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They promise to revolutionize how we interact with data, generating human-quality text, understanding natural language and transforming data in ways we never thought possible. From automating tedious tasks to unlocking insights from unstructureddata, the potential seems limitless. You get the picture.
Though you may encounter the terms “data science” and “dataanalytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, dataanalytics is the act of examining datasets to extract value and find answers to specific questions.
This blog explores the challenges associated with doing such work manually, discusses the benefits of using Pandas Profiling software to automate and standardize the process, and touches on the limitations of such tools in their ability to completely subsume the core tasks required of data science professionals and statistical researchers.
The value of Big Data is not solely dependent on the volume of data available, but on how it is utilized. The Big Data ecosystem is rapidly evolving, offering various analytical approaches to support different functions within a business. DescriptiveAnalytics is used to determine “what happened and why.”
According to Fortune Business Insights approximately 67% of the global workforce has access to business intelligence (BI) tools, and 75% has access to dataanalyticssoftware. So why would any organization that considers a decision critical use business intelligence data to make that decision?
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