DATA 100: Wicked Problems
Course ID: DATA 100
Course Attribute: COLL 100
Title: Wicked Problems: The New Data Paradigm and Emergent Human Development Processes
Credit Hours: 4
Meeting Times: 10:00 to 10:50 MWF
Location: ISC3 1111
Date Range: Fall Semester 2021
Course Description
Global, near present-time, high-resolution data is openly accessible to construct scientific descriptions of human development. In this course you will use data describing populations, governments, and both the natural and built environments in order to construct a close-to-reality description of your selected region or country. You will then intersect a selected dimension of human development as an initial boundary for investigating a contextually relevant research question. While development is often thought of as a “missing ingredient” that is needed to improve the well-being of people or the output of firms, in this course human development is defined as the ability to enlarge people’s choices, capabilities and freedoms and is understood as an emergent property from our complex and adapting economic and social system. Data science methods are used to construct close-to-reality descriptions of human development processes in order to identify interactions, describe co-evolving agents, recognize the conditions prevalent for emergence, understand the significance of scale and ultimately begin to establish a framework for analyzing human development’s multitude of seemingly intractable, wicked problems. During this course you will also learn to use the flexible data science, programming language R, but no prior experience is needed. Pre-requisite(s): None
Courses Objectives
- To introduce students to human development as the ability to enlarge people’s choices, capabilities and freedoms and as an emergent property from society functioning as a complex adaptive social and economic system.
- To introduce students to data science through understanding previously computationally intractable, wicked problems.
- To demonstrate competent information literacy and data science skills by producing knowledge from data through the creation of plots, graphs, charts and maps.
- To demonstrate competent application of fundamental computer science and statistics methods within the context of geospatial human development processes.
- To challenge students to become apprentice scholars by supporting their answer to a formulated central research question with close-to-reality geospatial descriptions of human development processes.
Honor Code
Among our most significant traditions is the student-administered honor system. The Honor Code is an enduring tradition with a documented history that originates as far back as 1736. The essence of our honor system is individual responsibility. Today, students, such as yourself, administer the Honor pledge to each incoming student while also serving to educate faculty and administration on the relevance of the Code and its application to students’ lives.
The Pledge
“As a member of the William and Mary community, I pledge on my honor not to lie, cheat, or steal, either in my academic or personal life. I understand that such acts violate the Honor Code and undermine the community of trust, of which we are all stewards.”
Accessibility, Attendance & Universal Learning
William & Mary accommodates students with disabilities in accordance with federal laws and university policy. Any student who feels s/he may need accommodation based on the impact of a learning, psychiatric, physical, or chronic health diagnosis should contact Student Accessibility Services staff at 757-221-2509 or at sas@wm.edu to determine if accommodations are warranted and to obtain an official letter of accommodation. For more information, please see www.wm.edu/sas.
I am committed to the principle of universal learning. This means that our classroom, our virtual spaces, our practices, and our interactions be as inclusive as possible. Mutual respect, civility, and the ability to listen and observe others carefully are crucial to universal learning. Active, thoughtful, and respectful participation in all aspects of the course will make our time together as productive and engaging as possible.
Grade Categories
| exceptional A = 100 ≥ 97.0 | excellent A = 96.9 ≥ 93.0 | superior A- = 92.9 ≥ 90.0 |
| very good B+ = 89.9 ≥ 87.0 | good B = 86.9 ≥ 83.0 | above average B- = 82.9 ≥ 80.0 |
| normal C+ = 79.9 ≥ 77.0 | average C = 76.9 ≥ 73.0 | sub par C- = 72.9 ≥ 70.0 |
| below average D+ = 69.9 ≥ 67.0 | poor D = 66.9 ≥ 63.0 | very poor D- = 62.9 ≥ 60.0 |
| failing F < 60.0 |
note .9 = .9 with bar notation
Grading Opportunities
| Five Data Science reflections | 25% | Due every other Friday |
| Five Individual Projects | 50% | Due every other Saturday |
| Final Project | 25% | due by 5PM on the last day of finals |