In a Q&A, Vincent Granville, executive data scientist and co-founder of Data Science Central, discusses how AI has changed the data science field and the ways in which it will continue to do so. The data science field has changed greatly with the advent of AI. Artificial intelligence has enabled the rise of citizen data scientists, the automation of data scientist's workloads, as well as the need for more skilled data scientists. Vincent Granville, co-founder of Data Science Central, a community and resource site for data specialists, expects to see an increase in AI and IoT in data science over the next few years, even as AI continues to change the data science field. In this Q&A, Granville discusses data science trends, the impact of AI and IoT on data scientists, how organizations and data scientists will have to adapt to increased data privacy regulations, and the evolution of AI. Data Science Central was acquired by TechTarget on March 4. Will an increase in citizen data scientists due to AI, as well as an increase of more formal data science education programs, help fix the so-called data scientist shortage?

You can search Google for pictures similar to a given image, for plagiarism detection or to find people that look like you. Here's how I did a test: Google returned pictures of people that look like me (see figure 2 and 4) - actually, pictures that look like the picture I uploaded, whether they represent a human being or not, or whether they represent a man or a woman. . In this first test, the results reported by Google are based on metadata, despite my efforts to eliminate metadata in the picture in figure 1. Unfortunately, as you can see, the results are wrong. Google correctly figured out that figure 1 represents Vincent Granville, and indeed the first picture returned by Google is the one from figure 1.

See below for an outdated HTML version; the PDF version is of much higher quality, proofread and with the most recent content. To download a copy, you must be a member. Click here to sign up and check all the benefits of membership. New random number generator: simple, strong and fast Lifetime value of an e-mail blast: much longer than you think Two great ideas to create a much better search engine Identifying the number of clusters: finally a solution Online advertising: a solution to optimize ad relevancy Example of architecture for AaaS (Analytics as a Service) Why and how to build a data dictionary for big data sets Hidden decision trees: a modern scoring methodology Approximate Solutions to Linear Regression Problems Preserving metric and score consistency over time and across clients Advertising: reach and frequency mathematical formulas Real Life Example of Text Mining to Detect Fraudulent Buyers Sales forecasts: how to improve accuracy while simplifying models? How could Amazon increase sales by redefining relevancy?

You can search Google for pictures similar to a given image, for plagiarism detection or to find people that look like you. I used a screenshot tool, to extract a picture of myself, free of compromising metadata (see figure 1 and 3), then clicked on the picture icon in the Google search box to upload the screenshot picture. I used a screenshot tool, to extract a picture of myself, free of compromising metadata (see figure 1 and 3), then clicked on the picture icon in the Google search box to upload the screenshot picture. Google returned pictures of people that look like me (see figure 2 and 4) - actually, pictures that look like the picture I uploaded, whether they represent a human being or not, or whether they represent a man or a woman. . In this first test, the results reported by Google are based on metadata, despite my efforts to eliminate metadata in the picture in figure 1.

Very interesting document, relatively recent (September 2015), authored by David Donoho (Statistics professor at Stanford) and posted on one of the MIT websites, here (41 pages, PDF). Below you will find the abstract and the table of content. Data Science without statistics is possible, even desirable. Data Science without statistics is possible, even desirable. Statistics is the least important part of data science.