Scala: The Unpredicted Lingua Franca for Data Science

on YouTube ↗47m

" summary

Argument: Scala gains traction because JVM deployment, functional closures and Spark let teams run distributed analytics without language gaps; claim includes a working Spark Notebook with a published Docker image, Spark 2.0 RC support and live examples. It demonstrates RDD/DataFrame/SQL/MLlib pipelines, local libraries (Smile, H2O, Deeplearning4j), and interactive reactive charts (geo, graph, radar) driven by multiple JVMs per notebook to avoid classpath clashes. The repo has ~1,350 stars and an active ~500-person channel, and the notebook runs and reproduces the demos.

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" chapters (50)

1. 0:00Introduction
2. 1:58Agenda
3. 2:20Why Scala
4. 4:24Spark Notebook
5. 5:47Markdown
6. 6:30Scala Features
7. 7:33Functional Programming
8. 10:43Spark
9. 12:13Python
10. 13:12Tuples
11. 14:35Pattern Matching
12. 14:49Case Classes
13. 16:05Type Inference
14. 16:54Type Safety
15. 17:25DomainSpecific
16. 17:46Notebooks
17. 17:59Spark Sessions
18. 18:34Caching
19. 19:01Sequel Query
20. 20:20Benefits of Scala
21. 20:34Scala Disadvantages
22. 21:51Teasers for tomorrow
23. 22:04We are still lacking
24. 22:41Spy Notebook
25. 25:02Spark Context
26. 25:29Notebook Driver
27. 25:57Notebook Stability
28. 26:07Notebook Objects
29. 26:19Legacy Objects
30. 27:43Spark Connector
31. 27:54Metadata
32. 28:24Logs
33. 29:01Side Panel
34. 30:26Plotting
35. 31:34Graph DSL
36. 32:23GeoPoint Shot
37. 32:57Reshape Data
38. 34:15Parse Data
39. 34:37Create a Dual Chart
40. 34:57Create a Rudder
41. 35:28Parallel coordinates
42. 35:54Time series
43. 36:09Time service
44. 36:28Reactive components
45. 37:11Synchronization
46. 39:34Models Algorithms
47. 43:11Spark Streaming
48. 44:11Other libraries
49. 44:41Docker
50. 45:16Courses

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