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Whether it’s because of experience, physical ability, or education level, some jobs tend towards a certain age of worker more than others.

Meet the people as old in their jobs as Tom Brady is in his.

Why some mathematicians argue the economy is designed to create a few super rich people – unless we stop it.

Explore and learn about New York City’s trees. Discover their species and diameter, record your stewardship activities, and share favorite trees with friends.

Use the {scales} R package to automatically adjust and format x- and y-axis scales to use log base 10 and natural log values

Every ten years since 1952, Sight and Sound, a British film magazine, has asked critics to list the greatest movies of all time. The magazine announced the results from the 2022 poll. There was a c…

Personal website and portfolio of Dr. Alison Hill.

Personal website and portfolio of Dr. Alison Hill.

Personal website and portfolio of Dr. Alison Hill.

Personal website and portfolio of Dr. Alison Hill.

Personal website and portfolio of Dr. Alison Hill.

Personal website and portfolio of Dr. Alison Hill.

What is the XY problem? When asking questions, how do I recognize when I'm falling into it? How do I avoid it? Return to FAQ index Other languages: ES, JA, PT, RU

R package for MetaboAnalyst. Contribute to xia-lab/MetaboAnalystR development by creating an account on GitHub.

Tidy Dataframes and Expressions with Statistical Details 📜🔣 - GitHub - IndrajeetPatil/statsExpressions: Tidy Dataframes and Expressions with Statistical Details 📜🔣

‎News · 2022

Musings of a Computer Scientist.

What role did the neighborhood you grew up in have in shaping your economic opportunities? Author Aaron Williams tells a data story about migration, community, and returning to your roots.

Tools that simplify some basic tasks in exploring and analyzing a dataset in a matrix or data.frame that contains data on demographics (e.g., counts of residents in poverty) and local environmental indicators (e.g., an air quality index), with one row per spatial location (e.g., Census block group). Key functions help to find relative risk or similar ratios of means in demographic groups, etc. For any imported/suggested packages not on CRAN, see http://ejanalysis.github.io

Weekly bite-sized tips on DataViz, Shiny and Stats/Machine Learning.

In this lab session, I share how to use apriori algorithm for association mining. The goal is to find useful causal and association rules which can help in designing promotions for the company. Plus, you get to see what's served at an Indian cafe.

Creating interactive plots of longest-active authors and most popular book per decade in ggplot2 and ggplotly.

People are the face, heart, and hands of a company. In people analytics, we analyze data to reveal actionable insights that provide evidence for decisions regarding employees, work, and business objectives. This talk will cover the use of data science for people analytics projects such as workforce planning, improving employee engagement, and retaining talent. Speaker bio: Elizabeth Esarove is a data scientist in People Analytics at AT&T. In her role, Elizabeth is part of a larger team focused on embedding data and analytics into the root of decision-making and transforming insights into actionable solutions that improve employee outcomes and drive business value. Timestamps: *Q&A timestamps listed further below 3:42 - Start of session 5:14 - What is People Analytics 6:26 - Opportunities for Data Science in People Analytics 7:10 - Using Predictive Models to Reduce Attrition 11:10 - Segmenting Your Population 18:55 - Communicating with Leaders 20:11 - Time Series Forecasting for Workforce Changes 24:41 - Analyzing Employee Survey Comments Helpful Resources Below: *more follow-up to come with a Q&A blog post in the works 😉 People Analytics Books Mentioned today: 📒 Handbook of Regression Modeling in People Analytics: with examples in R, Python and Julia by Keith McNulty https://lnkd.in/eBFgniFG 📒 Excellence in People Analytics: How to Use Workforce Data to Create Business Value by Jonathan Ferrar and David Green https://a.co/d/bJrMRuW People analytics books shared in a previous data science hangout: 📒 Predictive HR Analytics: Mastering the HR Metric: https://a.co/d/5Hx05mw 📒 Inclusalytics - How Diversity, Equity and Inclusion Leaders Use Data to Drive Their Work: https://lnkd.in/g48tdrMu Other links shared by Liz: Time Series Models 📒 Forecasting: Principles and Practice by Rob Hyndman and George Athanasopoulos https://otexts.com/fpp3/ 📒 Text Analytics Text Mining with R by Julia Silge & David Robinson https://lnkd.in/emawveZd Additional resources shared: 📒 R Gov Conference: https://lnkd.in/ePfN7jru (David Meza is presenting on the RStudio (Posit) Ecosystem as a Critical Part of NASA Analytics Capabilities) 📒 People analytics for getting to the moon | Data Science Hangout with David Meza, NASA: https://lnkd.in/eDirbgCF 📒 For LATAM and Spanish Speaking people, Sergio Garcia Mora shared the R4HR community which has developed lots of free access content: https://data-4hr.com/ 📒 John Kelly IV shared the Human Resources Science LinkedIn Group: https://lnkd.in/eEMpYAfk 📒 Adrian M. Pérez shared the People Analytics Handbook: https://lnkd.in/ecsWy-dA 📒 Data Science Hangout: pos.it/dsh 📒 All upcoming #Posit community events: pos.it/community-events Q&A Timestamps: *the following timestamps are approximate. 16:00 - What are the most important people analytics KPIs @ AT&T? Can you share how your team/HR acts on these predictions (for optimal policy) both experimentally and ethically? do you implement new policy in smaller groups? 23:00 - How have you validated the predictive models? Looking backwards, how precise were they? 25:00 - Do you work with your HRBPs to segment your population? 25:00 - What languages are you using to build your predictive models? 31:00 - Do you include demographic information (gender, race, age) in your models? 31:00 - Are your surveys anonymous? 32:00 - How would you get the ROI from HR attrition modeling? 34:00 - Are most data scientists from a Psychometrics background? 35:00 - Is there a kind of "critical mass" to apply People Analytics? (just for big companies?) 36:00 - Looking at positive / negative comments, do you quote verbatim comments in your reports? (e.g. "here is one of the very positive / very negative comments we received") 37:00 - Do you use something like Snowflake to store and model your data? And do you deploy these models automatically or manually update them? 38:00 - R user here. How do you balance between people-ops focused analytics tools from outside vendors (often very expensive, but helpful) with custom in-house analytics (often time-consuming)? 41:00 - How much of your work is driven by HR leadership, by HR business leaders, or by the HR analytics team pushing modeling and insights to those groups? 42:00 - What was your journey into learning data science and getting into people analytics? 44:00 - Do you have a role in education business units? to improve their questions, etc.? 45:00 - What is the HR tech stack at AT&T? Does your team have a data engineer solely for people data since they're more sensitive? 47:00 - How do you present your results? (an application, report, power point) and how important is it to learn other languages (javascript, css, sql)? If you were to start a people analytics team in a company (+1000), how do you start? 50:00 - Do you use an internal tool for surveys? Do you use thresholds to maintain anonymity? 53:00 - Does AT&T have remote workers? If so, does people analytics segment on remote vs hybrid vs on-site?