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August 2015 Blog Posts (95)

Top 5 Trends in Big Data Analytics

While many of us recognize that companies are empowered by actionable information penetrations and help drive sales, devotion and superior customer experiences, the thought of making sense of enormous quantities of information and undertaking the task of unifying is daunting. But that is slowly changing. Experts forecast that this year, budgets will be allocated by most companies, and that 2015 will undoubtedly be the year of big data and discover the best tools and resources to really…

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Added by Ayush Sharma on August 5, 2015 at 11:51am — No Comments

How Many Experts Does It Take

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Added by William Vorhies on August 5, 2015 at 11:00am — No Comments

Meta Collection of Data Science and Big Data Analytics Best Practices, Lessons Learned, and Success Stories

The following list of collections of collections of Data Science and Big Data Analytics Best Practices, Lessons Learned, and Success Stories is an updated version of a previous list posted here.

The flood of articles, webinars, and conferences related to Big Data is generating its own "infoglut". Consequently, it is really helpful when you find resources that…

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Added by Kirk Borne on August 5, 2015 at 10:57am — No Comments

Defining Your Data Quality Problems



To tackle any problem in a systematic and effective way, you must be able to break it down into parts. After all, understanding the problem is the first step to finding the solution.  From there, you can develop a strategic battle plan. With data quality, the same applies: every initiative features many stages and many different angles of attack.

When starting a…

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Added by Martin Doyle on August 5, 2015 at 5:06am — No Comments

3 Companies & Case Studies of AI In Investing

Artificial Intelligence in investing has long been practiced by secretive hedge funds like Renaissance Technologies. However, some of the same capabilities are being offered by companies to other players. Here are three examples of companies on how they are using data sets to predict stock & other financial market outcomes.…

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Added by Pansop on August 5, 2015 at 3:00am — No Comments

Why I don't like IT ppl in my Analytics team

In the beginning, there was Statistics, and for a time, it was good: it allowed Fisher to measure farm productivity. Then came Computer Science, and for a time, it was good: combined with Stats, it allowed the allies to save lives and fuel in WW2. Then came the IT barbarians, with their tool-centric religions, fads and next-coolest-framework cult. Analytics went downhill from there.

...

Sorry for the inflammatory intro, but despite the hyperbole, the fact remains: IT has not helped… Continue

Added by Jesus Ramos on August 4, 2015 at 10:44pm — 3 Comments

20 Big Data Repositories You Should Check Out

This is an interesting listing created by Bernard Marr. I would add the following great sources:

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Added by Mirko Krivanek on August 4, 2015 at 2:30pm — 1 Comment

15 Questions All R Users Have About Plots

Posted by DataCamp July 30th, 2015.

See the full blog here

R allows you to create different plot types, ranging from the basic graph types like density plots, dot…

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Added by William Vorhies on August 4, 2015 at 9:39am — No Comments

Top "Big Data" accounts on Twitter

Which accounts should I follow on Twitter to stay up to date with Big Data?

This is a common question for those who want to learn about Big Data or know more about the field.

Well, it just so happened that I wanted to practise my skills with Python and with Twitter API, I have thus created a way to find and rank the most successful accounts talking about Big Data on Twitter.

Here is the…

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Added by Luca Naso on August 3, 2015 at 9:04am — 6 Comments

Information Fusion and Data Integration: Fast vs. Batch

guest blog by Jin Kim, VP Product Development for…

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Added by William Vorhies on August 3, 2015 at 8:30am — No Comments

Aster and Text Analysis (TextChunker, Vector Distance, Levenshtein Distance, Text_Parser, and TF_IDF)

Some of you may not know that Aster provides deep capabilities in text analysis.  These functions are easy to use.  They also allow you to perform text analysis at scale.  What does this mean?  This means that I am able to take billions of customer service notes from a CRM system and perform text analysis.  Here is a sample of some of the Aster Text Analytic Functions:  (TextChunker, Vector Distance, Levenshtein Distance, Text_Parser, and TF_IDF)

TextChunker:…

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Added by John Thuma on August 3, 2015 at 7:30am — No Comments

The Return on Analytics: What is the Show-Stopper?

A recent Wikibon report found that enterprises are struggling to derive maximum value from Big Data. While they expect a return of around $3.50 on the dollar, their return to-date is just $0.55. So what stops the business from deriving value from analytics? Based on my extensive…

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Added by Bhavani Raskutti on August 2, 2015 at 4:57pm — No Comments

Ingredients Of Data Science

Guest blog post.

After reading many blog posts, articles and books, I have collected ingredients of data science! Moreover, I've classified them with a purpose of easily making a cook named as data science with below lists for whom wants to construct own career road map! Maybe, you wonder "why I am not giving the recipes of it" is because I do not have any real life experience.        …

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Added by Mirko Krivanek on August 2, 2015 at 8:30am — No Comments

11 most popular data science presentations on Slideshare

These presentations have been viewed between more than 25,000 times on average, though old articles have obviously more pageviews than new ones (assuming the popularity is identical), and some articles get more than 50% of their traffic more than 3 months after being published. Indeed, it's a very interesting statistical problem to adjust for this natural time bias.…

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Added by Mirko Krivanek on August 2, 2015 at 8:30am — 2 Comments

Eleven interesting questions about data science / big data

What are your thoughts on this? What would be your answers?  

Here's my list of questions:

  1. What best practices do you recommend, when starting and working on enterprise analytics projects?
  2. How do you see data science and exploitation of big data evolve, over the next 5-10 years?
  3. What are the bottlenecks and other issues that…
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Added by Mirko Krivanek on August 2, 2015 at 7:30am — No Comments

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