Enabling Citizen Data Scientists at Dow Chemical with Posit Academy

Led by James Wade, Associate Research Scientist at Dow Chemical

Timestamps:
2:46 - Start of presentation
5:25 - Goal: "apply science and engineering technical expertise along with data science tooling to innovate in the materials science arena."
6:36 - What does citizen data science mean?
8:05 - Data science as an interdisciplinary endeavor - looking to build a community of innovators
9:30 - Translating data to decisions
11:03 - Guidelines for success (data organizations, data access, data analysis, value preservation)
13:30 - Welcoming new users in an approachable, collaborative, and secure workspace with RStudio Team
14:25 - Making sure you can rapidly deploy your insights to others
16:25 - What is RStudio Academy?
20:55 - What do you need for academy? (Academy learners: 5-7 per cohort, cohort mentors from RStudio & your group, and a project - the closer to your work the better)
22:15 - Who is a good candidate?
23:55 - Who might not be the best candidate?
26:00 - What makes a good cohort? (similar work group, time zone, and skill level)
27:27 - Feedback (Are they still using the content they learned? 16 out of the 17 survey respondents were still writing code 6 months after)
31:42 - Community building (want to have a landing zone for people to continue to learn)
32:31 - RStudio Academy success story at Dow
35:30 - Start of Q&A portion

Questions:
36:00 - How do you help someone who knows coding would be useful but can't motivate to take 5 steps back to take 10 steps forward?
37:55 - How can more advanced users participate in developing curriculums?
39:44 - Does Academy also teach good coding style and version control?
41:00 - If you're trying to "sell" Academy to the individual who would fill the group mentor role, what level of commitment and bandwidth do they need to have?
42:13 - Is the type of data you work with relevant to the work you do at Dow? or random / set datasets regardless of which company you're with?
43:00 - What other ways of teaching R have you tried (or considered) at Dow? How does Academy compare?
44:55 - What is the duration of RStudio Academy?
46:15 - Can you have multiple cohorts go through at the same time? What if we want to up-skill hundreds of people?
48:20 - How did you find out who might be interested and get the word out?
50:08 - Advertising that you help learners up-skill in coding seems like a good way to set your company apart from others, are you hiring?
51:25 - After the RStudio Academy 10 week training is the Academy team still available for questions, support or consult?
53:01 - Is Academy only for R?
54:44 - How do you collaborate with others outside of Dow?
57:20 - How does RStudio Academy handle sensitive data?
1:00:20 - Do you have statistics on how many graduates are still using R?

Abstract:
In chemistry and materials science research, data is messy, unstructured, and scattered. Solving this problem requires researchers to deeply embed within data generation and analysis workflows.

We are on a multi-year journey to equip scientists and engineers with guidance and tools to extract insights from their data. To this end, we have developed a set of 15 guidelines designed to move our organization toward a collaborative, reproducible work process in a dynamic data-diverse environment.

In this talk, I will share our lessons from this journey learned through teaching, community building, and collaboration with a particular focus on the integration of language agnostic RStudio tools, products, and programs. I will especially be focusing on our experience with RStudio Academy.

Speaker Bio:
James is a research scientist working in the chemicals manufacturing industry as part of a research and development team. James applies materials characterization and data science with a special interest in sustainable materials design to develop new capabilities for research. His current focus is on augmenting materials characterization innovations with statistical analysis, machine learning, and data visualization.