Compilation – NG Data -50 Business Intelligence Blogs You Should Be Reading

ngdata-logo2

 

We are included in the list of 50 of the most valuable BI blogs on the web 

Hi Philippe, 

I work with NGDATA, a customer experience management solutions company that enables organizations to maximize the value of their customer relationships through its breakthrough solution, Lily EnterpriseTM. I wanted to reach out and let you know that we’ve just released our list of “50 Business Intelligence Blogs You Should Be Reading,” and you’ve made the list.

Congratulations! You can see the full list here: http://www.ngdata.com/top-business-intelligence-blogs/

(Note: The list is in random order; the blogs are not necessarily ranked or rated in order of quality or importance. The aim is to recognize some of the best, most useful blogs for staying in the loop on all things Business Intelligence among the many BI and Analytics blogs on the web.)
        
It would be great if you’d share this news internally and/or with your audience on social media. You can find us on Twitter @ngdata_com.

Thanks so much for your time, and congratulations again!

Angela

Angela Stringfellow

NGDATA​

 

49. The Brussels Data Science Community
@DataScienceBe

 

Brussels-Data-Science-Commu

The fastest-growing community of data scientists in Europe, The Brussels Data Science Community is a European knowledge hub for all things Big Data and data science. The community organizes events, shares knowledge, and conducts training to bridge the gap between academics and business through the value of analytics.

Three posts we like from The Brussels Data Science Community: 

 

Repost – Vincent Granville – 20 short tutorials all data scientists should read (and practice)

The new, completed version of this Data Science Cheat Sheet can be found here.

We are now at 20, up from 17. I hope I find the time to write a one-page survival guide for UNIX, Python and Perl. Here’s one for R. The links to core data science concepts are below – I need to add links to web crawling, attribution modeling and API design. Relevancy engines are discussed in some of the tutorials listed below. And that will complete my 10-page cheat sheet for data science.

Here’s the list:

  1. Tutorial: How to detect spurious correlations, and how to find the …
  2. Practical illustration of Map-Reduce (Hadoop-style), on real data
  3. Jackknife logistic and linear regression for clustering and predict…
  4. From the trenches: 360-degrees data science
  5. A synthetic variance designed for Hadoop and big data
  6. Fast Combinatorial Feature Selection with New Definition of Predict…
  7. A little known component that should be part of most data science a…
  8. 11 Features any database, SQL or NoSQL, should have
  9. Clustering idea for very large datasets
  10. Hidden decision trees revisited
  11. Correlation and R-Squared for Big Data
  12. Marrying computer science, statistics and domain expertize
  13. New pattern to predict stock prices, multiplies return by factor 5
  14. What Map Reduce can’t do
  15. Excel for Big Data
  16. Fast clustering algorithms for massive datasets
  17. Source code for our Big Data keyword correlation API
  18. The curse of big data
  19. How to detect a pattern? Problem and solution
  20. Interesting Data Science Application: Steganography

Other Cheat Sheets

Vincent’s Cheat Sheets for Perl, R, Excel (includes Linest, Vlookup), Linux, cron jobs, gzip, ftp, putty, regular expressions, Cygwin, pipe operators, files management, dashboard design etc. coming soon

Cheat Sheets for Python

Cheat Sheets for R

Cross Reference between R, Python (and Matlab)

Cheat Sheets for SQL

Additional

Related linkThe Data Science Toolkit

Other interesting links

20 short tutorials all data scientists should read (and practice) by Vincent Granville

20 short tutorials all data scientists should read (and practice)

We are now at 20, up from 17. I hope I find the time to write a one-page survival guide for UNIX, Python and Perl. Here’s one for R. The links to core data science concepts are below – I need to add links to web crawling, attribution modeling and API design. Relevancy engines are discussed in some of the tutorials listed below. And that will complete my 10-page cheat sheet for data science. 

