lessons from teaching data science to over a million people · the data science specialization •...
TRANSCRIPT
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Lessons from teaching data science to over a
million people
Sean KrossCRUNCH Conference
Budapest2017-10-20
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A little about me
• Formerly: The Johns Hopkins Data Science Lab
• Currently: The University of California San Diego
• Main interests:• Data Science• Online Education• Open Science
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Jeff Leek Roger Peng Brian Caffo
The Johns Hopkins Data Science Lab
@rdpeng @bcaffo@jtleek
jhudatascience.org
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?
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Part 1: The Data Science Specialization
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https://peerj.com/collections/50-practicaldatascistats/
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• Forecasting at Scale by Sean Taylor and Benjamin Letham (Facebook)
• How to Share Data for Collaboration by Shannon Ellis and Jeffrey Leek (Johns Hopkins Data Science Lab)
• Opinionated Analysis Development by Hilary Parker (Stitch Fix)
• Data Organization in Spreadsheets by Karl Broman and Kara Woo (The University of Wisconsin & DataCamp)
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Rationale: “Let’s put in-person courses online to augment in-person teaching.”
We were on to something.
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Nine Courses1. The Data Scientist’s Toolbox
2. R Programming
3. Getting and Cleaning Data
4. Exploratory Data Analysis
5. Reproducible Research
6. Statistical Inference
7. Regression Models
8. Practical Machine Learning
9. Developing Data Products
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Enrollment and Completions of the Data Science Specialization
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Key innovations
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Give everything away for free
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Capstones -> Portfolios -> Jobs
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Run every course every month
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Integrate content
https://ubc-mds.github.io/
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Lessons from Alumni: The Data Science Specialization
• Data scientists want to create online artifacts. See Stitch Fix and Stack Overflow’s technical blogs. Also see open source software projects like Prophet, a forecasting library from Facebook.
• Data Scientists want to be able to do in-house data science training.
• Domain expertise is important but so is buy-in from management.
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Part 2: Executive Data Science
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Lessons from Alumni: Executive Data Science
• Data science technologies tend to “trickle up.” (Especially good to know if you develop data science technologies.)
• Invest your precious time into basic statistics over basic programming.
• Your expectations for developing an analysis should resemble your expectations for developing software.
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Part 3: Mastering Software Development in R
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Data Scientist
Data Engineer
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Lessons from Alumni: Mastering Software Development in R
• The field of data science is still taking form.
• Experimentation with roles can give you a competitive advantage.
• Taking risks is easier of you’re part of a community.
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?
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• Learn how to use the command line from the ground up.
• No previous experience expected.
• The gateway into computationally intensive tasks.
• Includes an introduction to cloud computing.
• Free!
leanpub.com/unix
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Thank you!
Questions?
Link to these slides: seankross.com/crunch-talk/
Let’s talk: [email protected]
Find me on Twitter: @seankross