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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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5 Big Data Challenges in 2024

Knowledge Hut

Foresighted enterprises are the ones who will be able to leverage this data for maximum profitability through data processing and handling techniques. With the rise in opportunities related to Big Data, challenges are also bound to increase. Below are the 5 major Big Data challenges that enterprises face in 2024: 1.

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Who is a Big Data Engineer? Skills, Responsibilities, Salary

Knowledge Hut

Wondering what is a big data engineer? As the name suggests, Big Data is associated with ‘bigdata, which hints at something big in the context of data. Big data forms one of the pillars of data science. Who Is a Big Data Engineer?

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Who is a Big Data Engineer? Skills, Responsibilities, Salary

Knowledge Hut

Wondering what is a big data engineer? As the name suggests, Big Data is associated with ‘bigdata, which hints at something big in the context of data. Big data forms one of the pillars of data science. Who Is a Big Data Engineer?

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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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Big Data Technologies that Everyone Should Know in 2024

Knowledge Hut

Big data in information technology is used to improve operations, provide better customer service, develop customized marketing campaigns, and take other actions to increase revenue and profits. It is especially true in the world of big data. It is especially true in the world of big data.

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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.