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Vincent Granville
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  • Issaquah, WA
  • United States
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Vincent Granville's Discussions

Generating Random Points on a Sphere

Started this discussion. Last reply by mungunbat enkhbayar Apr 5. 3 Replies

While this looks like a basic problem, it is actually somewhat more complicated than expected. One might think of generating uniform deviates for the longitude, as well as uniform deviates for the…Continue

Defeating Email Monitoring Algorithms

Started this discussion. Last reply by Vincent Granville Jan 10. 1 Reply

People complain that governments or hackers are reading our messages for nefarious purposes. Of course this "reading" is done automatically, in large volume, by machines and NLP (natural language…Continue

Detecting Plagiarism with a Simple Trick

Started this discussion. Last reply by Jim Roberts Jan 17. 6 Replies

Many unscrupulous bloggers re-post copyrighted material on their blogs, without permission. The problem is compounded by the fact that Google can give credit to the illegal version, and erroneously…Continue

Challenge: Representation of Numbers as Infinite Products

Started Jan 4 0 Replies

This is our new challenge of the week. Previous challenges…Continue

Gifts Received (3)

 

Vincent Granville's Page

Profile Information

Short Bio
Well rounded, visionary data science executive with broad spectrum of domain expertise, technical knowledge, and proven success in bringing measurable added value to companies ranging from startups to fortune 100, across multiple industries (finance, Internet, media, IT, security), domains (data science, operations research, machine learning, computer science, business intelligence, statistics, applied mathematics, growth hacking, IoT) and roles (data scientist, founder, CFO, CEO, HR, product development, marketing, media buyer, operations, management consulting).

Vincent developed and deployed new techniques such as hidden decision trees (for scoring and fraud detection), automated tagging, indexing and clustering of large document repositories, black-box, scalable, simple, noise-resistant regression known as the Jackknife Regression (fit for black-box, real-time or automated data processing), model-free confidence intervals, bucketisation, combinatorial feature selection algorithms, detecting causation not correlations, automated exploratory data analysis with data dictionaries, data videos as a visualization tool, automated data science, and generally speaking, the invention of a set of consistent robust statistical / machine learning techniques that can be understood, implemented, interpreted, leveraged and fine-tuned by the non-expert. Vincent also invented many synthetic metrics (for instance, predictive power and L1 goodness-of-fit) that work better than old-fashioned stats, especially on badly-behaved sparse big data. Some of these techniques have been implemented in a Map-Reduce Hadoop-like environment. Some are concerned with identifying true signal in an ocean of noisy data.

Vincent is a former post-doctorate of Cambridge University and the National Institute of Statistical Sciences. He was among the finalists at the Wharton School Business Plan Competition and at the Belgian Mathematical Olympiads. Vincent has published 40 papers in statistical journals (including Journal of Number Theory, IEEE Pattern analysis and Machine Intelligence, Journal of the Royal Statistical Society, Series B), a Wiley book on data science, and is an invited speaker at international conferences. He also holds a few patents on scoring technology, and raised $6 MM in VC funding for his first startup. Vincent also created the first IoT platform to automate growth and content generation for digital publishers, using a system of API's for machine-to-machine communications, involving Hootsuite, Twitter, and Google Analytics.

Vincent's profile is accessible here and includes top publications, presentations, and work experience with Visa, Microsoft, eBay, NBC, Wells Fargo, and other organisations.

Follow me on Twitter at @AnalyticBridge.
My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/vincentg
Field of Expertise
Analytics, Big Data, Data Science
Professional Status
C-Level
Years of Experience:
15
Your Company:
Data Science Central, AnalyticBridge
Industry:
Internet
Your Job Title:
Executive Data Scientist, Co-Founder
How did you find out about DataScienceCentral?
Tim Matteson
Interests:
Networking, New venture, Recruiting, Other
What is your Favorite Data Mining or Analytical Website?
http://www.datasciencecentral.com
What Other Analytical Website do you Recommend?
http://www.analyticbridge.com

Bio

Well rounded, visionary data scientist with broad spectrum of domain expertise, technical knowledge, and proven success in bringing measurable added value to companies ranging from startups to fortune 100, across multiple industries (finance, Internet, media, IT, security) and domains (data science, operations research, machine learning, computer science, business intelligence, statistics, applied mathematics, growth hacking, IoT).

