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

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

More Free Data Sets

Started Dec 7, 2016 0 Replies

After posting my article A Plethora of Data Set Repositories, I received a few…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

Vincent Granville posted a blog post

Weekly Digest, February 27

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.Featured Resources and Technical ContributionsTimestamp Data Visualization by Matplotlib + …See More
13 hours ago
Robert liked Vincent Granville's blog post 21 Great Articles and Tutorials on Time Series
yesterday
Petronio Silva liked Vincent Granville's blog post 21 Great Articles and Tutorials on Time Series
yesterday
Mario Navarro liked Vincent Granville's blog post Python vs R: 4 Implementations of Same Machine Learning Technique
Friday
Abdoulaye Diallo liked Vincent Granville's blog post 21 Great Articles and Tutorials on Time Series
Friday
Vincent Granville posted blog posts
Friday
Patricia Moore liked Vincent Granville's blog post Python vs R: 4 Implementations of Same Machine Learning Technique
Thursday
Paul McLeod commented on Vincent Granville's blog post Python vs R: 4 Implementations of Same Machine Learning Technique
"The Perl version really is rather longhanded and a good candidate for translation, although the algorithm is one which is very suited to Perl, and perhaps does not let Python or certainly R really sing, given its hashes and regexp…"
Thursday
Raj Bhatt commented on Vincent Granville's blog post For tax purposes, how do you define a robot?
"It would be interesting to see how a government would tax robots, if the robots are based in another country. Can the US tax Mexican automotive robots or Chinese footwear-making machines, or Indian software ??"
Thursday
Renato Umeton commented on Vincent Granville's blog post Plotting Multiple Columns in D3
"Nice! What tool are you using for this? (the tool we see in the screenshot, with the Render button, etc)"
Thursday
Renato Umeton commented on Vincent Granville's blog post Plotting Multiple Columns in D3
"Nice! What tool are you using for this?"
Thursday
Manish Madhok liked Vincent Granville's blog post How and Why: Decorrelate Time Series
Thursday
Manish Madhok liked Vincent Granville's blog post How and Why: Decorrelate Time Series
Thursday
Asad Ali liked Vincent Granville's blog post The Twilight Zone Between True and False
Wednesday
Ashish Kumar pandey liked Vincent Granville's blog post The Mathematics of Machine Learning
Wednesday
Shay Pal liked Vincent Granville's blog post How and Why: Decorrelate Time Series
Wednesday

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, February 27

Posted on February 25, 2017 at 10:30am 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.

Featured Resources and Technical Contributions

Continue

21 Great Articles and Tutorials on Time Series

Posted on February 23, 2017 at 4:15pm 0 Comments

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, ouliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, and many more. To keep receiving these articles, …

Continue

Thursday News: R, Python, Julia, AI, IoT, DataViZ, Hadoop, ML

Posted on February 22, 2017 at 7:30pm 0 Comments

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

Continue

How and Why: Decorrelate Time Series

Posted on February 21, 2017 at 11:00pm 0 Comments

When dealing with time series, the first step consists in isolating trends and periodicites. Once this is done, we are left with a normalized time series, and studying the auto-correlation structure is the next step, called model fitting. The purpose is to check whether the underlying data follows some well known stochastic process with a similar auto-correlation structure, such as ARMA processes, using tools such as…

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