What is Data Chefs?
Data Chefs is a diverse community of learners bound by the goal of making rigorous data science accessible through open source tools. Whereas others seeking to teach data science focus on training people to master highly specialized skills or complicated tools, we seek to transform the community that builds the tools. We have charged this community with creating tools and approaches that make it easier for people to learn at a steady pace.
What do we believe?
The ultimate goal of Data Chefs is to transform data science tools and the data science community in order to make both more accessible.
Below is our depiction of the current landscape of efforts to bring data science to a wider community:
- Little overlap among tools, data science community, and broader community
- Tools have outsized influence
- Community voice is secondary
We believe that the ideal Data Science community should look like this:
- Considerable overlap among tools, data science community, and broader community
- Tools are just one component, not the driver
- Community voice is central
To make this ideal data science community a reality, Data Chefs has committed to the specific goals highlighted below:
• Smooth the learning curve among these tools
• Make collaboration across tools and groups easier
• Build a diverse, inclusive Data Science
What do ideal Data Chefs tools look like?
Take a look at the chart below. On the horizontal axis, there is a continuum from accessible to specialized. By accessible, we mean simple enough to be understood by newcomers; by specialized, we mean relating to technical expertise. These characteristics correspond to novice and expert, respectively. On the vertical axis, the continuum is from functional to aesthetic—in other words, from what works to what looks good.
All four characteristics (accessible, specialized, functional, and aesthetic) are an important part of data science work, and we believe that the ideal space for teaching data science through Data Chefs strikes a balance among all four characteristics, as represented by the “sweet spot” in the middle of our chart.
Our tools and approaches should be imbued with enough specialized, technical knowledge that they’re rigorous, yet they should be accessible to those without that in-depth knowledge; they should be effective, but nice to look at too.
Why the name “Data Chefs?”
Cooking is a basic social practice, but it’s also intimidating for a lot of people. In cooking, as in data science, there’s a disconnect between average people and those widely regarded as “experts.” In addition, those seeking to teach data science could learn a great deal from those who teach people how to cook. A great example is Julia Child. She was committed to teaching gourmet cooking to the masses, and she did so through her many books and TV shows. She resonated with many aspiring home chefs because she did not speak over their heads, but she also refused to dumb down her lessons. What’s more, on her later shows, she made a point to feature a diverse collection of chefs: women and men from diverse backgrounds who represented a broad range of American and global cuisines.
By emphasizing a pair of principles–maintain high technical standards while keeping in mind those who are just beginning their journey toward expertise, and always seek to learn from those who are different from you–Data Chefs embodies the approach to teaching adopted by great teachers such as Julia Child.
How can I contact Data Chefs?
You can reach us at datachefsinfo@gmail.com
You can also follow us on twitter: @data_chefs
Data Chefs 