On timing (data of the data)

Analysts of all kinds study integrity, transformation, and presentation of data.

The human mind can only handle so much information (3-7 visual concepts at once). To have the best effect and immediate influence, a common GIS philosophy applied at Carnegie Mellon is to minimize ink, simplify symbols, and limit visual categorization overall.

Entire books are dedicated to the topic of translating numbers to charts and graphs. Data scientists are constantly seeking to improve predictive models to run effectively and efficiently in consideration of potentially fickle audiences.

Why, what, and who are important. The best solutions also consider when.

Timing of information is critical to business and social connection. At first glance, early knowledge may seem best, and in many cases this is true.

However, consequences in human timing is social and mercurial, and highly sensitive to the culture, and immediate pressures and whims of the audience at hand.

Technology returns healthcare back to the general public

This article by Dr. Joon Yun, advocates and expresses the inevitability of Healthcare information, empowered the internet offering unparalleled access to current medicinal knowledge, along with rating services of providers, and crowdfunding investment opportunities, brings medicine back to the interests of consumers.

While the internet cannot be relied on to tell people everything, the base increase to general awareness should not be underestimated.

Data Science Gone Wrong

Bad Maps

Cartastophe is a website dedicated to studying poorly created maps provides a wide berth of map imagery and in depth commentary that range from criticizing bad map design principles.

Mistakes made by the infamous quants

It turns out common mistakes made by financial quants mirror common mistakes made in data science in general. This article references tendencies towards overfitting, training the model on training data versus test data, and consequences of overly complex models.

Dangers of cumulative consequences

Because data science is perceived as a nascent profession, it is currently funded and supported as a viable new mode of problem solving. However, with an abundance of mistakes and limited awareness, there is a risk that the profession, with many of the pitfalls and limitations similar to the field of statistics, will become once again marginalized and misunderstood.

Pitfalls of Geographical Data

Eyes can deceive, and even well intended reports can make mistakes.

Always Investigate beyond face value. When examining data sets, especially be wary when perceived correlations comes eerily close to representing population data.