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Employee Analysis: How Growth in Employee Strength Relates to Profits

December 11, 2014 by Nilesh Jethwa

Employee analysis.

In this article, we will perform some analysis on the employee related metrics from the top tech companies. All of the top tech companies are selected based on their current market capitalization with the exception of Yahoo. The year 2014 is not included as part of this analysis.[read more]

Who Gets the Call When Your Analytics Process Crashes?

October 10, 2014 by Bill Franks

Analytics matters.

I recently had a meeting with one of the largest companies in the world, where we discussed concerns about ongoing maintenance and, more importantly, ongoing repair required for analytics processes. The conversation helped solidify in my mind a major disconnect that often occurs when organizations deploy an analytics process into a production setting. Let’s walk through that disconnect here.[read more]

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Can Fossil Analysis Software Help Us Plan Curriculum?

August 20, 2014 by J. Kevin Byrne

Fossil analysis software.

Paleontologists use (free) software (known as PAST) for their statistical analysis. I bumped into it via key-word web-search to support academic planning I was doing. I offloaded it, took a close look, and I discovered I could use a specific menu within it (hierarchical analysis) to help reconceptualize curriculum at my design college.[read more]

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A Quick Guide to Structured and Unstructured Data

June 28, 2014 by Michele Nemschoff
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Guide to structured and unstructured data.

Big data has opened doors never before considered by many businesses. The idea of utilizing unstructured data for analysis has in the past been far too expensive for most companies to consider. Thanks to technologies such as Hadoop, unstructured data analysis is becoming more common in the business world.[read more]

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When Ideology Reigns Over Data

June 2, 2014 by Paul Barsch

Risky Business column.

Increasingly, the mantra of “let the data speak for themselves” is falling by the wayside. There are dangers to reputations, companies and global economies when researchers and/or statisticians either see what they want to see—despite the data, or worse, gently massage data to get “the right results.”[read more]

Job Market Explodes for Quantitative Students

May 22, 2014 by Linda Burtch

Jennifer Priestley, Professor of Statistics and Data Science at Kennesaw State University.

With the market for quantitative candidates continuing to gain momentum, Burtch Works spoke with Jennifer Priestley, Professor of Statistics and Data Science at Kennesaw State University - and friend of Burtch Works - about the job market for statistics majors, the beginnings of her MS in Applied Statistics program, and her thoughts on SAS vs. R.[read more]

SAS vs. R: The Deeper Dive

May 7, 2014 by Linda Burtch

SAS vs. R.

Last month, I conducted a quick “flash survey” of my network and asked: Which do you prefer to use, R or SAS? I posted the initial results on my blog a few weeks ago, and as promised during the webinar for our Data Scientist Salary Study, we’ve finished up our deeper dive analysis of the data from over 1,000 respondents.[read more]

Mobile Advertising, Clustering Algorithms, and Your Ticket for a Free Ride

May 4, 2014 by Lillian Pierson

Mobile advertising and clustering algorithms.

Because of some pretty bad-ass data science and Google’s ever-increasing awesomeness, it looks like one day in the not too distant future, we will all be able to get a free (or heavily-discounted) ride. A taxi ride, that is.[read more]

The Great Debate: SAS vs. R

April 14, 2014 by Linda Burtch

SAS vs. R.

Despite hearing more about R from clients and candidates than ever before, determining whether R was actually more popular than SAS proved difficult. A quick Google search for “R vs. SAS” returns more than a few pages dedicated to each side, as well as several heated LinkedIn discussions relating to the topic, with no definitive answers.[read more]

Big Data from Small Devices?

January 18, 2014 by Bruno Aziza
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Wearable tech.

When it comes to big data, the industry has shown no shortage of predictions for 2014. In fact, you might have read about insights on women in data science, ambitions for Machine Learning or a vision for the consumerization of Advanced Analytics.[read more]

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Time is Money: Milliseconds Matter [INFOGRAPHIC]

December 21, 2013 by Michele Nemschoff

Time is money.

Did you know just a one second increase in Amazon's page load time could potientially cost the retail giant $1.6 billion in annual sales? There's no question consumer online shopping expectations are at an all-time high. But did you know the time they spend on your site is at an all-time low?[read more]

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Be Wary of the Science of Hiring

December 10, 2013 by Paul Barsch
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Risky Business column.

While reliance on experience/intuition to hire “the right person” is rife with biases, there’s also danger in over-reliance on HR analytics to find and cultivate the ultimate workforce. While HR analytics seems to have room to run, there’s still the outstanding question of whether “the numbers” matter at all in hiring the right person.[read more]

Statisticians Push Back Against the "End of Theory" Problem

August 28, 2013 by Travis Korte

For years, some commentators have worried that increasing volumes of data coupled with better and better automated prediction methods would lead to an “end of theory.” Dr. Ryan Tibshirani, an Assistant Professor of statistics at Carnegie Mellon University, is trying to fix that.[read more]

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Big Data and Your Body

July 26, 2013 by Josh Polsky

(image: Norbert von der Groeben & Stanford University)

You can now get data output on your own output. This data-collecting toilet has been around for a few years, but with the emphasis on data collection of every kind becoming the norm, it only seems natural to revisit this invention, as well as similar technologies, and the benefits they present.[read more]

Statistics vs. Data Science vs. BI

May 17, 2013 by David Smith
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Statistician's profile

As someone who trained as a statistician, I've always struggled with that title. I love the rigor and insight that Statistics brings to data analysis, but let's face it: Statistics — the name — has always had a bit of a branding problem. That's why I'm a fan of the term "data scientist."[read more]