Vincent developed and deployed new techniques such as hidden decision trees (for scoring and fraud detection), automated tagging, indexing and clustering of large document repositories, black-box, scalable, simple, noise-resistant regression known as the Jackknife Regression (fit for black-box, real-time or automated data processing), model-free confidence intervals, bucketisation, combinatorial feature selection algorithms, detecting causation not correlations, and generally speaking, the invention of a set of consistent robust statistical / machine learning techniques that can be understood, implemented, interpreted, leveraged and fine-tuned by the non-expert. Vincent also invented many synthetic metrics (for instance, predictive power and L1 goodness-of-fit) that work better than old-fashioned stats, especially on badly-behaved sparse big data. Some of these techniques have been implemented in a Map-Reduce Hadoop-like environment. Some are concerned with identifying true signal in an ocean of noisy data.

Vincent is a former post-doctorate of Cambridge University and the National Institute of Statistical Sciences. He was among the finalists at the Wharton School Business Plan Competition and at the Belgian Mathematical Olympiads. Vincent has published 40 papers in statistical journals and is an invited speaker at international conferences. Vincent also created the first IoT platform to automate growth and content generation for digital publishers, using a system of API's for machine-to-machine communications, involving Hootsuite, Twitter, and Google Analytics.

Vincent's profile is accessible at http://bit.ly/1jWEfMP and includes top publications, presentations, and work experience with Visa, Microsoft, eBay, NBC, Wells Fargo, and other organisations.

Latest Activity

Nikola Mandic liked Vincent Granville's blog post PySpark Cheat Sheet: Spark in Python
3 hours ago
Ashis Samal liked Vincent Granville's blog post Must Know Tips/Tricks in Deep Neural Networks
10 hours ago
Afelio Padilla liked Vincent Granville's blog post Introduction to Principal Component Analysis
16 hours ago
Vincent Granville posted a blog post

Weekly Digest, May 1

Monday newsletter published by Data Science Central. Previous editions can be found here.  The contribution flagged with a + is our selection for the picture of the week.AnnouncementsData scientists attending the AMA Advanced Research Techniques Forum on June 25-28 in Seattle will find a network of colleagues serious about developing tools that can…See More
yesterday
Gene Smith liked Vincent Granville's blog post Which machine learning algorithm should I use?
Friday
Lance Chambers liked Vincent Granville's blog post Key Machine Learning PreReq: Viewing Linear Algebra through the right lenses
Friday
Francisco Alba Andres liked Vincent Granville's blog post Which machine learning algorithm should I use?
Friday
Eamon Corr liked Vincent Granville's blog post Which machine learning algorithm should I use?
Friday
Siddhi Patel commented on Vincent Granville's blog post Which machine learning algorithm should I use?
"Thanks... It is really very helpful..."
Friday
Harshendu Desai liked Vincent Granville's blog post Introduction to Principal Component Analysis
Thursday
Mia Lyst liked Vincent Granville's blog post Which machine learning algorithm should I use?
Thursday
William Vorhies liked Vincent Granville's blog post Key Machine Learning PreReq: Viewing Linear Algebra through the right lenses
Thursday
Vincent Granville posted blog posts
Thursday
Sheyda Saponar liked Vincent Granville's blog post Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics
Thursday
Sheyda Saponar liked Vincent Granville's blog post What is Data Science? 24 Fundamental Articles Answering This Question
Thursday
VAMSI NELLUTLA commented on Vincent Granville's blog post Book: Data Science for the Layman: No Math Added
"Love this book! Amazingly written and very easy to digest even the complex and confusing algorithms. Good job!!"
Thursday

Comment Wall (13 comments)

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At 1:37pm on June 23, 2016, Bill Bahl said…

Dr. Granville,

I enjoyed your white paper on Building Dashboards that Flow and could not agree more with minimalism. One thing that seems to be missing from the dashboard packages I've seen is control charts.  At least for the process owner, my personal opinion is a control chart should be the first chart.  If the process is not stable and predicable, statistical analysis seems futile.  Before I retired (two months ago) we started including these in the process owners' LEAN PIT boards.  We generated them in Minitab.  It only takes a few clicks once the data is paste into Minitab.  Bill Bahl

At 12:05pm on February 11, 2016, Dean Pangelinan said…

Dr. Granville,

Regarding the passerelle options for the Data Science certification program, does the notation of "IEEE Computer Science Society - Member" refer to Associate Membership in the IEEE Computer Society, or to full IEEE Membership with additional membership in the IEEE Computer Science Society?

