Session recording at https://youtu.be/gm5nohV30fE Abstract: Power BI is a widely successful tool for Business Intelligence projects, but there’s lots of value for Data Scientists too. This session will explore how to leverage Power BI for Data Science & Machine Learning workloads. From powerful & effortless EDA, built-in anomaly detection to R & Python integration, and even AutoML capabilities, we’ll see how these and other features can contribute to better AI project productivity and outcomes. Can Power BI be a superpower for Data Scientists? We’ll find out!

1. (Watch this session recording at https://youtu.be/gm5nohV30fE )

2. Twitter @rquintino
Medium /devscope-ai
Github /DevScope/ai-lab

7. https://www.sns.gov.pt/profissional/recursos-humanos-da-saude/informacao-rh/analise-mensal-do-balanco-social/

9. Visual & Code Data Prep / Intellisense / Profiling / ...

10. Fast (column store) Semantic Models

11. Powerful Analytical Engine for Metric Modelling

12. Filtering / Cross Filtering / ToolTips / Drill down/up / ...

14. Power BI Desktop – Free, Offline, Feature Rich

17. Disclaimer: Know a bit of R Shiny...

18. And Python Plotly Dash...

19. Big fans of Python StreamLit... (credits to Luís Costa ☺)

20. but... Business Reports/Dashboards with
Shiny, Python, StreamLit, just don’t!

21. https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining

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Delivering/Presenting DS/ML Outputs/Insights

23. Reusable TimeSeries/Forecast EDA

24. Eval performance & behaviors for different models

25. Awesome for ML Competitions ( I kid you not! ☺)

26. Leaderboards (ex: submission evaluation/validation)

27. 27
Machine Learning Governance (msft CGA team Cloud+AI)

29. Custom R and Python interactive EDA

30. Use natural language for quick questions & visuals

31. Visual creation using Q&A feature

33. Powerfull & Easily configurabe Drill Through Scenarios

34. Native Forecast (ETS based)

35. Built-in Clustering Support

36. Root causes (explain the increase/decrease)

38. Anomaly Detection and Root Cause Analysis

39. Quick glimpse/preview of Key predictors/”influencers”

40. Key “influencers” visual

41. Distribution drivers & outliers

44. Easy Refactoring, renames, measure formats,...

45. Reproducibility (just open it!)

46. What-ifs/Interactive Parameters

47. *New* Zoom Slider

48. *New* Native Small Multiples

50. Semantic Models based Auto ML (cloud/premium feature)

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64. rui.quintino@devscope.net
Linkedin.com/ in/rquintino
Twitter @rquintino
Medium /devscope-ai
Github /DevScope/ai-lab

65. Questions/ Feedback ?

66. rui.quintino@devscope.net
Linkedin.com/ in/rquintino
Twitter @rquintino
Medium /devscope-ai
Github /DevScope/ai-lab

68. Example: Natural Language Processing steps

69. Access to AI APIs (Azure Cognitive Services)

70. Sentiment Analysis of Guest Comments

71. Image Tagging and Key Phrases

74. Prep by example

75. Vision/Text AI APIs from Power BI (tagging, sentiment,...)

77. Run & Deploy Auto ML models in Azure Auto ML...

78. ... And using directly in Power BI/Power Query

81. https://towardsdatascience.com/the-data-science-landscape-f6f7842c9865