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What is data analytics? Analyzing and managing data for decisions

CIO Business Intelligence

The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance.

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An Important Guide To Unsupervised Machine Learning

Smart Data Collective

We’re living in an era of digital switch-over with only one constant – evolve. And that digital transformation is being introduced by high-tech solutions. Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. What Is Unsupervised Machine Learning?

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Adoption of Automated Sales & Underwriting Strategies can Transform Insurance

bridgei2i

As face to face contact, operating offline offices, physical audits and ancillary processes face constraints; these increasingly move to the digital bandwagon. In days ahead, digital transformation will be the saviour and guide of the industry. Images 1: Challenges before insurance in the post-Corona world.

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Trending Technologies for BI & Financial Planning and AnalysisMaking AI Real (Part 2)

Jedox

Part one of our blog series explored how people are the driving force behind the digital transformation and how it is fueled by artificial intelligence and machine learning. It runs statistics and algorithms (also known as data mining) on masses of historical data to calculate probabilities and future events.

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Are data science platforms a good idea?

CONTACT Software

Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, machine learning with stats models and scikit-learn, deep learning with TensorFlow” is one of Udemy’s course programs.

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Themes and Conferences per Pacoid, Episode 7

Domino Data Lab

O’Reilly Media had an earlier survey about deep learning tools which showed the top three frameworks to be TensorFlow (61% of all respondents), Keras (25%), and PyTorch (20%)—and note that Keras in this case is likely used as an abstraction layer atop TensorFlow. The data types used in deep learning are interesting.