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Actionable Insights from Competitive Research

Keeping your eye on your competitors is a vital strategy for helping your business grow. By watching what they're doing and looking at their successes and failures, you'll be able to keep a leg up and a competitive edge. That being said, we're going to look a little more in-depth into why you need to be incorporating competitive research into your SEO and digital marketing strategy, some metrics you should be looking at, and actionable results that you can look at to know that…

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Added by Robert Cordray on December 22, 2014 at 11:30am — No Comments

What can be predicted, and what can't?

Given the right data being correctly collected, and analyzed using sound predictive models, what can be predicted, and what can't be predicted no matter what?

I believe that I have an answer to this question. All systems and processes that rely on some energy source can be predicted, and the other way…

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Added by Vincent Granville on December 21, 2014 at 8:00pm — 1 Comment

Fallacy of Rational Prerequisite & My Fruitless Existence

Before elaborating on my fruitless existence - about my decision to avoid fruit - I want to emphasize how this blog is actually about something that I call the "Fallacy of Rational Prerequisite." There will be some misunderstanding about this term even after my prolonged explanation. I just want to state plainly at the outset that I am not proposing that people become irrational. If they are already so, I am not suggesting that they further the situation.…

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Added by Don Philip Faithful on December 20, 2014 at 8:21am — No Comments

Why Media Bias Has Nowhere to Run and Hide from Data Science

When you want to see the face of biased reporting in online news, you may not have to go further than, the satirical news site, The Onion. Titles such as “Media Reports of Bear Attacks May Be Biased”, “Weather Channel Accused of Pro-Weather Bias”, and “Media Criticized for Hometown Sports Reporting” can make us laugh, but they can…

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Added by Renette Youssef on December 19, 2014 at 10:46am — No Comments

The data science project lifecycle

How does the typical data science project life-cycle look like?

This post looks at practical aspects of implementing data science projects. It also assumes a certain level of maturity in big data (more on big data maturity models in the next post) and data science management within the organization. Therefore the life cycle presented here differs, sometimes significantly from purist definitions of 'science' which emphasize the…

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Added by Maloy Manna on December 18, 2014 at 2:39pm — No Comments

Key Takeaways: Pivotal’s Top 10 2015 Predictions

On Tuesday 12/16, I attended Pivotal’s Top 10 Data Science Predictions in 2015 webinar.

The webcast was ran by leaders from the Pivotal Data Science  team – Annika Jimenez, Kaushik Das and Hulya Farinas – who shared their insights on the key Data Science industry trends for the coming year. The webcast came off as a bit scripted, but one could tell that these three individuals have a passion for Data Science discipline and it’s future.

In this post, I’d like to take a…

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Added by Anthony Dutra on December 18, 2014 at 6:56am — No Comments

Weekly Digest - December 22

The full version is always published Monday. Starred articles or sections are new additions or updated content, posted between Thursday and Sunday. Articles marked with a + have interesting visualizations.

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Added by Vincent Granville on December 17, 2014 at 7:30pm — No Comments

Infographic: Data Science 2015 -- What's Hot & What's Not

CrowdFlower is excited to release our first “What’s Hot & What’s Not in Data Science” infographic. According to our team of data scientists, the forecast for 2015 includes data’s major impact on the Internet of Things, changes in the skills and structure of the data scientist role and heavy emphasis on finding rich data within big data.…CrowdFlower_Graphic_Whats_Hot2015

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Added by Renette Youssef on December 17, 2014 at 8:23am — No Comments

Can Analysis be Open-Source?

Over the past year, as the head of analytics at a tech startup, I've had many conversations with analysts about what they want to learn from their data. Perhaps unsurprisingly, a lot of companies have similar questions—What drives retention? How do customers interact with products? How do we better understand sales pipelines? What's the lifetime value of a user?

