Data Science Education Blog Summer Roadmap

Data Science Education Blog Summer Roadmap

This is an exciting time to be teaching students how to extract meaning from data. Amidst the flood of information available in almost all domains there has been a flourishing of powerful, open-source tools to help with the process. For instructors, the many changes can be hard to keep up with. Through a new blog that we've created, we're hoping to create a roadmap for faculty development that will ease the learning curve and help busy people incorporate new tools and approaches into their teaching.

Each day during the summer, starting today, we will add a new entry on a given topic, along with a short overview of why it is interesting and how it can be applied to teaching. We intend to make the entries short, succinct, and easy to comprehend with the goal that they will motivate you to dive deeper. We hope that these introductory pieces can be digested daily in 20 or 30 minute chunks that will leave you in a position to decide whether to explore more or integrate the material into your own classes. We'll include next steps and additional readings to allow you explore more as you have interest and time.

There is definitely an art to googling well that not everyone (including the three of us) can master. The data science field is also moving quickly, so answers from useful sites such as Stack Overflow may be quickly out of date. Our ambition is that by reading the short overview entries, a variety of instructors will take the opportunity to learn more about the exciting developments in data science and statistics.

The blog is up and running at https://teachdatascience.com . We hope that you bookmark the site and check in regularly. Want a reminder? Sign up for emails at https://groups.google.com/forum/#!forum/teach-data-science (you must be logged into Google to sign up).

We plan to cover the entire data science analysis cycle:

• data ingestation, data technologies, and data wrangling

• visualization and exploration

• workflow and reproducibility

• communication and reporting

as well as providing overviews of key reports and findings.

We welcome suggestions for topics: don't hesitate to share your ideas (guest entries are also welcomed!)

Hunter Glanz (Cal Poly, San Luis Obispo), Johanna Hardin (Pomona College), and Nicholas Horton (Amherst College)

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