Course Info
| Program Title | Public Affairs Executive Education |
| Course Title | Data Analytics for the Public Good |
| Course Number | PAF 586 |
| Course Level | Graduate |
| Course Start-End | July 3 to August 13, 2019 |
| Class Timings | Asynchronous |
Course Instructors
| Jesse Lecy, PhD | Professor |
Office Hours
| Jesse Lecy, PhD | Flexible, by appointment | Virtual | ONLINE OFFICE HOURS |
Textbooks
Course Description, Course Goal and Course Learning Objectives:
Public agencies are increasingly interested in unlocking the potential of large-scale data to improve service delivery and inform policy efforts. Computational tools capable of making productive use of big data have proliferated in recent years, drastically decreasing the barriers to entry for interested parties. This course will explore the practice of using data to improve organizational performance, including techniques for data collection, analysis, and behavior change. Students will operate as their own laboratory through a data journaling exercise, and devise strategies for incorporating data into management practices of public and nonprofit organizations.
One of the key course take-aways is that data can make your organization more effective, but data itself is not sufficient without strong management frameworks. The course is built around a quantified-self experiment where you will use life-logging tools and a journaling system to learn about managing information overload and building goal-oriented, evidence-based routines.
Students will also explore the social, political, and ethical considerations associated with building and managing data analytics programs in the public sphere. Students will engage critical dilemmas of data privacy, data protection, predictive analytics, personalized service delivery and resource provision, algorithmic regulation, and large-scale data analytics for administrative efficiencies and resource management optimization, among others.
Students will read several case studies that explore the use of data in organizations, will engage in discussions about socio-economic policy considerations, and write policy guidance frameworks on best practices in evidence-based management, open data, and privacy.
Learning Objectives
At the conclusion of this course, each student will be able to:
- Describe how public agencies harness large-scale data to inform policy design, increase stakeholder engagement, and improve service delivery.
- Recognize situations where it’s possible to collect data to inform organizational processes.
- Intelligently consider the social, political, and ethical considerations of using data analytics.
II. Assessment of Student Learning Performance & Proficiency: Keys to Student Success
Assigned work, including the course data journaling project, active engagement with weekly readings, and the quality of participation in discussion boards are a critical part of the course learning strategy. The student’s course grade is a direct reflection of demonstrated performance on these tasks.
• Students should take stated expectations seriously regarding preparation, conduct, and academic honesty in order to receive a grade reflective of outstanding performance.
• Students should be aware that merely completing assigned work in no way guarantees an outstanding grade in the course.
• To receive an outstanding course grade (using the grading scheme described below and the performance assessment approach noted above) all assigned work should completed on time with careful attention to assignment details.
III. Course Structure
A. Assigned Reading Materials
We will use the following required texts:
- Pentland, A. (2015). Social Physics: How social networks can make us smarter. Penguin.
- Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Routledge.
- Eagle, N., & Greene, K. (2014). Reality mining: Using big data to engineer a better world. MIT Press.
- O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.
In addition to the required textbooks, the instructor will supplement the assigned unit readings with various journal articles, policy reports, or other related material. These will be made available in the course shell in Canvas.
B. Course Grading System for Assigned Work, including Final Project:
Points will be allocated based upon three main assignments: reading summaries and presentations, discussion of topics on the discussion board, and a data journaling exercise.
| Reading summaries & Yellowdig discussions | 50% |
| Labs | 30% |
| Final Memo | 20% |
Students will participate in an exercise to collaboratively author a guide to data-driven management by summarizing readings from the course. A schedule of assigned summaries and due dates will be posted on the project site. Reading summaries (GitHub chapters) are due Sunday of each week (I will give you until Monday night the first week because of the holiday).
Students will engage with weekly material through the YellowDig discussion system. Points are allocated based upon specific actions on the discussion board (see examples below, points may be adjusted). Note that full credit requires active participation for a minimum of 5 weeks (you can earn up to 20 points each week).
- 1 point for a new pin with at least 50 words.
- 2 points for a comment made to another pin.
- 2 points if you receive a comment on your pin.
- 1 point for liking another pin.
- 5 points if you earn an instructor badge for an informative post.
- max of 20 points can be earned each week.
- max of 100 points can be earned overall
Labs will be used to explore a case study where machine learning is used to predict neighborhood change and gentrification in a city. You will have short tasks for each.
