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August 2014 Blog Posts (43)

The Shifting CXO crowns and the changing Analytics leadership requirements

"You see things and you say “why”.

But I dream things that never were; and I say, “why not”?"

George Bernard Shaw

A decade back, when ‘analytics’ was more an esoteric buzzword than an organizational necessity, leaders of fledging analytics divisions focused one key goal - ‘growth’

Today, with increasing acceptance of analytics across sectors and the explosive expected growth in Analytics software and…

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Added by Debleena Roy on August 31, 2014 at 5:30pm — No Comments

The Awkward Road

This blog is about the peculiar nature in which software sometimes gets developed. I hope that many readers will recognize the relevance of data science in the examples taken from my own projects. I propose that development is the product of creativity more than accreditation. Creativity is something complicated that interacts with a person over his or her life circumstances. Many people know how to write . . . sentences and paragraphs. However, the ability to write well does not necessarily…

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Added by Don Philip Faithful on August 30, 2014 at 8:59am — No Comments

Making D3.js Reusable With Templates

D3.js is an awesome library for visualization. However, it requires a developer to perform the magic. So far, it has not been so popular with traditional data analysts.

At vida.io, we set out to bring D3.js to less technically savvy users. We want to enable them to create amazing data visualizations with D3.js. Our approach is template.

What is a D3.js Template?

It’s a D3.js visualization that you can reuse by just plugging in new data. For example, one of our…

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Added by Phuoc Do on August 29, 2014 at 10:00am — 1 Comment

Frozen versus liquid analytics - or the competitive advantage of data science

In this article, I further emphasize the difference between data scientists and other analytic practitioners. I wrote last week that statisticians are to data scientists what astronomers are to physicists: there's some overlap, but less than most people think. Here, I elaborate on this theme.…

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Added by Vincent Granville on August 29, 2014 at 8:30am — No Comments

33 unusual problems that can be solved with data science

Here is a non-exhausting list of curious problems that could greatly benefit from data analysis. If you think you can't get a job as a data scientist (because you only apply to jobs at Facebook, LinkedIn, Twitter or Apple), here's a way to find or create new jobs, broaden your horizons, and make Earth a better world not just for human beings, but for all living creatures. Even beyond Earth indeed. Help us grow this list of 33 problems, to 100+.

The actual number is higher than 33, as…

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Added by Vincent Granville on August 28, 2014 at 9:00pm — 8 Comments

Big Data: What’s the Buzz Mean Beyond the Hype?

In this final blog post of three with Dr. Michael Cavaretta, we explore questions about big data, as well as some of his views on emerging technologies from start-ups. As Ford Motor Company’s top data scientist, Dr. Cavaretta has helped to…
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Added by Ryan Montano on August 28, 2014 at 9:30am — No Comments

My Top Websites For Big Data

My Top 6 Websites For Big Data (including Data Science Central of course!) are:

The Naked analyst Episode 8 looks at the best sites to learn about the world of Big Data.

Here are my top 6 sites. They range from the easily digestible to the totally…

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Added by Steve Bennett on August 27, 2014 at 8:27pm — 2 Comments

Weekly Digest - September 1

The full version is always published Monday. Starred articles or sections are new additions or updated content, posted between Thursday and Sunday. 

Sponsored Announcements…

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Added by Vincent Granville on August 27, 2014 at 2:30pm — No Comments

How to Become a Data Scientist

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Added by William Vorhies on August 27, 2014 at 7:30am — 25 Comments

5 levels of machine learning

There is a seismic shift underway in the engineering industries. The decreased cost of sensors, the increased amount of instrumentation on assets and need for new revenue streams are forcing engineering firms to reimagine business models.  The fusion of “atoms with bytes” promises to unlock new value previously unrecognized which generate additional revenue streams predicated on intelligence generated from the data. As machines…

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Added by derick.jose on August 26, 2014 at 7:30pm — No Comments

