tl;dr
Today, we are announcing the open and public availability of “Data Literacy with Data Commons” which 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.
This material can be accessed on our courseware page and it is open for anyone to take advantage of. If you use any of this material, we would love to hear from you! If you end up finding any of this material useful and would like to be notified of updates, do drop us a line.
What is it?
A set of modules focusing on several key concepts focusing on 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!
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 curriculum page details the curriculum organization and how to find key concepts/ideas to use.
What’s Different?
There are several excellent courses which range from basic data analysis to advanced data science. We make no claim about “Data Literacy with Data Commons” being a replacement for them. Instead, we hope for this curriculum to become a useful starting point for those who want to whet their appetite in becoming data literate. This material uses a hands on approach, replete with real world examples but without requiring any programming. It also assumes only a high-school level of comfort with math and statistics. Data Commons is a natural companion platform to enable easy access to data and core visualizations. We hope that anyone exploring the suggested examples will rapidly be able to explore more and even generate new examples and case studies on their own! If you end up finding and exploring new examples and case studies, please share them with us through this form.
What is Data Literacy?
What does it mean to be “data literate”? Unsurprisingly, the answer depends on who one asks: from those who believe it implies being a casual consumer of data visualizations (in the media, for example) to those who believe that such a person ought to be able to run linear regressions on large volumes of data in a spreadsheet. Given that most (or all) of us are proliferate consumers of data, we take an opinionated approach to defining “data literacy”: someone who is data literate ought to be comfortable with consuming data across a wide range of modalities and be able to interpret it to make informed decisions. And we believe that data literacy ought not to be exclusionary and should be accessible to anyone and everyone.
There is no shortage of data all around us. While some of it will always be beyond the comprehension of most of us, e.g. advanced clinical trials data about new drugs under development or data reporting the inner workings of complex systems like satellites, much of the data we consume is not as complex and should not need advanced degrees to consume and decipher. For example, the promise of hundreds of dollars in savings when switching insurance providers or that nine out of ten dentists recommend a particular brand of toothpaste or that different segments of the society (men, women, youth, veterans etc) tend to vote a certain way on specific issues. We consume this data regularly and being able to interpret it to draw sound conclusions ought not to require advanced statistics.
Unfortunately, data literacy has been an elusive goal for many because it has been gated on relative comfort with programming or programming-like skills, e.g. spreadsheets. We believe data literacy should be more inclusive and require fewer prerequisites. There is no hiding from a basic familiarity with statistics, e.g. knowing how to take a sample average—after all, interpreting data is a sStatistical exercise. However, for a large majority of us the consumption, interpretation and decision-making based on data does not need a working knowledge of computer science (programming).
As a summary, our view on “Data Literacy” can be described as follows:
- Ability to consume, understand, create, and communicate with data.
- Ability to make decisions based on data.
- And to do so confidently, i.e. reduce “data anxiety”.
- A skill for everyone, not just “data scientists”.
With these goals in mind, we hope that this introductory curriculum can help the target audiences towards achieving data literacy and inspire many to dive deeper and farther to become data analysts and scientists.
Crystal, Jehangir, and Julia, on behalf of the Data Commons team