Letter grades comport with a traditional set of intervals, subject to instructor discretion:
- 100 = A+
- 99 – 94% = A
- 93 – 90% = A -
- 89 – 87% = B+
- 86 – 84% = B
- 83 – 80% = B –
- Below 80% - C, D, F
D. General Grading Rubric for Written Work
Individual assignment grading rubrics will be provided. However, in order to understand the assessment approach for assigned work, in general all written work (assignments and labs) is assessed on these evaluative criteria:
- Assignment completeness – all elements of the assignment are addressed
- Quality of work – substantively rigorous in addressing the assignment
- Demonstrated understanding of the readings and the ability to apply concepts to unique domains
E. Late and Missing Assignments
This course is based on students reading course material, participating in discussion with colleagues and producing analytic essays. Accordingly, if students do not participate fully and completely, assessment of student performance will reflect that directly. For example, points lost for lack of participation in a weekly discussion session are not eligible for remedial action.
All assigned work is accompanied by detailed instructions, adequate time for completion and opportunities to consult the instructor with questions. As a result, each assignment element in the course is expected to be completed in a timely fashion by the specified due date. Unless arrangements have been made beforehand, late assignments will be docked a letter grade for each day late (subject to the instructor’s discretion). Late material should be submitted through Canvas, not sent via email. Missed exams (when applicable) will result in a zero, unless prior arrangements have been made.
F. Course Communications and Instructor Feedback:
Course content will be available on this website. All written assignments (labs and the final paper) will be submitted through the university’s Canvas platform. Yellowdig will serve as our disucussion board.
- Course communications will be transmitted through the class email list using ASU email addresses.
- Students should generally expect replies to instructor emails in 24 hours (with exceptions for travel when specified).
- The timeline for instructor grading or other feedback on assignments, either writer work or online discussion work, is generally between 5 and 10 work days.
G. Student Conduct: Expectation of Professional Behavior:
Respectful conversations and tolerance of others’ opinions will be strictly enforced. Any inappropriate language, threatening, harassing, or otherwise inappropriate behavior during discussion could result in the student(s) being administratively dropped from the course with no refund, per ASU policy USI 201-10. Students are required to adhere to the behavior standards listed in the Arizona Board of Regents Policy Manual Chapter V—Campus and Student Affairs .
H. Academic Integrity and Honesty
ASU expects the highest standards of academic integrity. Violations of academic integrity include but are not limited to cheating, plagiarism, fabrication, etc. or facilitating any of these activities. This course relies heavily on writing and original critical thought. Any student who is suspected of not producing his or her own original work will be reported to the College of Public Programs for investigation. Plagiarism will not be tolerated. Any student who plagiarizes or otherwise fabricates his or her work will receive no credit for that assignment. It will be recorded as zero points—and the student will risk a failing grade for the course. For more information, refer to http://provost.asu.edu/academicintegrity.
I. Student Learning Environment: Accommodations
Disability Accommodations: Students should be fully aware that the Arizona State University, the MA in EMHS program, and all program course instructors are committed to providing reasonable accommodation and access to programs and services to persons with disabilities. Students with disabilities who wish to seek academic accommodations must contact the ASU Disability Resources Center directly. Information on the Center’s procedures, resources and how to contact its staff can be found here: https://eoss.asu.edu/drc/. The Disability Resources Center is responsible for reviewing any student’s requests; once that review has taken place, the Center will provide the student with appropriate information on academic accommodations which in turn will be provided to the course instructor.
Religious accommodations: Students will not be penalized for missing an assignment due solely to a religious holiday/observance, but as this class operates with a fairly flexible schedule, all efforts should be made to complete work within the required timeframe. If this is not possible, students must notify the instructor as far in advance as possible in order to make an alternative arrangement.
Military Accommodations: A student who is a member of the National Guard, Reserve, or other branch of the armed forces and is unable to complete classes because of military activation may request complete or partial unrestricted administrative withdrawals or incompletes depending on the timing of the activation. For more information see ASU policy USI 201-18.
KEY COURSE THEMES
The Promise of Big Data:
In 2017 The Economist declared, “Data is to this century what oil was to the last one: a driver of growth and change. Flows of data have created new infrastructure, new businesses, new monopolies, new politics and – crucially – new economics.”
In 2011 McKinsey & Co. described big data as “the next frontier for innovation, competition, and productivity.” GE looked to big data to drive “changes as profound as industrialization… in the late 1700s”.
Challenges of Harnessing Data:
Many organizations have been slow in compiling, classifying, and organizing the data sitting in siloes and dark corners. It’s “a boring, boring job,” says Ger Baron, Amsterdam’s first-ever chief technology officer. “But very useful!”
