Today, businesses are aware that a huge part of their decision-making is impacted by big data. The large availability of data does not warrant its relevancy and neither does the analysis of big data by data scientists and analysts, as human judgment can sometimes be flawed. Moreover, several factors may impact data, either positively or negatively. As a result, data may fluctuate from time to time. That is why it becomes crucial for data teams to know how to make the right inferences from big data. This is only possible when data analysts and scientists are aware of the existential biases and the solutions to them.
Special thanks to Nate DW for the link to this article. The best one of the five of these is “Simpson’s Paradox”. No, not the one where Homer smashed his little boy’s piggy bank and is wondering what he’s done. It’s when you notice a pattern in groups of data that favors a trend but, when you look at the cumulative patterns of the groups, the trend looks totally different. This is an excellent read for those of you who are labeling yourselves “Data Scientist”. I’m just a “Data Tinkerer”.
Via: 5 common biases in big data
“A picture is worth a thousand words”, the old saying goes, and in some cases, a picture is worth even more than that. The human eye is composed of some 30 or more discrete components, and along with the optical nerves and the brain functions that process sight, can take in a contrast ratio of around 100,000:1 (over time) and can distinguish about 10 million colors. That sight-brain-pathway is a pattern-matching wonder and has “regions of interest” that the eye/brain connection focuses on (http://www.cambridgeincolour.com/tutorials/cameras-vs-human-eye.htm).
Making up one of our primary senses, sight is immeasurably important to conveying information, and it’s vital to the Data Scientist to understand how to best use various visualizations to display and discuss data.
There is a book reference in this article from 2013 that still is a must-read for anyone attempting data visualization at any level. The best lesson is to look through other people eyes to appreciate how the information must “Look”. I go by a simple rule, “If my wife, who is not technical or a data scientist, can’t understand the visual it probably needs more work”
Via: Microsoft Developer, Buck Woody
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If your website is data-intensive, then you will need to find a way to make that data easy to visualize. Humans, after all, are not wonderful at understanding long lists of raw numbers. That’s where charts and graphs come in — they can make complicated statistical relationships obvious and intuitive, as well as more accessibile…
via An Introduction to Chart.js 2.0 — Six Simple Examples — SitePoint
This is a very good article to read. It is academic based but still is very relevant to business. Data Science was a term coined in 1974, it was one of the courses I took in college. Now it is back again, to define some skills you should consider learning to help manage and use any “Big Data” you may have.