Using data science to understand skills learned in Coursera courses | Coursera Community
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Using data science to understand skills learned in Coursera courses

  • 1 February 2019
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Using data science to understand skills learned in Coursera courses
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Sign up for this live webinar with Rachel Reddick, Senior Data Scientist at Coursera. Rachel will be talking about:
  • Her background – how she got to where she is
  • How skills are handled in Coursera's skills graph
  • Current applications, including how the skills graph was built and how skills relate to finding relevant content
  • Where skills are going next
This webinar took place on 7 February 2019. Rachel's presentation slides are attached.

Webinar Recording


Some of the questions asked and answered in the webinar include (more questions answered in the webinar can be found here):
  • Do you collect the tag info "is taught by" in what you will learn about this course? Some courses have this info under "About this Course."
  • Do you have your own search engine or do you use something like solr? was it difficult to add your new module to existing infrastructure?
  • Re: R vs Python: I teach Python, but have no experience of R. I read a recent article that suggested that R was basically dead, and that Python was the way to go. Is that overstatement?
  • Do you think any of the work that you've done can translate to non-Coursera communities? Specifically ML developers?
  • Do you have a knowledge engineer on your team? How do you know that there is no better ontology for your purposes?
  • How important is understanding the business domain for data scientists? How much knowledge data scientist have related to business analysis to identify and work on different business problems?
  • How many tags do you have? What does the classifier that predicts them look like? What metrics do you use to assess them?
  • Have you ever looked at trying to analyse the learning style of learners ie preference to reading/videos/quizzes/assignments. Could identifying ratio of reading to videos to quizzes to assignments etc. help with recommendations?
These are awesome questions! Keep the conversation going by sharing your thoughts on these questions below – and feel free to share new questions, too.

About the Speaker
Rachel Reddick received a PhD in astrophysics from Stanford, but realized she wanted to do more down-to-earth work. After some exploration, she switched into data science. She first worked as a data scientist at Bosch, a large manufacturing company. Afterwards, she joined Coursera, where she's been working for almost two years.

2 replies

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Hi, could I have the slide in PDF ? 😃 Thank you !!
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Hi @Patrick Phat Nguyen. Thanks for your interest in Rachel's slides. I've attached her slides as a PDF to the post above.

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