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

One day, will humans be to AI what dogs are to humans now?

Started this discussion. Last reply by Vincent Granville 19 hours ago. 1 Reply

Do you think that one day, humans will find a way to not work and enjoy the life, relying on robots to help them with their needs, just like dogs who don't need to spend their time finding food and…Continue

Measuring Audience Overlap

Started this discussion. Last reply by Mercedes Feb 8. 3 Replies

Hi,I am looking for a tool (online app if possible) that measures the overlap in the number of users between two websites A and B, a tool that would offers statistics such asWebsite A had x visitors…Continue

Optimizing Office Furniture and Enterprise Real Estate Purchases

Started Jan 22 0 Replies

It is well know that office furniture / computer equipment, as well as renting / leasing or purchasing real estate to host your employees, is very expensive for corporations. Are there any companies…Continue

Can AI Algorithms be Fooled?

Started Jan 12 0 Replies

Can AI systems experience illusions as in human vision? For instance, look at the picture below (click on the image to zoom in.)…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

Pouya Esmailian commented on Vincent Granville's blog post A Simple Introduction to Complex Stochastic Processes
"Great article, I reached a better understanding by changing the variables as follows:"
12 hours ago
Vincent Granville replied to Vincent Granville's discussion One day, will humans be to AI what dogs are to humans now?
"Here is an interesting answer posted by David Taylor (PhD in psychology, UC Irvine: Here, in summary form, are my reasons for believing that this will happen. 1. Neurons are excruciatingly slow compared to silicon. Depending on the way you do the…"
19 hours ago
Gedean John Gazola liked Vincent Granville's blog post A Plethora of Original, Not Well-Known Statistical Tests
21 hours ago
Paweł Wawrzała liked Vincent Granville's blog post A Plethora of Original, Not Well-Known Statistical Tests
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victor zurkowski liked Vincent Granville's blog post A Plethora of Original, Not Well-Known Statistical Tests
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victor zurkowski liked Vincent Granville's blog post A Plethora of Original, Not Well-Known Statistical Tests
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John Barnshaw commented on Vincent Granville's blog post The fastest growing data science / big data profiles on Twitter
"Hi Vincent, Can you please explain the difference from a 'logistic regression' and a 'logic regression'?  I am very familiar with the former but have never heard of the latter.  Also, if the only difference is that…"
yesterday
Haran Lopes liked Vincent Granville's blog post 25 Statistical Concepts Explained in Simple English - Part 2
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Haran Lopes liked Vincent Granville's blog post Invitation to Join Data Science Central
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Tauheedul Ali liked Vincent Granville's blog post Free eBook: Applied Data Science (Columbia University)
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Vincent Granville posted blog posts
Tuesday
Azhar Bashir Khan liked Vincent Granville's blog post 31 Statistical Concepts Explained in Simple English - Part 8
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Tauheedul Ali liked Vincent Granville's blog post How and Why: Decorrelate Time Series
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Tauheedul Ali liked Vincent Granville's blog post Don’t Let Data Science Become a Scam
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Rajiv liked Vincent Granville's blog post A Plethora of Original, Not Well-Known Statistical Tests
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Henry Butler liked Vincent Granville's blog post Comprehensive Repository of Data Science and ML Resources
Monday

Comment Wall (15 comments)

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At 9:13am on December 13, 2018, victor zurkowski said…

Dear Vincent,

Do you know how long does membership approval in "Analytic Bridge" take? I want to submit an answer to the self-correcting random walk problem. The answer is long, and I left a copy of my document (not the final draft) in Github.

At 6:24am on October 01, 2017, Nitesh Choudhary gave Vincent Granville a gift
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

Vincent Granville's Videos

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Vincent Granville's Blog

How to Stabilize Data Systems, to Avoid Decay in Model Performance

Posted on February 18, 2019 at 9:30pm 0 Comments

Here we describe a simple methodology to produce predictive scores that are consistent over time and compatible across various clients, to allow for meaningful comparisons and consistency in actions resulting from these scores, such as offering a loan. Scores are used in various contexts, such as web page rankings in search engines, credit score, risk score attached to loans or credit card transactions, the risk that someone might become a terrorist, and more. Typically a score is a function…

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Should you Add your Coursera, Udacity, or DataCamp Training to your Resume?

Posted on February 18, 2019 at 8:30am 0 Comments

It all depends on the classes that you attended. Some are worth listing, some are best not to mention. Here I review of few of these data science curricula, and the impression it can make on hiring managers, depending on your profile, work experience, and strength (or lack of) of these programs.

Three…

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Weekly Digest, February 18

Posted on February 17, 2019 at 9:30am 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. To subscribe, follow this link.  

Announcement…

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Thursday News: DL, NLP, AI, Statistical Tests, Bayesian Reasoning, and more

Posted on February 14, 2019 at 12:30pm 0 Comments

Here is our selection of featured articles and technical resources posted since Monday. Enjoy the reading!

Resources

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