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Top 14 Must-Read Data Science Books You Need On Your Desk

Data Pine

Why You Need To Read Data Science Books. Before we tell you why each of our entries makes the best books on data science, it’s important to give you a little context on this most exciting of modern fields. In 2013, less than 0.5% of all available data was analyzed, used, and understood. click for book source**.

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150+ Top Global Cloud Thought Leaders and Next Generation Leaders of 2021

Whizlabs

Currently she works at Microsoft and concentrates mainly on cloud computing, edge computing, distributed systems and architecture, and a little bit of machine learning and artificial intelligence. Jason is the author or coauthor of four books – The Agile Architecture Revolution (Wiley, 2013), Service Orient or Be Doomed!

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Need for Data Modernization in Healthcare

GAVS Technology

The healthcare industry has more information than it can possibly make sense of, and it is likely that this data explosion will only continue to grow with time. So, what’s exactly behind the data overflow that we are seeing in the healthcare industry today? In 2013, the same figure was 5,500 miles – the rise is staggering.

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Data Vault 2.0: What You Need to Know

Astera

With rising data volumes, dynamic modeling requirements, and the need for improved operational efficiency, enterprises must equip themselves with smart solutions for efficient data management and analysis. This is where Data Vault 2.0 It supersedes Data Vault 1.0, It supersedes Data Vault 1.0, Data Vault 2.0

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Deep Learning Would Be Crucial Under Sanders’s Medicare for All System

Smart Data Collective

The professional conference Digital Health Summit was held at the CES in Las Vegas in January 2013. Among other issues, the conference proposed reducing healthcare costs through the use of big data and machine learning tools. It will be especially important in the United States as we discuss transitioning to a Medicare for All System.

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What Is Embedded Analytics?

Insight Software

In the past, data visualizations were a powerful way to differentiate a software application. Companies like Tableau (which raised over $250 million when it had its IPO in 2013) demonstrated an unmet need in the market. Strategic Objective Enjoy the ultimate flexibility in data sourcing through APIs or plug-ins.