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Thought this was interesting. Across 160 teams of researchers, just about all failed to make good life outcome predictions on things like GPA, evictions, layoffs, and others. Data followed 4.5k families across 15 years, with 13k features (varied over time). Haven't looked at it directly yet, but will be turning the docs and data inside out... In the meantime, authors claim this as showing the limits of ML. Oh, and it's published in PNAS, so you know there's some big publication energy there.
https://www.pnas.org/content/117/15/8398
Has anyone else begun to resent data science?
What is a data lake in basic terms?
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ETL
I would recommend picking an ETL tool first, then doing training for that tool. If you’re not sure what ETL tool to learn you may want to pick a company that you aspire to work for, look at the data engineer job description for that company, and focus on learning the ETL tools listed in the job description.
Some ETL tool examples at SAP Data Services, Apache Airflow, Microsoft SSIS, and AWS Glue.
Pick a cloud provider and learn about their data ingestion,storage, processing offerings. For ex: if AWS make yourself aware of Kinesis, Glue, S3, EMR,Redshift etc. Knowing Spark in and out will be really beneficial.
If I can ask, Why do you want to move from cloud & DevOps to data engineering? Any specific reasons
Thank you. Just to learn something new and add new skills :)