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

Our Solar System Does Not Have a Center

Started Jun 12 0 Replies

Just for the curious. This is not a data science problem.I was reading an article in National Geographic, entitled…Continue

Which classifier has the best performance?

Started this discussion. Last reply by Zhongmin Luo Jun 18. 8 Replies

This question was posted on one of our LinkedIn groups. The author wrote:In practice, given a wide range of classifiers, we often have to choose the one based on performance comparison through…Continue

Two very cool maps: how were their produced?

Started this discussion. Last reply by S Fraser May 12. 12 Replies

We received a request to identify the source for the two cool maps below. I did a Google Image Search and found the source for one of them, but for the other one the map seems to have disappeared…Continue

Coverage problem for cell phone towers

Started this discussion. Last reply by Thought May 21. 2 Replies

This is an interesting mathematical challenge to help solve a big business problem. Where to put the cell towers to achieve (say) a 95% coverage of a specific area. By coverage, I mean that 95% of…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

Pulkkinen Heikki commented on Vincent Granville's blog post Nice Generalization of the K-NN Clustering Algorithm -- Also Useful for Data Reduction
"Interesting approach! Have you compared the performance to K-NN yet?The brute force search over all data points is not the only way of making K-NN queries. A precomputed k-d tree (which can be compared to computing the cliques) can be used…"
59 minutes ago
Vikram Dutt liked Vincent Granville's blog post Free Deep Learning Book (MIT Press)
2 hours ago
AJAY PRASHANTH BARLA liked Vincent Granville's blog post Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics
6 hours ago
Animesh Shaw liked Vincent Granville's discussion Addendum to my data science book (updated)
10 hours ago
Animesh Shaw liked Vincent Granville's group Data Science Apprenticeship
10 hours ago
Animesh Shaw liked Vincent Granville's group Data Science Certification
10 hours ago
Cedric Anover liked Vincent Granville's blog post Hitchhiker's Guide to Data Science, Machine Learning, R, Python
21 hours ago
Vincent Granville posted a blog post

Nice Generalization of the K-NN Clustering Algorithm -- Also Useful for Data Reduction

I describe here an interesting and intuitive clustering algorithm (that can be used for data reduction as well) offering several advantages, over traditional classifiers:More robust against outliers and erroneous dataExecuting much fasterGeneralizing well known algorithmsYou don't need to know K-NN to understand this article -- but click here if you want to learn more about it. You don't need a background in…See More
21 hours ago
Ziv Rubin liked Vincent Granville's blog post The best kept secret about linear and logistic regression
Monday
Kimberly Stewart liked Vincent Granville's blog post Free Deep Learning Book (MIT Press)
Monday
Kimberly Stewart liked Vincent Granville's blog post 27 Great Resources About Logistic Regression
Monday
Marija Zoldin liked Vincent Granville's blog post Two Great Courses on Deep Learning and AI
Sunday
Steve Miller commented on Vincent Granville's blog post Types of Machine Learning Algorithms in One Picture
"This graphic is a beautifully clear representation. Seeing it done well, like this, brings into sharp focus just how badly similar overviews are provided elsewhere!  "
Sunday
Taylor Nelson commented on Vincent Granville's blog post State-of-the-Art Machine Learning Automation with HDT
"Thanks Vincent!  I followed the link to see how it's done in Python.  Appreciate your link to this article!"
Sunday
Taylor Nelson liked Vincent Granville's blog post State-of-the-Art Machine Learning Automation with HDT
Sunday
Robert Louie liked Vincent Granville's blog post Data Science Cheat Sheet
Sunday

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

Nice Generalization of the K-NN Clustering Algorithm -- Also Useful for Data Reduction

Posted on August 15, 2017 at 7:30am 1 Comment

I describe here an interesting and intuitive clustering algorithm (that can be used for data reduction as well) offering several advantages, over traditional classifiers:

  • More robust against outliers and erroneous data
  • Executing much faster
  • Generalizing well known algorithms

You don't need to know K-NN to understand this article -- but click here if you want to…

Continue

Weekly Digest, August 14

Posted on August 12, 2017 at 1:30pm 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.

Upcoming Webinars and Resources
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Two Great Courses on Deep Learning and AI

Posted on August 10, 2017 at 2:00pm 0 Comments

Deep Learning, Neural Networks and AI

The course is a new one by Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain. It will start Aug 15. 

About this course: If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly…

Continue
 
 
 

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