Gifts Received (6)
Vincent Granville's Page
Profile Information
- Short Bio:
- Founder and self-publisher at DataShaping.com.
- Company:
- Data Shaping Solutions
- Job Title:
- Executive Data Scientist, Co-Founder
- Seniority:
- C-Level/SVP/Executive Team
- Job Function:
- Other
- Country
- United States
- Number of employees:
- 1 to 49
- Industry:
- Publishing/Broadcast/Media
- LinkedIn Profile:
- http://www.linkedin.com/in/vincentg
- Interests:
- Networking, New venture, Recruiting, Other
- Topics of Interest
- Data Science, Machine Learning, Data Visualization, Data Storytelling, Fintech, Scientific Research, Business Analytics, Digital Publishing, Mathematics
Bio
Vincent Granville, PhD is a pioneering data scientist, mathematician, entrepreneur, investor, co-founder of Data Science Central (acquired in 2020) and DataShaping.com, former VC-funded executive, author and patent owner. Vincent's past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, CNET, InfoSpace and other Internet startup companies (one acquired by Google). Vincent is also a former post-doct from Cambridge University, and the National Institute of Statistical Sciences (NISS).
Vincent published in Journal of Number Theory, Journal of the Royal Statistical Society (Series B) and IEEE Transactions on Pattern Analysis and Machine Intelligence. One of his books was published by Wiley. For details, see my Google Scholar profile, here.
Vincent Granville's Videos
Vincent Granville's Blog
Simple Machine Learning Approach to Testing for Independence
Posted on April 7, 2021 at 9:00pm 0 Comments 0 Likes
We describe here a methodology that applies to any statistical test, and illustrated in the context of assessing independence between successive observations in a data set. After reviewing a few standard approaches, we discuss our methodology, its benefits, and drawbacks. The data used here for illustration purposes, has known theoretical…
ContinueA Plethora of Machine Learning Tricks, Recipes, and Statistical Models
Posted on April 5, 2021 at 6:59pm 0 Comments 0 Likes
Defining and Measuring Chaos in Data Sets: Why and How, in Simple Words
Posted on March 28, 2021 at 3:00pm 0 Comments 1 Like
There are many ways chaos is defined, each scientific field and each expert having its own definitions. We share here a few of the most common metrics used to quantify the level of chaos in univariate time series or data sets. We also introduce a new, simple definition based on metrics that are familiar to everyone. Generally speaking, chaos…
ContinueHurwitz-Riemann Zeta And Other Special Probability Distributions
Posted on March 21, 2021 at 8:30pm 0 Comments 1 Like
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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.
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
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
Hi Vincent,
Can you suggest some points or links about serious data quality issue with the information pulled.
Thanks
Lissy
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)?
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