Please advise, at your earliest convenience.

--  Dean Pangelinan

At 5:05pm on June 21, 2015, Sankara Kumaravel gave Vincent Granville a gift
Gift
Dear Dr.Vincent, Thanks for preserving such a nice professional web page for Data Analytics, this is really help for the novice like me.
At 5:28am on June 15, 2015, Lissy Able said…

Hi Vincent,

Can you suggest some points or links about serious data quality issue with the information pulled.

Thanks

Lissy

At 3:38pm on March 11, 2015, Donald Tynes said…

Vincent,

I recently was hired as a data scientist. As a new hire, leading the department of Business Intelligence, I am faced with self-posed questions such as, "What do I need to accomplish in the first 5 days?" And, "What should I accomplish in the first month?" And, of course, "How do I develop a long-term plan for transforming the business into a data-driven organization?" To make the problem of determining how I should focus my attention even more complicated, I have a single employee whom I want to groom to understand the algorithms that I am implementing. Also, I have a CEO who only agreed to hire for this position because the CIO, CFO, and COO encouraged him to do so, but he is highly skeptical of what data science can do for the organization; this complicates matters too because it puts on me a pressure to be dazzling right out-of-the-box. 

I have given these questions considerable thought. I am on day 3 of my new job. I have decided to orient myself on the business' data, query tools, and self-service tools, such as QlikView. I have so many ideas, I have difficulty in choosing a single direction in which I should run. I must note that I want to be significantly impactful while minimizing disruptions in the business' daily functions. To that end, I keep thinking, "run a clustering analysis! Discover the patterns and trends in the company's data to begin the model-building process."

What advice would you give a young data scientist on his 4th day on the job (as it is for me, tomorrow)? 

At 5:22am on December 3, 2014, Harvey Summers said…

I thought you might like this site: http://rpsychologist.com/d3/CI/ 

Interpreting Confidence Intervals

an interactive visualization

At 11:29pm on October 31, 2014, Philippe Van Impe said…

Being from Belgium, you are welcome to join our meetup group about data sciences http://www.meetup.com/Brussels-Data-Science-Community-Meetup/

At 12:21pm on September 25, 2014, Christian Block said…

Hello Vincent, 

I just found DataScienceCentral and wanted to say thank you for putting it together! I'm looking forward to reading through more of the content and checking out your book (which I have ordered).  

Best Regards,

Christian Block

At 6:19am on August 5, 2014, Nasir M. Uddin, Ph.D. said…

Hi Vincent:

This is a great platform to be informed with any latest updates in the field of data science - thank you so much for such a platform.

Regards, Nasir 

At 8:03am on January 8, 2013, Marc Jape said…
Vincent:

This is a great platform that I was not aware of. Keep up the good work.

Regards,
Marc

Vincent Granville's Videos

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Vincent Granville's Blog

Weekly Digest, May 1

Posted on April 29, 2017 at 8:30am 0 Comments

Monday newsletter published by Data Science Central. Previous editions can be found here.  The contribution flagged with a + is our selection for the picture of the week.

Announcements

Continue

18 Great Blogs Posted in the last 12 Months

Posted on April 27, 2017 at 8:07am 0 Comments

This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at least 6 month old but no more than 12 month old. The previous digest in this series was posted here a while back. 

18 Great Blogs Posted in the last 12 Months…

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Thursday News: AI, Python, Hadoop, ML, Data Science, Automation

Posted on April 27, 2017 at 5:36am 0 Comments

Here is our selection of featured articles and resources posted since Monday:

Continue

Which machine learning algorithm should I use?

Posted on April 26, 2017 at 6:30pm 1 Comment

By Hui Li, Principal Staff Scientist, Data Science, at SAS.

A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is “which algorithm should I use?” The answer to the question varies depending on many factors, including:

  • The size, quality, and nature of data.
  • The available computational time.
  • The urgency of the task.
  • What you want to do with the data.

Even an experienced data…

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