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30 Best Data Science Books to Read in 2023

Analytics Vidhya

Introduction Data science has taken over all economic sectors in recent times. To achieve maximum efficiency, every company strives to use various data at every stage of its operations.

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Data Science Blogathon 30th Edition- Women in Data Science

Analytics Vidhya

The Biggest Data Science Blogathon is now live! Martin Uzochukwu Ugwu Analytics Vidhya is back with the largest data-sharing knowledge competition- The Data Science Blogathon. Knowledge is power. Sharing knowledge is the key to unlocking that power.”―

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Build a Full Big Data Platform Right Away?

Team Data Science

Should companies go full blowing big data/data science platform right away? Are you in the Proof-of-Concept phase, where you are just working with offline data, where you are proving your concepts? To answer the question at the beginning: No, I wouldn't go for a big data solution.

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The Downfall of the Data Engineer

Maxime Beauchemin

This post follows up on The Rise of the Data Engineer , a recent post that was an attempt at defining data engineering and described how this new role relates to historical and modern roles in the data space. In my experience, it’s rare to find any sort of decent dev or test environments in the big data world.

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7 Resources to Becoming a Data Engineer

KDnuggets

An estimated 8,650% growth of the volume of Data to 175 zetabytes from 2010 to 2025 has created an enormous need for Data Engineers to build an organization's big data platform to be fast, efficient and scalable.

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Why you should not learn everything in Data Science

Team Data Science

"Since I started exploring Data Engineering, it has been overwhelming. All the technology and Data Science hype. So here is the trend analysis on the topic of Big Data. If you look at this, you can see that a few years ago, everyone was talking about Big Data and how Big Data revolutionizing everything.

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Brief History of Data Engineering

Jesse Anderson

They were the first companies to commercialize open source big data technologies and pushed the marketing and commercialization of Hadoop. It was the place where the brightest big data minds came and spoke. Big data projects were given to data scientists and data warehouse teams, where the projects subsequently failed.