Midterm project

For this semester’s midterm project, we will be participating in the American Statistical Association’s Police Data Challenge. Read the information on the website for many more details. For the midterm project, all students should visualize the Seattle data set.

From the website:

Awards will be given in three categories (1) Best Overall Analysis, (2) Best Visualization, and (3) Best Use of External Data.

Winning teams will receive a $50 Amazon gift card, complimentary memberships to ASA, and a Police Data Challenge 2017 t-shirt, along with bragging rights and a chance to have an impact on local communities.

Winners also will be profiled and promoted to ASA’s membership of leading statisticians and data scientists in academia, industry, business, and government, and through ASA’s public education campaign, ThisisStatistics. Second and third place winners will also be recognized.

###Requirements for the competition

Prepare the materials that you need to submit to the competition: PowerPoint presentation with up to 10 slides, and document with $\leq 500$ words detailing your process. Each team should submit these files by Nov 3.

###Additional requirements for DSCI 310 Midterm

The main additional requirement is to use Census data to some extent in your project. One way is to merge Census Tract information with the Tract information in the Seattle data file. A very nice R package for downloading Census data is tidycensus, especially the function get_acs(). This function makes use of the Census API, specifically reading in data from the American Community Survey (ACS).

Additionally, each team will present their PowerPoint slides beginning on October 27 (so really, the deadline to have your PowerPoint done is 10/27, not 11/3).

Rubrics for DSCI 310 midterm

Relevant resources

Group assignments

Professionally, you will not get to always choose your own groups. For the midterm project you will be assigned into groups, which are listed below. Divide the work, and conquer!

Group presentation order:

4, 2, 7, 1, 9, 8, 6, 5, 3

  • Group 1:

    • Jack Barta
    • Sam Meyer
    • Mariah Quam
  • Group 2:

    • Fengrui Xue
    • Thomas Gathje
    • Max Oelerking
  • Group 3:

    • Kapil Khanal
    • Luke Peacock
    • Jimmy Hickey
  • Group 4:

    • Will Diedrick
    • Yulun Xu
    • Adam Clemens
    • Lauren Willkom
  • Group 5:

    • Salman Quraishi
    • Waheed Khan
    • Linh Nguyen
  • Group 6:

    • Nathan Smith
    • Paul Boelter
    • David Stampley
    • Akif Khan
  • Group 7:

    • Austin Ellingworth
    • Brad Erickson
    • Sam Dokkebakken
  • Group 8:

    • Matthew Ladin
    • McHale Dye
    • Catherine Nead
  • Group 9:

    • Stacey Miertschin
    • Reagan Buske
    • Ross Krueger
Silas Bergen
Silas Bergen
Associate professor of statistics and data science