plot types: GGally, Interactivity and 3D plotting with plotly, ggmaps |
ggobi’s list of GGally extensions; r-bloggers on ggmap |
| 5 | Plotting for humans | stat545: dos-and-donts, Geckoboard tips |
| 6 | Plotting in Python, Part I: matplotlib | pyplot tut |
| 7 | Plotting in Python, Part II: seaborn | elitedatascience seaborn tut |
| 8 | Hackathon |
Annotated Resources
Here are prominent course resources that we will be referring to.
- R for Data Science (r4ds)
- Overall good book on using R for data science – including data vis, of course!
- ggplot2 book
- Readable, comprehensive resource for learning about
ggplot2, by the main author of theggplot2package, Hadley Wickham.
- Readable, comprehensive resource for learning about
- STAT 545 “All the Graph Things” by Jenny Bryan.
- Contains tutorials relevant to our subject matter.
Other resource that you might find useful:
- R Graphics Cookbook
- Good as a reference if you want to learn how to make a specific type of plot in
ggplot2.
- Good as a reference if you want to learn how to make a specific type of plot in
- Jenny Bryan’s ggplot2 tutorial
- Has a lot of examples and less dialogue.
- ggplot2 cheat sheet
- Great for quick reference if you need something beyond tab-completion.
- “Visualization Analysis and Design” by Tamara Munzner, CRC Press, 2014.
- The go-to book for data vis theory.
Policies
Please see the general MDS policies.
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.