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Data-Driven Management and Policy

Foundations of data analytics for evidence-based management and policy.



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Course Info

Program Title Evans School of Public Policy
Course Title Data-Driven Management and Policy
Course Number PUBPOL 599
Course Level Graduate
Course Start-End Spring Quarter, 2019
Class Timings Monday 17:30-20:20 Pacific Time
Class Location https://asu.zoom.us/j/742627567

Course Instructors

José Manuel Magallanes, PhD Professor
Eric van Holm, PhD. Professor

Office Hours

José Manuel Magallanes, PhD Flexible, by appointment Virtual ONLINE OFFICE HOURS
Eric van Holm, PhD. Flexible, by appointment Virtual ONLINE OFFICE HOURS

Textbooks

Paul Teetor Not Required
Wickham, H., & Grolemund, G. Free Online
Peng, R. D., & Matsui, E. Free Online
Chester Ismay & Albert Y. Kim Free Online
Smith, Smith, and Johnson Not Required

Course Description

The course will cover ways that data can be used to improve performance in public organizations, as well as tools and techniques for collecting, analyzing, and displaying data. Thus, an emphasis will both be placed on the substantive idea behind using data for performance management as well as programming. The class will be taught in R software and will introduce students to its language, structure, and capabilities for data analysis. Through R, students will become comfortable inputting, manipulating, and visualizing data through the software. In addition to the technical skills gained, students will be introduced to how data can drive performance management through a self-directed project and understand the potential and possible abuses of data to drive public organizations. As part of the class, students will collect their own data, use that data in an individual experiment to test performance, and display the outcome using a ’dashboard’ in R.

Course Objectives

  1. Use data analytics to improve performance in public management.
  2. Understand the R language for the collection and manipulation of data.
  3. Produce visualizations in dashboards to ease decision making.

Prerequisites

Students are expected to complete this online tutorial prior to the course (just the free material), or have equivalent experience in R:

https://www.datacamp.com/courses/free-introduction-to-r

Software

Students are expected to install the following software on their computers prior to the first week of class:

  • R (choose according to your Operating System): https://cran.r-project.org/
  • RStudio Desktop Personal License (choose according to your Operating System) https://www.rstudio.com/products/rstudio/download/

Evans School Community Conversation Norms

This course has adopted the Evans School Community Conversation Norms. Please be aware of these norms in interactions with the instructor and other students. At the Evans School, we value the richness of our differences and how they can greatly enhance our conversations and learning. As a professional school, we also have a responsibility to communicate with each other–inside and outside of the classroom–in a manner consistent with conduct in today’s increasingly diverse places of work. We hold ourselves individually and collectively responsible for our communica- tion by:

  • Listening carefully and respectfully
  • Sharing and teaching each other generously
  • Clarifying the intent and impact of our comments
  • Giving and receiving feedback in a “relationship –building” manner
  • Working together to expand our knowledge by using high standards for evidence and analysis

Changes to the Syllabus

The professors reserve the right to make changes to the syllabus during the quarter. The profes- sors will notify students immediately by email and in class if any changes are made.

Submitting Assignments

Please submit your assignments in the appropriate column next to your name in the Google Sheets below. Your assignments should be turned in as a R Markdown file in html; please publish your markdown using RPubs so that we can view it. Follow this video for a guide to Rpubs

The link ypu get from RPubs is the one you will paste to the spreadsheet. Notice that we need to see your work when clicking your link. You can use any other way as long as we can see your work. please verify your link after submitting to the following google sheet.

Google Sheet

Grading Policy

Grades consider two elements:

  • Weekly labs: Each week students are to complete a set of exercises.
  • Final project: Students will prepare a dashboard, in 3 stages:
    • Dashboard plan. Students should present a dashboard proposal, justifying its creation and importance.
    • Dashboard data repository. Students will prepare a repository with the data to be used in the dashboard.
    • Dashboard release.

The final grade will be comprised of the following weighted elements:

  • 40% for completing the Labs.
  • 20% for completing Dashboard plan.
  • 20% for completing Dashboard data repository
  • 20% for completing Dashboard release.

Letter grades comport with a traditional set of intervals:

  • 100 – 98% = A+
  • 97 – 94% = A
  • 93 – 90% = A -
  • 89 – 87% = B+
  • 86 – 84% = B
  • 83 – 80% = B –
  • Below 80% - C, D, F

Course Schedule

This represents a general plan. Check on the Schedule tab of this site for the most recent dates and deadlines.

Week 01, 04/01 - 04/05: Intro to R for performance

  • Introduction to Markdown, R, and R Studio
  • Uses of data in public management, reasons for doing experiments.

Week 02, 04/08 - 04/12: R basic elements

  • Data types in R
  • Ways of collecting data: passive, active, administrative intro to life logging and semester project.
  • Design of Experiments.

Week 03, 04/15 - 04/19: R Data structures

  • Lists.
  • Vectors.
  • Data frames.

Week 04, 04/22 - 04/26: Working on data frames

  • Logical operators.
  • Control of execution
  • Functions.

Week 05, 04/29 - 05/03: Intro to Data Visualization

  • Visualization guidelines.
  • R and the grammar of graphics.

Week 06, 05/06 - 05/10: Spatial and Multidimensional visualization

  • Bivariate and multivariate plots.
  • Mapping in R.

Week 07, 05/13 - 05/17: Dynamic visualization:

  • The design and construction of dashboards.

Week 08, 05/20 - 05/24: R and evidence production

  • Analysis of experiments.
  • Testing outcomes in R.

Week 09, 05/27 - 05/31: Memorial day

Instructions will be given on what to do for this week.

Week 10, 06/03 - 06/07: Advanced topics for dashboards

  • Shiny widgets.
  • Advanced dashboards.

Week 11, 06/10 - 06/14: Final week

Submitting final project.