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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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What is data analytics? Analyzing and managing data for decisions

CIO

Data analytics is a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. What are the four types of data analytics? Data analytics methods and techniques.

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DirectX Visualization Optimizes Analytics Algorithmic Traders

Smart Data Collective

A growing number of traders are using increasingly sophisticated data mining and machine learning tools to develop a competitive edge. If your trade analysis and trials are a pain in the neck or you barely register any results, you’ll undoubtedly be looking for ways to change things up. This is according to Danyel Fisher.

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The top 15 big data and data analytics certifications

CIO

Certification of Professional Achievement in Data Sciences The Certification of Professional Achievement in Data Sciences is a nondegree program intended to develop facility with foundational data science skills. They can visualize and present data findings in dashboards, presentations, and commonly used visualization platforms.

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12 data science certifications that will pay off

CIO

Cost: $180 per exam Location: Online Duration: Self-paced Expiration: Credentials do not expire SAS Certified Advanced Analytics Professional The SAS Certified Advanced Analytics Professional credential validates your ability to analyze big data with a variety of statistical analysis and predictive modeling techniques.

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Python for Business: Optimize Pre-Processing Data for Decision-Making

Smart Data Collective

Accordingly, before using that data in machine learning or an algorithm, you need to convert it into a precise format suitable for the system to inherit it. For instance, the Random Forest Algorithm in Python doesn’t support null values. Hence, data preprocessing is essential and required.

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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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