There is a limit to what you can do from scratch. There is also the phrase "standing on the shoulders of giants," but I would like to use articles that can be used as reference as the wisdom of our predecessors to improve our level.
-Personal notes and links about machine learning ① (Machine learning) -Personal notes and links about machine learning (2) (Deep Learning) -[Personal notes and links about machine learning ③ (BI / Visualization)] (https://qiita.com/CraveOwl/items/7846abccbbaebed6ce63)
Python
Matplotlib is often used for Python graphs, but the articles are organized with the need to make graphs a little cooler.
seaborn -Use seaborn if you want to visualize your data in python!
Yellowbrick -[Python] Library "Yellowbrick" that makes machine learning more visible
pandas-profiling -pandas-profiling was extremely convenient for exploratory data analysis-Google Colab edition -Tutorial
BI
Speaking of BI, Tableau and Looker are famous these days, but I like Google Data Portal because of various things.
DataPortal
-Links to see when beginners get started with Data Portal (formerly Data Studio)
-Data Portal ✕ Community Visualization Summary
Tableau
-Data science starting from Tableau
Open Street Map
ArcMap
BigQuery GIS
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