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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.

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DirectX Visualization Optimizes Analytics Algorithmic Traders

Smart Data Collective

Analytics technology has become an invaluable aspect of modern financial trading. A growing number of traders are using increasingly sophisticated data mining and machine learning tools to develop a competitive edge. For example, when your trading algorithm makes losses or a particular threshold or condition is met.

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What is NLP? Natural language processing explained

CIO

Search engines, machine translation services, and voice assistants are all powered by the technology. How natural language processing works NLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning.

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What is predictive analytics? Transforming data into future insights

CIO

billion in 2022, according to a research study published by The Insight Partners in August 2022. Predictive analytics in business Predictive analytics draws its power from a wide range of methods and technologies, including big data, data mining, statistical modeling, machine learning, and assorted mathematical processes.

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What is data analytics? Analyzing and managing data for decisions

CIO

It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. What are the four types of data analytics?

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An Important Guide To Unsupervised Machine Learning

Smart Data Collective

Unsupervised ML uses algorithms that draw conclusions on unlabeled datasets. As a result, unsupervised ML algorithms are more elaborate than supervised ones, since we have little to no information or the predicted outcomes. Overall, unsupervised algorithms get to the point of unspecified data bits. Source ].

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What is a data engineer? An analytics role in high demand

CIO

Data engineers also need communication skills to work across departments and to understand what business leaders want to gain from the company’s large datasets. Data engineer vs. data architect. The data engineer and data architect roles are closely related and frequently confused.

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