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An Accurate Approach to Data Imputation

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction In order to build machine learning models that are highly generalizable to a wide range of test conditions, training models with high-quality data is essential.

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Mobile Data Collection: What it is and what it can do

FineReport

Data collection is nothing new, but the introduction of mobile devices has made it more interesting and efficient. But now, mobile data collection means information can be digitally recording on the mobile device at the source of its origin, eliminating the need for data entry after the information is collected.

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The quest for high-quality data

O'Reilly on Data

These data sets are often siloed, incomplete, and extremely sparse. Moreover, the domain knowledge, which often is not encoded in the data (nor fully documented), is an integral part of this data (see this article from Forbes). See this article on data integration status for details.

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Why Nonprofits Shouldn’t Use Statistics

Depict Data Studio

Today’s article comes from Maryfrances Porter, Ph.D. & — Thank you to Ann Emery, Depict Data Studio, and her Simple Spreadsheets class for inviting us to talk to them about the use of statistics in nonprofit program evaluation! . Why Nonprofits Shouldn’t Use Statistics. & Alison Nagel, Ph.D And here’s why!

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Analytics Insights and Careers at the Speed of Data

Rocket-Powered Data Science

This article quotes an older market projection (from 2019) , which estimated “the global industrial IoT market could reach $14.2 Embedding real-time dynamic analytics at the edge, at the point of data collection, or at the moment of need will dynamically (and positively) change the slope of your business or career trajectory.

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Bringing an AI Product to Market

O'Reilly on Data

In this article, we turn our attention to the process itself: how do you bring a product to market? The development phases for an AI project map nearly 1:1 to the AI Product Pipeline we described in the second article of this series. Acquiring data is often difficult, especially in regulated industries. Identifying the problem.

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Managing risk in machine learning

O'Reilly on Data

There are also many important considerations that go beyond optimizing a statistical or quantitative metric. As we deploy ML in many real-world contexts, optimizing statistical or business metics alone will not suffice. How to build analytic products in an age when data privacy has become critical”. Culture and organization.