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Deciphering The Seldom Discussed Differences Between Data Mining and Data Science

Smart Data Collective

You may not even know exactly which path you should pursue, since some seemingly similar fields in the data technology sector have surprising differences. We decided to cover some of the most important differences between Data Mining vs Data Science in order to finally understand which is which. What is Data Science?

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The top 15 big data and data analytics certifications

CIO

The certification focuses on the seven domains of the analytics process: business problem framing, analytics problem framing, data, methodology selection, model building, deployment, and lifecycle management. They know how to assess data quality and understand data security, including row-level security and data sensitivity.

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The Evolving Role of Analytics in Supply Chain Security

Smart Data Collective

For example, more companies than ever are using analytics to bolster their security. They are also using data analytics tools to help streamline many logistical processes and make sure supply chains operate more efficiently. The market for security analytics will be worth over $25 billion by 2026.

Security 316
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What is a data architect? Skills, salaries, and how to become a data framework master

CIO

Solutions data architect: These individuals design and implement data solutions for specific business needs, including data warehouses, data marts, and data lakes. Application data architect: The application data architect designs and implements data models for specific software applications.

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What is data governance? Best practices for managing data assets

CIO

It encompasses the people, processes, and technologies required to manage and protect data assets. The Data Management Association (DAMA) International defines it as the “planning, oversight, and control over management of data and the use of data and data-related sources.”

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10 Key Data Mining Challenges in NLP and Their Solutions

Dataversity

Even as we grow in our ability to extract vital information from big data, the scientific community still faces roadblocks that pose major data mining challenges. In this article, we will discuss 10 key issues that we face in modern data mining and their possible solutions.

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Belcorp reimagines R&D with AI

CIO

It’s worth noting that each initiative carried its own unique complexity, such as varying data sizes, data variety, statistical and computational models, and data mining processing requirements. Working with non-typical data presents us with a reality where encountering challenges is part of our daily operations.”