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R RMDS (Research Methods and Data Science) Search Home Courses New Discussions 📣RMDS Announcements 💼General Discussion 📼Free Videos 🆕Getting Started 📈Analytics and Modeling 🛎️New Products & Services Events 🌐RMDS Annual Conference 2023 🌎RMDS Annual Conference 2022 🌎RMDS Annual Conference 2021 🌎RMDS Annual Conference 2020 🌎RMDS Annual Conference 2019 🎫Upcomimg Events Career Development 💼Employment Opportunities 🔬Mentorship Program 🔬STEM Acceleration Program 🔬Deep Dive Program 🔬Project Impactor Upskill Program 🔬Progression System / Value Journey 📋International Youth Test Project 🎓Data Science Certification Courses Turning Business Problems into Data Science Problems AI Bias & Surveillance: Recognition, Analysis, and Prediction AI & Ethics: Bias, Diversity and Ethical Decision Making with AI Systems Facebook Ad Analysis Using Python Project 📊Data 📈Workflow ⚙️Code 📝Report 📽️Plan Expert Circle 👩🏫Expert Introduction 📖Publishing with RMDS 🗣️Speaking at IM Data 2023 conference 🏫Become a mentor at RMDS 📚2022 Impactful Data Science Book Project RMDS Partners 📓RM Institute 🧑🤝🧑USC Data Science Professional Practicum Links RMDS LinkedIn Group RMDS (Research Methods and Data Science) Global Portal Search Home Courses New Discussions 📣RMDS Announcements 💼General Discussion 📼Free Videos 🆕Getting Started 📈Analytics and Modeling 🛎️New Products & Services Events 🌐RMDS Annual Conference 2023 🌎RMDS Annual Conference 2022 🌎RMDS Annual Conference 2021 🌎RMDS Annual Conference 2020 🌎RMDS Annual Conference 2019 🎫Upcomimg Events Career Development 💼Employment Opportunities 🔬Mentorship Program 🔬STEM Acceleration Program 🔬Deep Dive Program 🔬Project Impactor Upskill Program 🔬Progression System / Value Journey 📋International Youth Test Project 🎓Data Science Certification Courses Turning Business Problems into Data Science Problems AI Bias & Surveillance: Recognition, Analysis, and Prediction AI & Ethics: Bias, Diversity and Ethical Decision Making with AI Systems Facebook Ad Analysis Using Python Project 📊Data 📈Workflow ⚙️Code 📝Report 📽️Plan Expert Circle 👩🏫Expert Introduction 📖Publishing with RMDS 🗣️Speaking at IM Data 2023 conference 🏫Become a mentor at RMDS 📚2022 Impactful Data Science Book Project RMDS Partners 📓RM Institute 🧑🤝🧑USC Data Science Professional Practicum Links RMDS LinkedIn Group RMDS (Research Methods and Data Science) Global Portal HOME HOME Latest Log inSign up WELCOME TO THE GLOBAL RMDS COMMUNITY! Welcome to the Global RMDS community, where you can interact with thousands of data driven researchers and practioners. Here, you can display your projects and become more impactful. →General Discussion →Free Videos →RMDS Annual Conference Events and Recordings →Courses →Projects Share GR Global RMDS admin 📖member 3 days ago Posted in General Discussion HAPPY NEW YEAR Happy New Year to all here from RMDS! LikeComment 0 comments Share AL Alex Liu 📖member 3 days ago Posted in General Discussion 2023 TECH TRENDS PER FAST COMPANY https://www.fastcompany.com/90827380/the-biggest-tech-trends-of-2023-according-to-over-40-experts GR Liked by Global LikeComment 0 comments Share GR Global RMDS admin 📖member 6 days ago Posted in Mentorship Program ChatGPT Tutorial - A Crash Course on Chat GPT for Beginners - YouTube LikeComment 0 comments Share GR Global RMDS admin 📖member 7 days ago Posted in General Discussion Advanced ChatGPT Guide - How to build your own Chat GPT Site - YouTube is interesting. LikeComment 0 comments Share AL Alex Liu 📖member 10 days ago Posted in General Discussion 2023 WILL BE THE AI YEAR! https://www.linkedin.com/news/story/ais-next-frontier-5515732/ GR Liked by Global LikeComment 0 comments Share GR Global RMDS admin 📖member 12 days ago Posted in Employment Opportunities WORLD BANK DATA OPPORTUNITY Here's a great opportunity for a Data Governance pro to join the private sector arm of the World Bank Group. Apply now! closes Jan 3. https://worldbankgroup.csod.com/ats/careersite/JobDetails.aspx?id=20395&site=1 AL Liked by Alex LikeComment 0 comments Share GR Global RMDS admin 📖member 14 days ago Posted in General Discussion DATA SCIENCE AND AI TRENDS 2023 Top Data Science and AI Trends for 2023 (analyticsindiamag.com) GR AL Liked by Global and 1 other LikeComment 1 comment Share VG Vincent Granville 📖member 14 days ago Posted in General Discussion SYNTHETIZING THE INSURANCE DATASET USING COPULAS: TOWARDS BETTER SYNTHETIZATION In the context of synthetic data generation, I’ve been asked a few times to provide a case study focusing on real-life tabular data used in the finance or health industry. Here we go: this article fills this gap. The purpose is to generate a synthetic copy of the real data set, preserving the correlation structure and all the statistical distributions attached to it. I went one step further and compared my results with those obtained with one of the most well-known vendors in this market: Mostly.ai. I was able to reverse-engineer the technique that they use, and I share all the details in this article. It is actually a lot easier than most people think. Indeed, the core of the method relies on a few lines of Python code, calling four classic functions from the Numpy and Scipy libraries. Caption: Comparing real data with two synthetic copies Automatically detecting large homogeneous groups — called nodes in decision trees — and using a separate copula for each node is an ensemble technique not unlike boosted trees. In the insurance dataset, I manually picked up these groups. Either way (manual or automated), it leads to better performance. Testing how close your synthetic data is to the real dataset using Hellinger or similar distances is not a good idea: the best synthetic dataset is the exact replica of your real data, leading to overfitting. Instead, you might want to favor synthetized observations with summary statistics (including the shape of the distribution in high dimensions) closely matching those in the real dataset, but with the worst (rather than best) Hellinger score. This allows you to create richer synthetic data, including atypical observations not found in your training set. Extrapolating empirical quantile functions (as opposed to interpolating only) or adding uncorrelated white noise to each feature (in the real or synthetic data) are two ways to generate observations outside the observed range when using copula-based methods, while keeping the structure present in the real data. Read the full article with Python implementation, here . See more GR AL Liked by Global and 1 other LikeComment 0 comments Share R RMDSVideo admin 📖member 19 days ago Posted in Free Videos TOKENIZATION: WHAT IS THE OPPORTUNITY? Your browser does not support the video tag. AL Liked by Alex LikeComment 0 comments Share R RMDS admin 📖member 19 days ago Posted in Data IM DATA CRM TRAINING: CLV DATASET IM Data CRM Training: CLV Dataset by Professor Sijun Wang 0_RBC illustration.xlsx 16.8 KB AL Liked by Alex LikeComment 0 comments Upcoming events * 1 Sep RMDS Annual Conference 2023 07:00 PM - 10:00 AM UTC TRENDING POSTS * AL 2023 Tech Trends per Fast Company Alex Liu * GR Happy New YearGlobal RMDS This website uses cookies to provide you with the best experience. Read our Cookie Policy to learn more. DeclineAccept This website uses cookies to provide you with the best experience. Read our Cookie Policy to learn more. DeclineAccept ` ` ` ` ` `