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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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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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The State of Data Engineering at Data Day Texas 2024

Jesse Anderson

The premier of my latest talk covering The State of Data Engineering. This starts with data warehousing and goes into data science. I finish off by showing how data engineering can avoid the same fate as data warehousing and data science.

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8 In-Demand Data Science Certifications for Career Advancement [2023]

Analytics Vidhya

As recruiters hunt for professionals who are knowledgeable about data science, the average median pay for a proficient Data Scientist has soared to $100,910 […] The post 8 In-Demand Data Science Certifications for Career Advancement [2023] appeared first on Analytics Vidhya.

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Academia to Industry: Data Science Graduate Programs for South Africa’s Future

Analytics Vidhya

Introduction South Africa is not an exception as data science-driven economic change sweeps the world. The nation is seeing an increase in demand for qualified data science workers as a result of its booming IT sector and developing data-driven industries.

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Data Engineering Weekly #181

Data Engineering Weekly

Editor’s Note: A New Series on Data Engineering Tools Evaluation There are plenty of data tools and vendors in the industry. Data Engineering Weekly is launching a new series on software evaluation focused on data engineering to better guide data engineering leaders in evaluating data tools.

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GPT and LLMs from a Data Engineering Perspective

Jesse Anderson

There has been quite a bit of writing covering GPT and LLMs from data science and business perspectives. I haven’t seen much from the data engineering side. Let me share my perspective, having been in data and AI for a while and using LLMs before they became popular. How can we use LLMs in data engineering?