Let’s talk about reproducible data analysis
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The document discusses common problems in data analysis such as ensuring repeatability and reproducibility, using multiple tools and data sources, enabling collaboration between users of different skill levels, deployment of models and results, and organization of work. It introduces KNIME as an open source platform that can help address these problems through its use of workflows to capture parameters, data, and analysis steps in a visual interface, allowing for interactive, reproducible, collaborative, deployable, and findable data analysis.
Let’s talk about reproducible data analysis
- 1. © 2018 KNIME AG. All Rights Reserved. Interactive and reproducible data analysis with the open-source KNIME Analytics Platform Greg Landrum, Ph.D. KNIME AG @dr_greg_landrum Frankfurt Data Science Meetup 18 June 2018
- 2. © 2018 KNIME AG. All Rights Reserved. Let’s talk about reproducible data analysis Greg Landrum, Ph.D. KNIME AG @dr_greg_landrum Frankfurt Data Science Meetup 18 June 2018
- 3. © 2018 KNIME AG. All Rights Reserved. 3 Some data analysis problems • Interactive data analysis and modeling • Repeatability and reproducibility
- 4. © 2018 KNIME AG. All Rights Reserved. 4 Some data analysis problems • Interactive data analysis and modeling • Repeatability and reproducibility • The need to use multiple tools and multiple data sources • Collaboration between users with different sophistication levels • Deployment • Just staying organized
- 5. © 2018 KNIME AG. All Rights Reserved. 5 Some data analysis problems • Interactive data analysis and modeling • Repeatability and reproducibility • The need to use multiple tools and multiple data sources • Collaboration between users with different sophistication levels • Deployment • Just staying organized I think workflows can help with these
- 6. © 2018 KNIME AG. All Rights Reserved. 6 Some data analysis problems • Interactive data analysis and modeling • Repeatability and reproducibility • The need to use multiple tools and multiple data sources • Collaboration between users with different sophistication levels • Deployment • Just staying organized I think KNIME can help with these
- 7. © 2018 KNIME AG. All Rights Reserved. 7 The Open Source KNIME® Analytics Platform
- 8. © 2018 KNIME AG. All Rights Reserved. 8 Visual KNIME Workflows Nodes perform tasks on data Workflows combine nodes to model data flow Status Input(s) Outputs Not Configured Idle Executed Error
- 9. © 2018 KNIME AG. All Rights Reserved. 9 Workflows capture parameters and data https://www.knime.com/blog/stuck-in-the-nine-circles-of-hell-try-parameter-optimization-a-cup-of-tea
- 10. © 2018 KNIME AG. All Rights Reserved. 10 Workflows capture parameters and data https://www.knime.com/blog/stuck-in-the-nine-circles-of-hell-try-parameter-optimization-a-cup-of-tea
- 11. © 2018 KNIME AG. All Rights Reserved. 11 Workflows capture parameters and data https://www.knime.com/blog/stuck-in-the-nine-circles-of-hell-try-parameter-optimization-a-cup-of-tea
- 12. © 2018 KNIME AG. All Rights Reserved. 12 Workflows capture parameters and data https://www.knime.com/blog/stuck-in-the-nine-circles-of-hell-try-parameter-optimization-a-cup-of-tea
- 13. © 2018 KNIME AG. All Rights Reserved. 13 Analysis & Mining Statistics, Machine Learning, Data Mining, Web Analytics, Text Mining, Network Analysis, Social Media Analysis, R, Weka, Python, Community / 3rd party, ... Data Access MySQL, Oracle, ... SAS, SPSS, ... Excel, Flat, ... Hive, Impala, ... XML, JSON, PMML Text, Doc, Image, ... Web Crawlers, Industry Specific, Community / 3rd party ... Transformation Row, Column, Matrix Text, Image, Networks, Time Series, Java, Python, Community / 3rd party, ... Visualization R, Python, JFreeChart, JavaScript, Community / 3rd party, ... Deployment via BIRT PMML, XML, JSON Databases, Excel, Flat, etc. Text, Doc, Image Industry Specific Community / 3rd party, ... Over 2000 native and embedded open-source nodes : Big Data Hive, Impala, HDFS Vertica, Teradata/Aster, Spark, MLlib, Community / 3rd party, ...
