Reproducible Dashboards and other great things to do with Jupyter
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Mac Rogers of Domino Data Lab discusses Jupyter
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Reproducible Dashboards and other great things to do with Jupyter
- 1. Reproducible Dashboards ©2017 Domino Data Lab and other great things to do with Jupyter
- 2. Data Science Life Cycle Source: Philip Guo, ACM https://cacm.acm.org/blogs/blog-cacm/169199-data-science-workflow-overview-and-challenges/fulltext
- 3. ©2017 Domino Data Lab Insight to impact Machine consumers Human consumers Real-time APIs Batch APIs Lightweight, Interactive apps Scheduled reports Ad hoc reports
- 4. ©2017 Domino Data Lab Redo-ability Repeatable vs. Replicable vs. Reproducible Further reading: Mark Liberman’s Language Log - http://languagelog.ldc.upenn.edu/nll/?p=21956
- 5. ©2017 Domino Data Lab Jupyter Notebooks
- 6. ©2017 Domino Data Lab Customer Success Engineering Operations Product Management Sales Data Science Evangelist We’re Hiring! San Francisco New York London Chicago
Hinweis der Redaktion
- Hi / Welcome Thanks for joining us Intro on myself Chemical engineer by training Quantitative researcher at a hedge fund Domino helping DS do more DS, better Survey of the crowd How many data scientists Academic researchers Industries Introduce topic Reproducible dashboards - and other great things to do with jupyter Tips and tricks on how to be better at jupyter Reproducible Jupyter Dashboards
- Context, Background What is a dashboard Conceptually Place to synthesize information Technically Both the jupyter landing page A project by jupyter to enable notebooks to be dashboards Philip Guo - UC San Diego
- What is.. Reproducibility? Finance How do I make sure this works when it hits the market? When the SEC shows up? Insurance How do I prove I'm not biased? Academia / Engineering / Scientific research Distinction between reproducibility, repeatability, replicability In Data Science: Repeat - same person, same circum, same results “But it worked on my computer” Data parsing library that went from NA to 1-1-70 replicate - re-calc Same data - same functions - same environment - same result reproduce - re-start New data - new methods - same results This is more than just tools - more than just consistency - this is the entire data science system of record happening Its about a knowledge mgmt system version control system Experimentation lab Lab notebook Discussion platform Workflow platform In order to produce truly reproducible work - that someone else can build upon and come to truth with and which can be used to advance the understanding of your group, whether your team, your lab, your organization or you field, You have to do more than share data or open source your code More than version control Ultimately, it’s less about leading someone down the path you took to get there, so much as blazing such a good trial they’re able to find their own way
- open-source web application that allows you to create and share documents contain live code, equations, visualizations and narrative text More Info - jupyter.org