Data literacy with Data Commons
Overview
“Data literacy with Data Commons” comprises curriculum/course materials for instructors, students and other practitioners working on or helping others become data-literate. This includes detailed modules with pedagogical narratives, explanations of key concepts, examples, and suggestions for exercises/projects focused on advancing the consumption, understanding and interpretation of data in the contemporary world. In our quest to expand the reach and utility of this material, we assume no background in computer science or programming, thereby removing a key obstacle to many such endeavors.
Objectives
- Exposing students (and practitioners) to the basics of data comprehension and interpretation.
- Introducing some basic and intermediate data-driven decision making concepts.
- Explicitly cater to the needs of students (and practitioners) who have no prior programming experience and a limited exposure to basic statistical concepts.
- Adopt a narrative-based approach to appeal to a wide range of audiences.
- Grounding most/all examples and illustrative assignments on real data.
- Using as many of the out-of-the-box and freely available data exploration tools to preclude any advanced or specialized knowledge.
- Make the curriculum materials openly available and support extensive customizations for any instructors who wish to adopt components/modules to suit their needs.
Who is this for?
Anyone and everyone. Instructors, students, aspiring data scientists and anyone interested in advancing their data comprehension and analysis skills without needing to code. For instructors, the rest of this page details the curriculum organization and how to find key concepts/ideas to use.
Suggested prerequisites
We suggest only the following prerequisites for this curriculum:
- High-school level mathematics (basic).
- High-school level statistics (basic).
Why Data Commons?
Data Commons (datacommons.org) is an open and public-access platform to all sorts of publicly available data in the world. From demographic data to economic/financial data to health indicators to weather/climate, Data Commons aggregates and makes available data about millions of places and thousands of metrics, e.g.,population growth rate.Additionally, Data Commons also provides some out-of-the-box data analysis tools, e.g., timeline charts, maps, scatter plots and the ability to download the data.
For the purpose of the data literacy, the Data Commons platform becomes an important component of the curriculum because it helps satisfy several curriculum development objectives:
- Real data Data Commons provides open and easy access to a plethora of publicly available real data.
- Open access Data Commons is available to everyone at no cost and with no restrictions of use.
- Customization When viewing or analyzing any data on the Data Commons website, e.g., the median household income in California, Nevada and Oregon, one can easily replace the places to display the same information for an entirely different set of locations, e.g., the median household income in Florida, Louisiana and Texas.
Course content
The course consists of a set of modules focusing on several key concepts, including data modeling, analysis, visualization and the (ab)use of data to tell (false) narratives. Each module lists its objectives and builds on a pedagogical narrative around the explanation of key concepts, e.g.,, the differences between correlations and causation. We extensively use the Data Commons platform to point to real world examples without needing to write a single line of code!
The course consists of a set of modules focusing on several key concepts, including data modeling, analysis, visualization and the (ab)use of data to tell (false) narratives. Each module lists its objectives and builds on a pedagogical narrative around the explanation of key concepts, e.g., the differences between correlations and causation. We extensively use the Data Commons platform to point to real world examples without needing to write a single line of code!
What is not covered?
We note that the curriculum objectives, themes, content and areas of focus are neither exhaustive nor a one size fits all. For example, we do not focus on the ethics of data collection in this curriculum. While these issues are of utmost importance, we chose to focus on a more basic and hands-on approach with the available resources.
We also have more advanced “Data Science with Real Data” curriculum/course material.
Page last updated: December 17, 2024 • Send feedback about this page