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

Using AI to Write Articles and Research Papers

Started Jun 17 0 Replies

I am wondering if news outlets sometimes use bots to write articles. I've seen in the past articles that looked like they were written by a bot. The sentences didn't make much sense, and it almost…Continue

Will GDPR kill business in Ireland?

Started this discussion. Last reply by James Theobald Jun 7. 2 Replies

Companies such as Facebook and Apple use Ireland as a tax shelter, with great benefits for them. As billion of dollars in lawsuits could result from GDPR, will Ireland still be able to attract big…Continue

Can AI Robots Beat Professional Stock Traders?

Started May 21 0 Replies

I am not talking here about high frequency trading, which has been done by automated algorithms long ago, and that humans can not beat (if done well!) But more like, if you allow a robot or a human…Continue

Which Data Science Techniques Should I Introduce to my Team Members?

Started this discussion. Last reply by Yann MAINVIS May 17. 1 Reply

This is an interesting question asked by a director managing a team of scientists, who wants to keep his team updated on data science techniques. Below is the email exchange in question:Hey Vincent.…Continue

 

Vincent Granville's Page

Profile Information

Short Bio
Data science pioneer, founder, author, CEO, investor, 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
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

Data science pioneer, founder, author, CEO, investor, 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

Rohan Kotwani commented on Vincent Granville's blog post Free Book: Applied Stochastic Processes
"This book does a great job explaining concepts which are intuitive to data scientist/machine learning engineering in explainable language."
yesterday
Vincent Granville posted blog posts
yesterday
Vincent Granville posted a blog post
Thursday
Mel Oyler liked Vincent Granville's blog post Free Book: Applied Stochastic Processes
Wednesday
Tim Matteson liked Vincent Granville's blog post Weekly Digest, July 16
Jul 16
Roger Bertoia liked Vincent Granville's blog post What do mathematicians mean by good math and bad math?
Jul 16
Tetiana Boichenko commented on Vincent Granville's blog post Weekly Digest, July 16
"Thanks for the feature, Vincent!"
Jul 16
Tetiana Boichenko liked Vincent Granville's blog post Weekly Digest, July 16
Jul 16
Vincent Granville's blog post was featured

Weekly Digest, July 16

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 ContributionsBest Machine Learning Tools: Experts’ Top Picks …See More
Jul 15
Vincent Granville posted a blog post

Weekly Digest, July 16

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 ContributionsBest Machine Learning Tools: Experts’ Top Picks …See More
Jul 15
Ali Awadh liked Vincent Granville's blog post Is it still possible today to become a self-taught data scientist?
Jul 14
Frederick T Williams liked Vincent Granville's blog post Six categories of Data Scientists
Jul 14
Frederick T Williams liked Vincent Granville's blog post 66 job interview questions for data scientists
Jul 14
Lance Norskog commented on Vincent Granville's blog post The First Things you Should Learn as a Data Scientist - Not what you Think
"Also- put up a production site. Just a web site where you upload cat pix, the app consults Google's API, then gives an answer. This is not data sci per se, but you're going to want to understand the quirks of putting Data Sci into…"
Jul 14
Prithvi Raj Bhardwaj commented on Vincent Granville's blog post Advanced Machine Learning with Basic Excel
"Beautiful. Very nice article. Excel makes you understand."
Jul 13
Harshendu Desai commented on Vincent Granville's blog post Is it still possible today to become a self-taught data scientist?
"The problem  with  industries is too much  fishing  in  interview process which  restrict the candidate to do  the actual  job.  Everyone wants everything.  If you look at job  requirement…"
Jul 13

Comment Wall (14 comments)

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At 6:24am on October 01, 2017, Nitesh Choudhary gave Vincent Granville a gift
Gift
Your posts are very informative and I have learned a lot from them. Thanks for sharing!
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 

Vincent Granville's Videos

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Vincent Granville's Blog

Weekly Digest, July 23

Posted on July 21, 2018 at 7:30am 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

Feature Selection For Unsupervised Learning

Posted on July 20, 2018 at 9:34am 0 Comments

This is my presentation for the IBM data science day, July 24.

Abstract

After reviewing popular techniques used in supervised, unsupervised and semi-supervised machine learning, we focus on feature selection methods in these different contexts, especially the metrics used to assess the value of a feature or set of features, be it binary, continuous or categorical…

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Weekly Digest, July 16

Posted on July 15, 2018 at 9:30am 1 Comment

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
 
 
 

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