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A Brief History of Data Visualization

Jet Global

Editors note: This blog was originally published in October 2013, and has been completely revamped and updated for accuracy, relevancy, and comprehensiveness in September 2019 Prior to the 17th century, data visualization existed mainly in the realm of maps, displaying land markers, cities, roads, and resources.

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Leveraging MITRE ATT&CK: How Your Team Can Adopt This Essential Framework

CIO Business Intelligence

What if there were a free, globally accessible, and open framework that could help your team map attacks, visualize strengths and weaknesses in your environment, and understand where you can strengthen controls to protect critical assets against attackers? In fact, it has been available since 2013.

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Data Lineage Through the Decades: Where It’s Going (And Where It’s Been)

Alation

Back then, visualizing impact analysis seemed futuristic with great promise. It wouldn’t be until 2013 that the topic of data lineage would surface again – this time while working on a data warehouse project. Data warehouses obfuscate data’s origin In 2013, I was a Business Intelligence Engineer at a financial services company.

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Six keys to achieving advanced container monitoring

IBM Big Data Hub

Containers have increased in popularity and adoption ever since the release of Docker in 2013, an open-source platform for building, deploying and managing containerized applications. Organizations might have different needs and different goals regarding their container strategy and must align what they measure with those goals.

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It's Not The Ink, It's The Think: 6 Effective Data Visualization Strategies

Occam's Razor

Delete anything that's redundant, and simply visualize what's left for sharper focus. The latter is especially important because it directly ties to what content the ads/marketing should contain, what the tone and texture should be of the landing page/app experience, and what we'll use to measure success (S, T, D, C metrics).

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Why you should care about debugging machine learning models

O'Reilly on Data

Partial dependence, accumulated local effect (ALE), and individual conditional expectation (ICE) plots : this involves systematically visualizing the effects of changing one or more variables in your model. 8] , [12] Again, traditional model assessment measures don’t tell us much about whether a model is secure.

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Perform time series forecasting using Amazon Redshift ML and Amazon Forecast

AWS Big Data

to create forecast tables and visualize the data. Time series data is plottable on a line graph and such time series graphs are valuable tools for visualizing the data. The data contains measurements of electric power consumption in different households for the year 2014. In our case, we use Amazon Redshift Query Editor v2.0