Tips on How to Avoid Biased ModelsBias in the model can happen through different stages of the modeling process. The most important stage is data selection and sometimes bias is the result of selection of the data itself, rather than an error with labeling the data. In this latest DSC podcast we will talk about different categories of data bias and different approaches to avoid a bias decision as a result of low quality data. Download nowJob SpotlightFaculty Open Rank (Assistant or Associate Professor) - MS in Data Science and BS in IS - CUNYHuman Resources Data Analytics Analyst - CCHCSFeatured JobsBusiness Analyst - Electronic Arts (EA)Machine Learning Engineer / Architect - VMwareData Scientist, Analytics (PhD) - FacebookData Engineer - Python - SpotifyInfrastructure Software Engineer - DropboxIntegration Engineer - AirbnbData Scientist - AmazonData Engineer - PinterestHead of Data Science, Alexa InternetData Scientist (Algorithm Developer) - Bridgestone AmericasData Scientist / Statistician / Economist - AppleSoftware Development Engineer I - Mastercard LabsData Architect (Remote) - LenovoSenior Data Engineer - WalmartCloud Data Engineer, Revenue Science - TwitterSenior Data Engineer - Modeling and ML - Rockstar GamesEngineering Manager - Metadata Ingest - HuluData Scientist, Workforce Analytics - Warner Bros. EntertainmentCheck out the most recent jobs on AnalyticTalent.comHealthcare or the Economy? Bull! It’s Healthcare AND the EconomyAuthor: Bill Schmarzo - other articles by Bill SchmarzoWe have a once in a lifetime opportunity to address one of modern society’s biggest challenges – the choice between healthcare or the economy. For the past several decades, we have treated this as an “either or” choice between one or the other; that to improve the economy we must reduce healthcare spending or to improve healthcare we must sacrifice the economy. To that sort of thinking I say BULL!The coronavirus COVID-19 situation is showing us a very important lesson – that healthcare and the economy are tightly linked; that it can’t be an “either or” choice. As a society – as a world – we must start thinking more holistically about the healthcare or economy challenge and reframe our approach from a scarcity (“either or”) mentality to an abundance (”and”) mentality.Read the full articleRelated articles: AI | ML | Deep Learning | Data Analytics | Big DataUpcoming DSC WebinarsDemocratizing Analytics from the Ground Up - April 28Graph Algorithms Combined with ML are Saving the World - April 30See More

500 Petabytes of Data to Understand the Universe Better - March 18The Vera C. Rubin Observatory, currently under construction in Chile, will conduct a vast astronomical survey of our dynamic Universe starting in 2022. They plan to collect 500 petabytes of image data by observing the skies continuously for 10 years and produce nearly instant alerts for objects that change in position or brightness every night. In addition to astronomical data, their dataset will include DevOps, IoT, and real-time monitoring data. Register todayJob SpotlightFaculty Open Rank (Assistant or Associate Professor) - MS in Data Science and BS in IS - CUNYHuman Resources Data Analytics Analyst - CCHCSSenior Consultant: Market Research - RSGFeatured JobsData Scientist - JetBlue AirwaysManufacturing Data Analyst, Technical Infrastructure - GoogleSenior Engineer, Data Science Operations - SquarespaceData Scientist, Ad Experience - SpotifyData Scientist - CiscoData scientist/Technical product Manager - PayPalData Engineer, Trust - AirbnbData Scientist - Barclays Investment BankSoftware Engineer - Data - PandoraCamera Machine Learning Engineer - AppleStaff Data Scientist - WalmartResearch Data Scientist - FacebookSoftware Engineer - Backend - Data Platform - UberSenior Analyst Sourcing Operations - The Coca-Cola CompanyCheck out the most recent jobs on AnalyticTalent.comUpcoming DSC Webinars and ResourcesEOL for ETL–The Future of Data Wrangling in the Cloud - eBookAccelerating AI Adoption with Machine Learning Operations (MLOps)Managing Risk in Decentralized Networks with Time Series - March 24500 Petabytes of Data to Understand the Universe Better - March 18Build Better ML Models with These 5 QA Methods - On-Demand WebinarSee More

