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Vincent Granville
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  • Issaquah, WA
  • United States
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Vincent Granville posted a blog post

Machine Learning and Data Science Cheat Sheet

Originally published in 2014 and viewed more than 200,000 times, this is the oldest data science cheat sheet - the mother of all the numerous cheat sheets that are so popular nowadays. I decided to update it in June 2019. While the first half, dealing with installing components on your laptop and learning UNIX, regular expressions, and file management hasn't changed much, the second half, dealing with machine learning, was rewritten entirely from scratch. It is amazing how things changed in…See More
Jun 6
Vincent Granville posted a blog post

Gentle Approach to Linear Algebra, with Machine Learning Applications

This simple introduction to matrix theory offers a refreshing perspective on the subject. Using a basic concept that leads to a simple formula for the power of a matrix, we see how it can solve time series, Markov chains, linear regression, data reduction, principal components analysis (PCA) and other machine learning problems. These problems are usually solved with more advanced matrix calculus, including eigenvalues, diagonalization, generalized inverse matrices, and other types of matrix…See More
May 28
Vincent Granville posted a blog post

New Book: Classification and Regression In a Weekend (in Python)

We have added a new free book in our selection exclusively for DSC members. See the first entry below, to get started with machine learning with Python.1. Book: Classification and Regression In a WeekendThis tutorial began as a series of weekend workshops created by Ajit Jaokar and Dan Howarth. The idea was to work with a specific (longish) program such that we explore as much of it as possible in one weekend. This book is an attempt to take this idea online. The best way to use this book is to…See More
May 16
Vincent Granville posted a blog post

Confidence Intervals Without Pain, with Excel

We propose a simple model-free solution to compute any confidence interval and to extrapolate these intervals beyond the observations available in your data set. In addition we propose a mechanism  to sharpen the confidence intervals, to reduce their width by an order of magnitude. The methodology works with any estimator (mean, median, variance, quantile, correlation and so on) even when the data set violates the classical requirements necessary to make traditional statistical techniques work.…See More
May 9

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) 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 (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. 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.
My Website or LinkedIn Profile (URL):
http://www.linkedin.com/in/vincentg
Field of Expertise:
Data Mining, Marketing Databases, Web Analytics, Statistical Consulting, Other
Years of Experience in Analytical Role:
15+
Professional Status:
C-Level
Interests:
Networking, New Venture, Other
What is your Favorite Data Mining or Analytical Website?
http://www.analyticbridge.com
What Other Analytical Website do you Recommend?
http://www.datasciencecentral.com
Your Company:
Data Shaping Solutions
Industry:
Internet
How did you find out about AnalyticBridge?
Founder

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Vincent Granville's Blog

Machine Learning and Data Science Cheat Sheet

Posted on June 6, 2019 at 8:27pm 0 Comments

Originally published in 2014 and viewed more than 200,000 times, this is the oldest data science cheat sheet - the mother of all the numerous cheat sheets that are so popular nowadays. I decided to update it in June 2019. While the first half, dealing with installing components on your laptop and learning UNIX, regular expressions, and file management hasn't changed much, the second half, dealing with machine learning, was rewritten entirely from scratch. It is amazing how things changed in…

Continue

7 Simple Tricks to Handle Complex Machine Learning Issues

Posted on June 4, 2019 at 12:00pm 0 Comments

We propose simple solutions to important problems that all data scientists face almost every day. In short, a toolbox for the handyman, useful to busy professionals in any field.

1. Eliminating sample size effectsMany statistics, such as correlations or R-squared, depend on the sample size, making it difficult to…

Continue

Gentle Approach to Linear Algebra, with Machine Learning Applications

Posted on May 28, 2019 at 9:00pm 0 Comments

This simple introduction to matrix theory offers a refreshing perspective on the subject. Using a basic concept that leads to a simple formula for the power of a matrix, we see how it can solve time series, Markov chains, linear regression, data reduction, principal components analysis (PCA) and other machine learning problems. These problems are usually solved with more advanced matrix calculus, including eigenvalues, diagonalization, generalized inverse matrices, and other types of…

Continue

New Book: Classification and Regression In a Weekend (in Python)

Posted on May 16, 2019 at 6:24pm 0 Comments

We have added a new free book in our selection exclusively for DSC members. See the first entry below, to get started with machine learning with Python.

1. Book: Classification and Regression In a Weekend

This tutorial began as a series of weekend workshops created by Ajit Jaokar and Dan Howarth. The idea was to work with a specific (longish) program such that we explore as much of it as possible in one weekend. This book is an attempt to take this idea online.…

Continue

Confidence Intervals Without Pain, with Excel

Posted on May 9, 2019 at 11:30am 0 Comments

We propose a simple model-free solution to compute any confidence interval and to extrapolate these intervals beyond the observations available in your data set. In addition we propose a mechanism  to sharpen the confidence intervals, to reduce their width by an order of magnitude. The methodology works with any estimator (mean, median, variance, quantile, correlation and so on) even when the data set violates the classical requirements necessary to make traditional statistical techniques…

Continue

Comment Wall (79 comments)

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At 6:48pm on December 13, 2018, Victor Zurkowski said…

Dear Vincent,

I want to let you know that appreciate your posts. Your excursions in data science, number theory, probabilities, experimental mathematics, etc. are thoughtful, provocative, on occasions infuriating opinionated, and never dull. I also admire how prolific you are. 

Here's to many more blog entries,

Victor

At 1:15am on June 24, 2013, Hina Ranglani said…
sir,looking for a answer how
scaling up for high dimensional data& high speed data streams..
At 4:07am on April 12, 2013, Nene Lawani said…
Thanks Vincent! Sorry I missed this earlier...
At 4:06pm on March 10, 2013, Serge Kozlov said…

Thanks for the invite!

At 10:52am on March 10, 2013, Robert Downing said…

Thanks for the invite!

At 3:41am on March 9, 2013, Shane Campbell said…

vincent, 

thanks for adding me. can i ask you why you choose to add people like me? didn't see it coming?

regards,

shane

At 6:37am on February 21, 2013, hunter.robertallan said…

Thanks also for the invite. I do have a question about data mining methods and tools. I have a specific situation in mind at work, and would like some professional feedback. Anyone willing to lend an ear and opinion..? thanks...rob

At 4:01am on February 17, 2013, Rishi Jain said…

Hey Vincent,

 

Sometime back I saw internship material on your site. I am working in the Analytics Industry and looking for improving my technical skills. Please let me know in case you guys are offering any internships which I can pursue along with my job.

Regards,

Rishi

At 3:04pm on December 19, 2012, Vickie Comrie said…

Thanks Vincent!  I am honored to be your friend.

Cheers,

Vickie

At 1:05pm on September 29, 2012, Ankit Sharma said…

Thank you Vincent. 

 
 
 

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