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Self Driven Data Science — Issue #39
Here’s this weeks lineup of data-driven articles, stories, and resources delivered faithfully to your inbox for you to consume. Enjoy!
A Beginner’s Guide to Data Engineering — Part II
In this follow-up article, the author goes a bit more in-depth, focusing on building good data pipelines and highlighting ETL best practices using Python, Airflow, and SQL.
Stop Looking for Data Scientists
We are asking the wrong things from Data Scientists and we are looking in the wrong places. The author argues that data science is more about the intelligent use of programming, rather than programming itself.
When K-Means Clustering Fails
How do we segment our market? Typically, one of the first approaches is by K-means clustering. As a popular data clustering technique, K-means is effective for project necessitated market segmentation.
How to Datalab: Running Notebooks Against Large Datasets
Streaming your big data down to your local environment is slow and costly. This article helps you further utilize interactive Python notebooks by running them in the cloud and therefore improving speed and data connectivity.
Predicting Upsets in the NCAA Tournament
It’s time for March Madness! Picking upsets correctly can distinguish your bracket and give you a competitive edge in your pool. This exploratory data analysis dives into predicting possible upsets and how to use an algorithmic edge to beat the odds.
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Self Driven Data Science — Issue #39 was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.
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The views and opinions expressed in this article are solely those of the authors and do not reflect the views of Bitcoin Insider. Every investment and trading move involves risk - this is especially true for cryptocurrencies given their volatility. We strongly advise our readers to conduct their own research when making a decision.