These questions were familiar to us and we'd worked on many of them ourselves. To find answers in our own data, we wrote…

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Added by Benn Stancil on December 17, 2014 at 7:00am — No Comments

The Future of Big Data is Wearables

Guest blog past by Rohit Yadav, from BRIDGEi2i Analytics Solution

The Net (Part 1)

The plot goes something like this – Sandra Bullock plays a computer expert Angela Benett, her life changes when she is sent a program with a crazy glitch to ‘de-bug’. Soon she finds out some vital government information on the disk, things gets nutty as fruitcake, her life becomes a nightmare with her records getting erased and she is given a new identity of some chick with a…

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Added by Vincent Granville on December 16, 2014 at 7:30pm — 2 Comments

Rules for building a Data Product in IT organizations

In my consulting work in the Enterprise IT space, I am seeing a definite trend of growing interest in Data Product/Advanced Analytics Design and Development which is becoming increasingly mainstream. Even as I view this a positive, it comes with its own set of perils and pitfalls that will need to be avoided.  

Enterprise IT Application Development is often bureaucratic and involves multiple and redundant levels of management through the design, development and testing phases.…

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Added by Manoj Sharma on December 16, 2014 at 8:30am — No Comments

Data Visualization of Employee metrics at the top Tech companies

The top tech companies by market capitalization are IBM, HP , Oracle , Microsoft , Cisco , SAP , EMC , Apple , Amazon and Google

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.

 

Data: The source of this data is from the public financial records from SEC.gov

 

All the sales figures are normalized and reported in USD…

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Added by Nilesh Jethwa on December 15, 2014 at 11:01am — 4 Comments

Best solution to a problem: data science versus statistical paradigm

The definition of 'best' depends on which school you follow. Data science and classic statistical science are at the opposite ends of the spectrum. So let's clarify what 'best solution' means in these two opposite contexts:

'Best', according to statistical science:

  • It usually means the global maximum of a mathematical optimization problem
  • The objective function involved is usually a maximum likelihood function, KS, c-statistics, or some function…
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Added by Vincent Granville on December 14, 2014 at 8:30pm — 7 Comments

How-to use Bag of little bootstraps Methodology to Compute Error Bounds on Machine Learning Tasks

We all know that calculating error bounds on metrics derived from very large data sets has been problematic for a number of reasons. In more traditional statistics one can put a confidence interval or error bound on most metrics (e.g., mean), parameters (e.g., slope in a regression), or classifications (e.g., confusion matrix and the Kappa statistic).

For many machine learning applications, an error bound could be very important.…

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Added by Anna Anisin on December 14, 2014 at 3:33pm — No Comments

69 new external resources and articles about data science, big data - December 13

Starred articles were potential candidates for our picture of the week published in our weekly digest. Enjoy our new selection of articles and resources (R, data science, Python, machine learning etc.) Comments are from Vincent…

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Added by Amy on December 13, 2014 at 4:00pm — No Comments

Discover, Access, Distill: The Essence of Data Science

I coined this motto - the fact that data science is Discover, Access, Distill, back in October 2013. It looks like this is becoming a popular concept. It is the title of Fred Cadena's blog. It is also…

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Added by Amy on December 12, 2014 at 6:30pm — 1 Comment

Big Data: The Key Vocabulary Everyone Should Understand

Guest blog post by Bernard Marr, first published here.

The field of Big Data requires more clarity and I am a big fan of simple explanations. This is why I have attempted to provide simple explanations for some of the most important technologies and terms you will come across if you’re looking at getting into big…

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Added by Vincent Granville on December 12, 2014 at 12:00pm — 6 Comments

10 data science predictions for 2015

These predictions were published by the International Institute for Analytics (IIA). They produced a nice infographics, featured below, and re-tweeted many times by various bloggers, using the hash tag #2015Analytics. Other interesting predictions include…

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Added by Vincent Granville on December 12, 2014 at 10:30am — No Comments

Our iceberg is melting . Now where's that data scientist?

On the face of it, John Kotter’s seminal book “Our iceberg is melting” is a simple tale of a group of penguins who are scared about losing their home, their iceberg, and yes, even more scared of the changes that could entail. But through that simple story and their struggle for finding…

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Added by Debleena Roy on December 10, 2014 at 8:30am — No Comments

Weekly Digest - December 15

The full version is always published Monday. Starred articles or sections are new additions or updated content, posted between Thursday and Sunday. Articles marked with a + have interesting visualizations.

Featured

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Added by Vincent Granville on December 9, 2014 at 9:30pm — No Comments

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