Assignment 2: Exploratory Data Analysis
In this assignment, you will identify a dataset of interest and perform an exploratory analysis to better understand the shape & structure of the data, investigate initial questions, and develop preliminary insights & hypotheses. Your final submission will take the form of a report consisting of captioned visualizations that convey key insights gained during your analysis. The assignment consists of two parts: (1) data selection & wrangling and (2) exploratory visual analysis.
Part 1: Data Selection & Wrangling
First, you will pick a topic area of interest to you and find a dataset that can provide insights into that topic. Your dataset must fit the course theme of data for civic participation: you should choose a publicly accessible dataset relevant to the functioning of local, national, or global communities. Within this theme you are free to choose a domain of interest; example topics include health, transportation, economic development, climate, migration, legislative voting, and campaign finance. Try to find datasets that you would be interested in visualizing as part of your final project!
Prior to analysis, you should write down an initial set of at least three questions you'd like to explore. You must submit a form describing your dataset and initial questions by Monday 4/10, 5pm. You should start this process ASAP. We can not stress this strongly enough: finding appropriate high-quality datasets and cleaning the data so that it can be used with visualization tools can require significant effort. Do not delay!
Part 2: Exploratory Visual Analysis
Next, you will perform an exploratory analysis of your dataset using a visualization tool such as Tableau. You should consider two different phases of exploration.
In the first phase, you should seek to gain an overview of the shape & stucture of your dataset. What variables does the dataset contain? How are they distributed? Are there any notable data quality issues? Are there any surprising relationships among the variables? (Note that this stage can overlap with your initial data preparation.)
In the second phase, you should investigate your initial questions, as well as any new questions that arise during your exploration. For each question, start by creating a visualization that might provide a useful answer. Then refine the visualization (by adding additional variables, changing sorting or axis scales, filtering or subsetting data, etc.) to develop better perspectives, explore unexpected observations, or sanity check your assumptions. You should repeat this process for each of your questions, but feel free to revise your questions or branch off to explore new questions if the data warrants.
Your final submission should take the form of a report – similar to a slide show or comic book – that consists of 10 or more captioned visualizations detailing your most important insights. Each visualization image should be a screenshot exported from a visualization tool, accompanied with a title and descriptive caption (1-4 sentences long) describing the insight(s) learned from that view. Your "insights" can include important surprises or issues (such as data quality problems affecting your analysis) as well as responses to your analysis questions. Provide sufficient detail for each caption such that anyone could read through your report and understand what you've learned. Finally, the end of your report should include a brief summary of main lessons learned.
To help formulate your report, we've provided a basic HTML template for you to fill in. You will need to edit the HTML to add your captions and links to image files. Exported image files should reside in the same local directory as your HTML file. You are free, but not required, to annotate your images to draw attention to specific features of the data: you might perform highlighting within the visualization tool itself, or draw annotations on the exported image. When complete, submit a zip file to Canvas with your HTML file and visualization screenshot images.
To help you gauge the scope of this assignment, see this example report analyzing data about motion pictures. Also: To easily export images from Tableau, use the "Worksheet > Export > Image..." menu item.
Here is a list of data sources that you might use for this assignment. You are also free to use data from a source different from those included here. If you have any questions on whether your dataset is appropriate, please ask the course staff ASAP!
- data.seattle.gov - City of Seattle Open Data
- data.wa.gov - State of Washington Open Data
- nwdata.org - Open Data & Civic Tech Resources for the Pacific Northwest
- data.gov - U.S. Government Open Datasets
- U.S. Census Bureau - Census Datasets
- IPUMS.org - Integrated Census & Survey Data from around the World
- Federal Elections Commission - Campaign Finance & Expenditures
- Federal Aviation Administration - FAA Data & Research
- fivethirtyeight.com - Data and Code behind the Stories and Interactives
- Buzzfeed News
- Socrata Open Data
- 17 places to find datasets for data science projects
You are free to use one or more visualization tools in this assignment. However, in the interest of time and for a friendlier learning curve, we strongly encourage you to use Tableau. Tableau provides a graphical interface focused on the task of visual data exploration. You will (with rare exceptions) be able to complete an initial data exploration more quickly and comprehensively than with a programming-based tool.
- Tableau - Desktop visual analysis software. Available for both Windows and MacOS; register for a free student license.
- Voyager - Research prototype from the UW Interactive Data Lab. Voyager combines a Tableau-style interface with visualization recommendations. Use at your own risk!
- R, using the ggplot2 library or with R's built-in plotting functions.
- Jupyter Notebooks (Python), using libraries such as Altair or Matplotlib.
Data Wrangling Tools
The data you choose may require reformatting, transformation or cleaning prior to visualization. Here are tools you can use for data preparation. We recommend first trying to import and process your data in the same tool you intend to use for visualization. If that fails, pick the most appropriate option among the tools below. Contact the course staff if you are unsure what might be the best option for your data!
- Tableau - Tableau provides basic facilities for data import, transformation & blending.
- Trifacta Wrangler - Interactive tool for data transformation & visual profiling.
- OpenRefine - A free, open source tool for working with messy data.
Each submission will be graded based on both the analysis process and included visualizations. Here are our grading criteria:
- Poses clear questions applicable to the chosen dataset.
- Appropriate data quality assessment and transformation.
- Sufficient breadth of analysis, exploring multiple questions.
- Sufficient depth of analysis, with appropriate follow-up questions.
- Expressive & effective visualizations crafted to investigate analysis questions.
- Clearly written, understandable captions that communicate primary insights.
This is an individual assignment. You may not work in groups.
This assignment involves two stages of submission. First, you must submit a form indicating your choice of dataset and analysis questions by Monday 4/10, 5pm. Second, the completed exploratory analysis report is due Friday 4/14, 5pm. You should fill out the provided HTML template and upload to Canvas a zip file containing your completed HTML file and visualization screenshots.