- 14. © 2018 KNIME AG. All Rights Reserved. 14 Some supported data types • Numbers, strings, etc • Bit/count vectors • Images • Documents • Networks • Chemistry types • Biology types
- 15. © 2018 KNIME AG. All Rights Reserved. 15 Free E-Learning Course: Web Page 15 • Hands-on e-learning course • Data Access, ETL, Analytics, Control Structures, Visualization • Around 50 small units • … with exercises • … and with solutions on the EXAMPLES server • Final exercises to test your knowledge! https://www.knime.org/knime- introductory-course
- 16. © 2018 KNIME AG. All Rights Reserved. 16 KNIME Beginner’s Luck Book Free Copy of KNIME Beginner’s Luck Book at KNIME Press https://www.knime.org/knimepress Promotion Code: FRANKFURT-MEETUP-0618 Valid until 31 August
- 17. © 2018 KNIME AG. All Rights Reserved. 17 The KNIME Software Ecosystem KNIME Analytics Platform KNIME Supported Extensions KNIME Extensions Partner Extensions Community Extensions KNIME Server
- 18. © 2018 KNIME AG. All Rights Reserved. 18 KNIME Server Shared Repositories Access Management Web Enablement Flexible Execution
- 19. © 2018 KNIME AG. All Rights Reserved. 19 KNIME, the company • KNIME AG founded in 2008 • Offices in Zürich (HQ), Konstanz, Berlin, and Austin • 50+ employees • Maintainer of the Open Source KNIME Analytics Platform – comprehensive data loading, processing, analysis, modeling platform – visual frontend – open: to all sorts of data, other tools (R and Python, etc.), various user personas – 20+ open source releases since 2006 – open source. • KNIME Server – 14 commercial product releases since 2008 • KNIME cloud offerings
- 20. © 2018 KNIME AG. All Rights Reserved. 20 Some data analysis problems • Interactive data analysis and modeling • Repeatability and reproducibility • The need to use multiple tools and multiple data sources • Collaboration between users with different sophistication levels • Deployment • Just staying organized
- 21. © 2018 KNIME AG. All Rights Reserved. 21 Interactive data analysis and modeling • Fairly often the whole process of data preprocessing, analysis, and modeling can’t be (or shouldn’t be) fully automated. • We want/need a human in the loop • Would be lovely if this weren’t painful Interactive
- 22. © 2018 KNIME AG. All Rights Reserved. 22 Repeatability and reproducibility • I can reproduce what I did before or repeat the same process with a different data set/method • You can do the same thing • Not necessarily talking about strict reproducibility (out to the 15th decimal place), but if we miss that we should be able discover where deviations come from • Would be lovely if this weren’t painful Reproducible
- 23. © 2018 KNIME AG. All Rights Reserved. 23 The need to use multiple tools and multiple data sources • There is no one-size-fits-all solution (or “one-stop shop”) • We’re inevitably going to be using more than one piece of software and working with data from more than one source. • Would be lovely if this weren’t painful Open
- 24. © 2018 KNIME AG. All Rights Reserved. 24 Collaboration between users with different sophistication levels • Some personae:1 – The scripter/programmer: “I’ve got this great new method you should try” – The tool user: “I’ll use software, but there’s no way I’m writing code” – The “stakeholder”: “Those folks are doing useful stuff and I need their results, but I don’t have time to learn some complex new piece of software.” • Would be lovely if enabling collaboration between these different personae wasn’t painful 1 Yes, these are stereotypes Collaborative
- 25. © 2018 KNIME AG. All Rights Reserved. 25 Deployment • Once I’ve built something I’d like to make it available to my colleagues – Sharing models – Sharing methods – Sharing results • Would be lovely if this weren’t painful Deployable
- 26. © 2018 KNIME AG. All Rights Reserved. 26 Just staying organized • I can usually remember where my scripts are • There’s no way I can remember where yours are • It would be lovely if it weren’t painful to find stuff Findable
- 27. © 2018 KNIME AG. All Rights Reserved. 27 Some data analysis problems • Interactive data analysis and modeling • Repeatability and reproducibility • The need to use multiple tools and multiple data sources • Collaboration between users with different sophistication levels • Deployment • Just staying organized
- 28. © 2018 KNIME AG. All Rights Reserved. 28 Some data analysis problems • Interactive data analysis and modeling • Repeatability and reproducibility • The need to use multiple tools and multiple data sources • Collaboration between users with different sophistication levels • Deployment • Just staying organized I think KNIME can help with these
- 29. 29© 2018 KNIME AG. All Rights Reserved. Thanks! dr_greg_landrum [email protected]
- 30. 30© 2018 KNIME AG. All Rights Reserved. The KNIME® trademark and logo and OPEN FOR INNOVATION® trademark are used by KNIME.com AG under license from KNIME GmbH, and are registered in the United States. KNIME® is also registered in Germany.