Synthetic criterion to choose the right variables for your predictive algorithm

The success of any big data or data science initiative is determined by the kind of data that you collect, and how you analyze it. In this article, we describe a simple criterion to select great metrics out of dozens, hundreds or even millions of potential predictors - sometimes called features or rules by machine learning professionals, or independent variables, by statisticians.…

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Added by Vincent Granville on August 26, 2014 at 3:30pm — No Comments

Crowdsourced Q&A with Peter Norvig on Data Science

*Note this was originally posted at Leada's Blog

When we first began working on Leada, we sought to better understand the data science industry by interviewing professionals in the field. As students simply wanting to learn more about data science, we ultimately created a free resource to inform both undergraduates and professionals about the data science industry. We accomplished this by…

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Added by Brian Liou on August 26, 2014 at 9:00am — No Comments

Should we all have two ID's (one for backup purposes)?

With high levels of ID theft occurring each day (if you believe the media, you'd think 50 million people have experienced critical ID theft in US alone over the last two years) and how hard it is to recover from this crime, one wonders: should we all have multiple ID's?

If your answer is yes, then the next big question is: how do you implement this? You'd think that if your primary ID has been compromised, chances are that your secondary ID has been compromised as well, unless these…

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Added by Vincent Granville on August 25, 2014 at 4:48pm — No Comments

Data Scientists by Education 2014

According to recent survey by Burtch…

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Added by Michael Walker on August 25, 2014 at 12:10pm — No Comments

Protecting Data with Data: Predicting Cyber Threats Before they Happen

Businesses have always faced security threats, whether is someone breaking in to steal some cash or equipment, a disgruntled employee selling company secrets, or something else altogether, there has always been cause to be careful as a business owner.

 

However, with the evolution of technology, and the digital age coming into full swing, security threats have evolved as well, and…

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Added by Beau Winchester on August 25, 2014 at 11:51am — No Comments

Big Data Divide: The challenge of a modern-day data & analytics platform when you’re not Google (Part 1 of 2)

Editor's Note: The corrected, updated and expanded version of this article is found here:

http://www.bit.ly/didata002

The core…

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Added by Noel Milton Vega on August 25, 2014 at 9:18am — No Comments

Crazy Data Science Tutorial: Classification and Clustering

In order to write a tutorial about classification, it was necessary to find an example that was broad enough that it would need to be sub-divided. Since I actually care about whether you remember this stuff, it needed to be something that a lot of people like and would relate to. And since I have a lot of international subscribers, it needed to be cross-cultural as well. So what is universal, cross-cultural, and dearly loved?

Beer.

Beer. Heck yeah.

There’s American beer,…

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Added by Randal Scott King on August 25, 2014 at 8:01am — 3 Comments

The most expensive data science textbook

This rudimentary statistics textbook, entitled Statistics: The Art and Science of Learning from Data (3rd Edition), sells on Amazon for $157.79. Not sure if everyone sees the same price as me (maybe prices are user-customized), if price changes over time, but it seems stable. Below is a screenshot.…

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Added by Vincent Granville on August 21, 2014 at 7:30pm — 7 Comments

Why Zipf's law explains so many big data and physics phenomenons

The Zipf's law states that in many settings (that we are going to explore), the volume or size of entities is inversely proportional to a power s (s > 0) of their ranking. This has important implications in predictive modeling, discussed below. The processes that create this type of dynamic are not well understood. It is the purpose of this article to explain the underlying mechanics. The traditional example for the Zipf distribution is the distribution of Internet…

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Added by Vincent Granville on August 21, 2014 at 3:00pm — 12 Comments

Data Science Helps Businesses to Become Data-driven

Data Science



Dr. Michael Cavaretta, leader of the Predictive Analytics Group in Ford’s Research and Advanced Engineering group, continues his conversation this week with Anametrix CEO …
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Added by Ryan Montano on August 21, 2014 at 9:30am — No Comments

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