ETL Pipelines
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01. Introduction
02. Lesson Overview
03. World Bank Datasets
04. How to Tackle the Exercises
05. Extract
06. Exercise: CSV
07. Exercise: JSON and XML
08. Exercise: SQL Databases
09. Extracting Text Data
10. Exercise: APIs
11. Transform
12. Combining Data
13. Exercise: Combining Data
14. Cleaning Data
15. Exercise: Cleaning Data
16. Exercise: Data Types
17. Exercise: Parsing Dates
18. Matching Encodings
19. Exercise: Matching Encodings
20. Missing Data - Overview
21. Missing Data - Delete
22. Missing Data - Impute
23. Exercise: Imputation
24. SQL, optimization, and ETL - Robert Chang Airbnb
25. Duplicate Data
26. Exercise: Duplicate Data
27. Dummy Variables
28. Exercise: Dummy Variables
29. Outliers - How to Find Them
30. Exercise: Outliers Part 1
31. Outliers - What to do
32. Exercise: Outliers - Part 2
33. AI and Data Engineering - Robert Chang Airbnb
34. Scaling Data
35. Exercise: Scaling Data
36. Feature Engineering
37. Exercise: Feature Engineering
38. Bloopers
39. Load
40. Exercise: Load
41. Putting It All Together
42. Exercise: Putting It All Together
43. Lesson Summary
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19. Exercise: Matching Encodings
Code
If you need a code on the
https://github.com/udacity
.
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