Is Data Science Really Science?

Hey there awesome people, after attending 2 unforgettable days of Web Summit Lisbon 2016 and a few months of inactivity due to educational/personal reasons (such as getting my masters degree on track) I’m back to WordPress again, this time with full strength, jabbing and boxing for a right hook knockout everyday as Gary Vaynerchuk writes in his 3rd book and his daily vlog.

Today I’m sharing a very interesting article I read on LinkedIn this morning entitled “Is Data Science Really Science?” wrote by Bill Schmarzo.

The article called my attention right away, not only because I’m an aspiring Data scientist, but also because data (big and small), information and knowledge mining/management is the “new oil” – maybe even the oxygen – to the second half of 21st century successful organizations (startups, tech related or not).

Stay awesome.
Best regards, Pedro Calado

Data Scientist Joined the White House to Wrangle Data Issues

Gigaom

DJ Patil, who has held of a number of jobs as a data scientist, most notably at LinkedIn, has joined the White House as a data scientist-in-residence with a focus on helping handle health care data, according to John Podesta, Counselor to the President, on a conference call held Thursday. Podesta said Patil had joined the White House this week during a call related to a White house briefing on how the government should handle data and consumer privacy.

After leaving LinkedIn, Patil took on a few jobs, including as data scientist in residence at Greylock, a venture capital firm. His most recent position — and current one, according to his LinkedIn profile — was as vice president of product at RelateIQ, a customer relationship management startup that was acquired by Salesforce.com in July.

Patil is one of the biggest figures in the data science movement, often crediting for coining…

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Brief Introduction: Dive Into the World of Data

Data Science- All you need to know.

What is Data science?

Data science is magic.

Magic, not just because it can grow exponentially and unpredictably, neither because of the vastness in context of what each data holds within, but also because it has capability to re-define the entire process of how the perfectly armored and sophisticated model or a product (in theory) can be actually nothing but perfectly imperfect. What is interesting is, even when we do not know “why” the product’s marketability is low, we are ready to make decision with “what” is the reason for product’s marketability to be low. The data is everything. Once you understand the concept why it is important, you begin to know that everything could be possibly made perfect (as close to it as possible).

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