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Data Challenge Lab

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Where students develop their data skills by solving a progression of increasingly difficult challenges

In the Challenge Lab, students of all academic backgrounds develop the practical skills and expertise of professional data scientists. There are no prerequisites, although some coding experience is helpful.

There are no lectures in the Challenge Lab. Instead, students learn by completing a series of real-world challenges, drawn from work done by data experts. These challenges include analyzing voter behavior in a presidential election, visualizing the effects of climate change, investigating potential Medicare fraud, and understanding the dynamics of the opioid epidemic.

Through these challenges, students learn not only how to manipulate, visualize, and model data, but also how to approach open-ended data projects, ask good questions, and be skeptical about their results.

Students receive both daily feedback and one-on-one coaching, as well as the support of a small learning team of other students. Although all work is ultimately individual, members of learning teams collaborate and help one another through the challenges.

Experts are vital to the Challenge Lab. We create challenges by finding data experts and reverse-engineering their projects. We also partner with leading data scientists to understand the components of their expertise and then incorporate their skills into our curriculum.

The Challenge Lab is experimental and constantly evolving. We believe that we can always improve, and so continually update our curriculum and format. For more information on the topics covered in the class, you can explore our most recent curriculum in the lab's open content.

The class has limited enrollment. It is required to attend an information session and then complete an application. Information sessions are held at the end of the preceding quarters.

Students in the Stanford Data Challenge Lab confront the messiness of data, Stanford News.

ENGR 150: Data Challenge Lab
Terms: Win, Spr | Units: 5 | UG Reqs: WAY-AQR, WAY-CE
Instructors: Sara Altman, Bill Behrman, Hadley Wickham