Here’s the list:

  1. Tutorial: How to detect spurious correlations, and how to find the …
  2. Practical illustration of Map-Reduce (Hadoop-style), on real data
  3. Jackknife logistic and linear regression for clustering and predict…
  4. From the trenches: 360-degrees data science
  5. A synthetic variance designed for Hadoop and big data
  6. Fast Combinatorial Feature Selection with New Definition of Predict…
  7. A little known component that should be part of most data science a…
  8. 11 Features any database, SQL or NoSQL, should have
  9. Clustering idea for very large datasets
  10. Hidden decision trees revisited
  11. Correlation and R-Squared for Big Data
  12. Marrying computer science, statistics and domain expertize
  13. New pattern to predict stock prices, multiplies return by factor 5
  14. What Map Reduce can’t do
  15. Excel for Big Data
  16. Fast clustering algorithms for massive datasets
  17. Source code for our Big Data keyword correlation API
  18. The curse of big data
  19. How to detect a pattern? Problem and solution
  20. Interesting Data Science Application: Steganography

Other Cheat Sheets

Vincent’s Cheat Sheets for Perl, R, Excel (includes Linest, Vlookup), Linux, cron jobs, gzip, ftp, putty, regular expressions, Cygwin, pipe operators, files management etc. coming soon

Cheat Sheets for Python 

Cheat Sheets for R 

Cross Reference between R, Python (and Matlab) 

Cheat Sheets for SQL 

Additional 

Related linkThe Data Science Toolkit

Other interesting links

25 Data Scientists Popular on LinkedIn by Vicent Granville

I received this morning, like everyone, an email from LinkedIn about 59 of your LinkedIn connections that are more popular than you. In short, it represents about 0.5% of my 10,000+ connections, and I decided to share with you those people listed at the top – the top 100, not just the top 59. This is based on profile views over the last 7 days, so it will change week after week. Also, I removed recruiters, generic VC or angel investors, and non data scientists in general (based on skill mix): it narrowed down to 25 profiles, listed below.

Interestingly, very few are females (I think only one, Monica), and of course quite a few are employed by linkedIn. Some famous people like Gregory from KDNuggets, Nate Silver or Prof. Davenport are missing. But at least, it shows a very different picture, compared with the traditional “top data scientists” lists published by journalists, and based on some mysterious mix of questionable Twitter metrics, and unfiltered data. In short, the list below provides a fresh perspective about top data science thought leaders and practitioners.

  1. Bernard Marr – Best-Selling Author, Keynote Speaker and Consultant …
  2. DJ Patil – VP of Product at RelateIQ
  3. Josh Bersin – Principal and Founder, Bersin by Deloitte
  4. Chuck Brooks – Vice President, Client Executive for the Department …
  5. Avinash Kaushik – Author, Blogger, Digital Marketing Evangelist
  6. Roman Stanek – Founder and CEO at GoodData
  7. Daniel Tunkelang – Head of Query Understanding at LinkedIn
  8. Joseph Sirosh – Corporate Vice President at Microsoft
  9. Deepak Agarwal – Director of Engineering at LinkedIn
  10. David Barton – Analytics Divisional Head at Innovation Enterprise
  11. Dez Blanchfield – Strategy & Architecture, Australian Federal G…
  12. John Ricci – Founder at US Angel Investors
  13. Pardeep Kumar Mishra – Big Data / Hadoop Consultant
  14. Vince Zhu – Graduate Student at NYU Courant Institute of Mathematic…
  15. Muddu Sudhakar – VP & GM Big Data and Cloud Analytics at VMware
  16. Craig Kuo-Jen Chao, PhD – BD & Data Scientist at Vpon Inc. 
  17. Vincent Granville – Data Scientist, Startup Entrepreneur
  18. Dan Steinberg – President at Salford Systems
  19. Simon Zhang – Business Analytics Sr. Director at LinkedIn
  20. Monica Rogati – VP of Data at Jawbone
  21. Srikanth Velamakanni – Chief Executive Officer, Fractal Analytics
  22. Shize Su – PhD Candidate at University of Virginia, Electrical and …
  23. Wolfgang Kraske – Senior Principal Consultant: Big Data Integration…
  24. Venkat Viswanathan – Chairman at LatentView
  25. Boumediene Hamzi – Mathematician, Researcher in Control & Dynam…