He ought to know. The Netherlands’ capital has 12,000 different datasets, and even they can’t tell him everything about the city. For example, no one knows exactly how many bridges span Amsterdam’s famous canals, because the city’s individual districts have not centralized their infrastructure data.
That story underscores the challenges organizations face in the realm of data governance, or the methods and rules that organizations use to assure the quality of data, manage it, integrate it into business processes, and manage its risks.
Abundant Data by Itself Solves Nothing:
Despite the promise of big data, industrial enterprises are struggling to maximize its value. Why? Abundant data by itself solves nothing. Its unstructured nature, sheer volume, and variety exceed human capacity and traditional tools to organize it efficiently and at a cost which supports return on investment requirements. Inherent challenges tied to evolution and integration of industrial information and operational technology, make it difficult to glean intelligence from operational data, compromising projects underway and promise for further investment and value.
Firms are years away from getting full value from their data assets:
Throwing cash at the problem isn’t helping matters either. Companies need to scale back their ambitions to invest in projects that are more evolutionary than revolutionary in nature, looking to tweak rather than overhaul existing operational practices. In short, the best big data strategy may be to go small.
Effective Approaches:
It’s tough enough for many organizations to catalog and categorize the data at their disposal and devise the rules and processes for using it. It’s even tougher to translate that data into tangible value.
There would be no data and analytics revolution without easily accessible, increasingly inexpensive computing power: the cloud, the Internet, and powerful, versatile software and algorithms. Yet technology is only part of the story. People are equally important.
The technology and the people who deploy it also need a process or system of rules to guide how people create and use information. Rules help transform the noise of disordered information into legible signals with the power to sharpen and deepen the focus on the customer (broadly defined), and in the process improve health outcomes, the customer experience, the realization of business value, and civic life and engagement.
Course Schedule
This represents a general plan. Check on the Schedule tab of this site for the most recent dates and deadlines.
READING ASSIGNMENTS: DUE SUNDAY OF EACH WEEK
Each week one team is assigned to a topic. They are responsible for reading all of the articles listed under the topic and creating an overview of key management insights and suggested best practices for the topic, and short (half page) summaries of each article. The summaries comprise the chapters of a collaborative class text book that is written using an open-source book model. The purpose of this assignment is to create a useful overview of the topics covered in this class that will serve as a reference guide so that it is easy for students to find content in the future.
http://ds4ps.org/ddm-textbook-summer-2019/
The team is also responsible for writing a Yellowdig blog post on their readings and generate some discussion questions for classmates.
Groups
Team 1:
Robert Lott
James Hogue
Kirsten VanDeventer
Team 2:
Annie Ackroyd
Victoria Adair
Bret Petersen
Team 3:
Cody Lundell
Christina Worden
Daniel Gabiou
Team 4:
Martha Ramos
Philip Schlotter
Randolph Wilkins
Team 5:
Troy George
Jamie Bandy
Week 01: The Value of Data
- The Value of Data
- Open Data (Team 1)
- The Big Promise of Big Data (Team 2)
- Challenges of Big Data (Team 3)
LAB COMPONENT: Models of Neighborhood Change
Week 2: Collecting Data
- Data on Teams (Team 4)
- Eyes in the Sky (Team 5)
- Social Media Data (Team 1)
- Remote Sensors (Team 2)
LAB COMPONENT: Measurement
Week 3: Prediction
- Discovery (Team 3)
- Prediction / Moneyball (Team 4)
- Bias in Prediction (Team 5)
LAB COMPONENT: Feature Selection
Week 4: Challenges of Big Data
- Data Quality (Team 1)
- Manipulation (Team 2)
- Ethics of Algorithms (Team 3)
- Best Practices for Privacy (Team 4)
LAB COMPONENT: Feature engineering
Week 5: Managing with Data
- Managerial Experiments (Team 5)
- Information Blindness (Team 1)
No Lab
Week 6: Data-Driven Human Resources
- Building Effective Teams (Team 2)
- Incentivizing Teams (Team 3)
- A Tale of Two Data-Driven Management Systems: Amazon and Zappos (Team 4)
- A Tale of Two Data-Driven Management Systems: Amazon and Zappos (Team 5)
August 13 - Final Memo Due
FINAL MEMO OPTIONS:
- Option A: Write a proposal to automate a process in your organization. Define the variables you would need for your model, describe how data would be collected, and discuss ethics.
- Option B: Develop a plan to moneyball the school district using AI for better HR practices. What data would you need for hiring and firing decisions? How would it be collected? What challenges of predictive accuracy and bias might you anticipate?