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The methodology described here has broad applications, leading to new statistical tests, new type of ANOVA (analysis of variance), improved design of experiments, interesting fractional factorial designs, a better understanding of irrational numbers leading to cryptography, gaming and Fintech applications, and high quality random numbers generators (and when you really need them). It also features exact arithmetic / high performance computing and distributed algorithms to compute millions of binary digits for an infinite family of real numbers, including detection of auto- and cross-correlations (or lack of) in the digit distributions.The data processed in my experiment, consisting of raw irrational numbers (described by a new class of elementary recurrences) led to the discovery of unexpected apparent patterns in their digit distribution: in particular, the fact that a few of these numbers, contrarily to popular belief, do not have 50% of their binary digits equal to 1. It turned out that perfectly random digits simulated in large numbers, with a good enough pseudo-random generator, also exhibit the same strange behavior, pointing to the fact that pure randomness may not be as random as we imagine it is. Ironically, failure to exhibit these patterns would be an indicator that there really is a departure from pure randomness in the digits in question.In addition to new statistical / mathematical methods and discoveries and interesting applications, you will learn in my article how to avoid this type of statistical traps that lead to erroneous conclusions, when performing a large number of statistical tests, and how to not be misled by false appearances. I call them statistical hallucinations and false outliers.This article has two main sections: section 1, with deep research in number theory, and section 2, with deep research in statistics, with applications. You may skip one of the two sections depending on your interests and how much time you have. Both sections, despite state-of-the-art in their respective fields, are written in simple English. It is my wish that with this article, I can get data scientists to be interested in math, and the other way around: the topics in both cases have been chosen to be exciting and modern. I also hope that this article will give you new powerful tools to add to your arsenal of tricks and techniques. Both topics are related, the statistical analysis being based on the numbers discussed in the math section. One of the interesting new topics discussed here for the first time is the cross-correlation between the digits of two irrational numbers. These digit sequences are treated as multivariate time series. I believe this is the first time ever that this subject is not only investigated in detail, but in addition comes with a deep, spectacular probabilistic number theory result about the distributions in question, with important implications in security and cryptography systems. Another related topic discussed here is a generalized version of the Collatz conjecture, with some insights on how to potentially solve it.Read the full article here. Content1. On the Digits Distribution of Quadractic Irrational NumbersProperties of the recursionReverse recursionProperties of the reverse recursionConnection to Collatz conjectureSource codeNew deep probabilistic number theory resultsSpectacular new result about cross-correlationsApplications2. New Statistical Techniques Used in Our AnalysisData, features, and preliminary analysisDoing it the right wayAre the patterns found a statistical illusion, or caused by errors, or real?Pattern #1: Non-Gaussian behaviorPattern #2: Illusionary outliersPattern #3: Weird distribution for block countsRelated articles and booksAppendixSee More

Find your Data Scientist today with a special Buy 1 and Get 50% off the second Job Posting By February 29thData Scientists are a rare breed and AnalyticTalent / Data Science Central is the largest community of its kind with a million+ members that engage in discussions, trends and the best practices. It is the only job board devoted to its own scientific community. Learn more and get the promo code here.Job SpotlightHuman Resources Data Analytics Analyst - CCHCSSenior Consultant: Market Research - RSGManager-Optimization Consulting - River Logic Inc.Sr. Research Data Scientist - UC DavisFeatured JobsData Scientist - McDonald'sData Science & Analytics Leader - NetflixLead Product Analytics/Data Scientist - PayPalData Scientist – Disney+ Product, CommerceConsultant Statistics and Data Science - ShellApplied Scientist - YelpData Scientist - AppleManager, Data Science, Content Marketing Analytics - SpotifyData Scientist, Analytics - FacebookData Analyst, Health, Google AI - GoogleSenior Software Engineer - PandoraData Scientist II - MastercardSenior Data Scientist - TripadvisorSenior Analyst, Advanced Analytics - HuluSenior Data Scientist, Growth - TwitterCheck out the most recent jobs on AnalyticTalent.comUpcoming DSC Webinars and ResourcesBuild Better ML Models with These 5 QA Methods - On-Demand WebinarData Science Fails: Ignoring Business Rules & Expertise - DSC PodcastTrends in Social Network Analysis - DSC PodcastSee More

Summary: The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out the big news is how much more capable all the platforms have become. Of course there are also some interesting winner and loser stories.The Gartner Magic Quadrant for Data Science and Machine Learning Platforms is just out for 2020. The really big news is how many excellent choices are now available. In a remarkable move, the whole field of competitors has moved strongly up and to the right offering more and more Leaders or near-leader Visionaries than ever before.It’s a mark of maturity in our industry that so many platforms offer fully capable model development, operationalizing, and management features. That list of requirements as defined by Gartner grows longer every year and earning a better rating requires increasing capability and increasing customer satisfaction.What Are the Major Changes?As in previous years we’ve charted the major changes in position using green arrows for improvement and red arrows to indicate a reduced rating. The blue dots are current ratings and the gray dots are from a year ago.Read the full article here with the 2020 version of the above chart, with comments.See More

In this notebook, we try to predict the positive (label 1) or negative (label 0) sentiment of the sentence. We use the UCI Sentiment Labelled Sentences Data Set.Sentiment analysis is very useful in many areas. For example, it can be used for internet conversations moderation. Also, it is possible to predict ratings that users can assign to a certain product (food, household appliances, hotels, films, etc) based on the reviews.In this notebook we are using two families of machine learning algorithms: Naive Bayes (NB) and long short term memory (LSTM) neural networks.AYLIENDeeplearning4jUnderstanding LSTM NetworksEmpirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling The Unreasonable Effectiveness of Recurrent Neural NetworksWe will use pandas, numpy for data manipulation, nltk for natural language processing, matplotlib, seaborn and plotly for data visualization, sklearn and keras for learning the models.Read the full article with source code and illustrations, here. See More

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