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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

What is data analytics? 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?

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Data Analytics: The Four Approaches to Analyzing Data and How To Use Them Effectively

KDnuggets

You will learn about descriptive analytics, data warehousing, machine learning, and big data.

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Master the Power of Data Analytics: The Four Approaches to Analyzing Data

KDnuggets

Learn about descriptive analytics, data warehousing, machine learning, and big data.

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Three Types of Actionable Business Analytics Not Called Predictive or Prescriptive

Rocket-Powered Data Science

In these applications, the data science involvement includes both the “learning” of the most significant patterns to alert on and the improvement of their models (logic) to minimize false positives and false negatives. Cognitive analytics is basically the opposite of descriptive analytics.

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6 Case Studies on The Benefits of Business Intelligence And Analytics

datapine

Using business intelligence and analytics effectively is the crucial difference between companies that succeed and companies that fail in the modern environment. BI users analyze and present data in the form of dashboards and various types of reports to visualize complex information in an easier, more approachable way.

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

Smart Data Collective

Built-in Data Analytics Tools: Python has some built-in data analysis tools that make the job easier for you. For example, the Impute library package handles the imputation of missing values, MinMaxScaler scales datasets, or uses Autumunge to prepare table data for machine learning algorithms.