Is Data Science Really Science?

data 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

Advertisements

Data Science Talent is Key to Analytical Innovators


Technopreneurph

Companies continually look for ways to outperform their competitors. One way they are trying to get ahead is through the application of analytics on their data. Researchers, for example, have found that top-performing businesses were twice as likely to use analytics to guide future strategies and guide day-to-day operations compared to their low-performing counterparts.

Researchers from MIT and SAS suggest that top performing companies use analytics differently than bottom performing companies. They found that Analytic Innovators (businesses where analytics created a competitive advantage and has helped innovation), more so than Analytically Challenged, use analytics primarily to increase value to the customer rather than to decrease costs/allocate resources, aggregate/integrate different business data silos to look for relationships among once-disparate metrics and gain executive support around the use of analytics to encourage sharing of best practices and data-driven insights throughout the company.

Analytics don’t occur in a vacuum. Companies need the right…

View original post 402 more words

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…

View original post 117 more words

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).


View original post 835 more words

Microsoft Buys Data Science Specialist Revolution Analytics


Gigaom

Microsoft has agreed to acquire Revolution Analytics, a company built around commercial software and support for the popular R statistical computing project. The open source R project is hugely popular among data scientists and research types, and having Revolution’s R experts in-house could be a big deal for Microsoft as it tries to establish itself as the go-to place for data science software.

Among Revolution’s additions to the standard R capabilities were simplifying the use of the program and engineering it to run across big data systems such as Hadoop. Here’s how Joseph Sirosh, Microsoft’s corporate vice president for machine learning, explains what the deal means in a blog post:

As their volumes of data continually grow, organizations of all kinds around the world – financial, manufacturing, health care, retail, research – need powerful analytical models to make data-driven decisions. This requires high performance computation that is “close”…

View original post 412 more words

Big Data and Statisticians, Revisited (Video)


What's The Big Data?

Data Science, Big Data and Statistics – can we all live together? from Chalmers Internal on Vimeo.

Terry Speed on how (and a bit on why) statisticians have been left out of the big data movement. Best slide comes at 34:20 and I wish Speed have talked more about how his “personal statistical paradigm” contrasts with the ideology of big data.

“This is the Golden Age of statistics–but not necessarily for statisticians”–Gerry Hahn

“Those who ignore statistics are condemned to re-invent it”–Brad Efron

HT: Nathan Yau

View original post