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Cheatsheets#

Quick reference guides for your favorite data science tools and packages.

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Videos#

Watch demos, tutorials, and talks from our team and community.

Astral (creators of uv) joins OpenAI

Wondering why Astral joined OpenAI? We recently hosted Charlie Marsh from Astral on The Test Set, where he discussed how AI agents were already reshaping Astral’s roadmap. Let’s hear where his head was at before the news broke. #python #pythonprogramming #ai #llm #astral #uv #openai #chatgpt

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education RStats Open Source Oss Reticulate

Regression models still rule in P&C Insurance | Jim Weiss | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guest was Jim Weiss, Chief Risk Officer, commercial and executive at Crum and Forster! Some topics covered in this week’s Hangout were the use of regression modeling (have you ever heard of the Tweedie distribution?) in property and casualty insurance, handling changing model results and communicating them to business stakeholders, using GenAI and “co-opetition” to identify and prevent fraudulent claims, and identifying and managing bias or confounding effects in pricing models. Resources mentioned in the video and chat: The Once and Future C&F → https://www.cfins.com/the-once-and-future-cf-landing/ Tweedie distribution → https://en.wikipedia.org/wiki/Tweedie_distribution Statistical Rethinking Lectures Playlist → https://www.youtube.com/playlist?list=PLDcUM9US4XdNOlqSyhe38US8mFgmqzI14 Considerations for Managing Potential Bias in Pricing Models → https://eforum.casact.org/article/91188-considerations-for-managing-potential-bias-in-pricing-models Pointblank for data validation (Python) → https://posit-dev.github.io/pointblank/ Pointblank (R) → https://rstudio.github.io/pointblank/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 02:16 “Who is Crum and Forster? What do you do? What types of problems does your team help them solve?” 04:14 “What kind of data types would you see in your day to day or your team would see in your day to day? And what is an example of a problem that you feel like you’ve solved lately or that you’ve been working on lately?” 10:14 “What types of regression do you use?” 13:35 “In your insurance modeling career, what is the most unusual or unexpected variable that has contributed to one of your models?” 18:48 “How do you handle it when you see that the results are significantly different across models?” 21:45 “Are you Team Bayesian or Team Frequentist when it comes to your statistics?” 27:13 “My health care organization is interested in identifying fraudulent claims. Currently, they’re looking at Excel spreadsheets, same time, different person. Do you have any advice on a better way to guide them to automation?” 30:51 “What software do you use to do your job?” 35:40 “Do you ever use instrumental variables in your models?” 47:47 “Do you have any career advice for us? Is there something you wish that you had known when you were first entering the industry?” 50:38 “How much do you and your team use AI to help you along?” 52:56 “How does your team span expertise in such varied fields?”

How to use Positron’s GitHub integration | Isabella Velásquez & Libby Heeren | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren and Isabella Velasquez walk through collaborating in Positron using the GitHub integrations and extensions. They show starting a project, protecting the main branch, opening and closing issues, creating and deleting branches, stashing and popping changes, resolving merge conflicts, opening and reviewing pull requests, and more. Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Isabel Zimmerman Isabella’s Bluesky: https://bsky.app/profile/ivelasq3.bsky.social Libby’s Bluesky: https://bsky.app/profile/libbyheeren.bsky.social Isabella’s LinkedIn: https://www.linkedin.com/in/ivelasq/ Libby’s LinkedIn: https://www.linkedin.com/in/libbyheeren/ Resources mentioned in the video: Positron → https://positron.posit.co/ VS Code Source Control → https://code.visualstudio.com/docs/sourcecontrol/overview Positron “create issues” triggers (add your own, too!) → positron://settings/githubIssues.createIssueTriggers GitHub → https://github.com/ Linking a GitHub issue to a pull request → https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue GitHub commands for PRs → https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue Git → https://git-scm.com/ Quarto → https://quarto.org/ GitHub Pull Requests and Issues Extension → https://marketplace.visualstudio.com/items?itemName=GitHub.vscode-pull-request-github Azure DevOps → https://azure.microsoft.com/en-us/products/devops/ Happy Git with R → https://happygitwithr.com/ Software Carpentry Git → https://swcarpentry.github.io/git-novice/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps of Questions / Topics 00:00 Introduction 02:56 Creating a Quarto book project in Positron 05:54 Initializing a Git repository and publishing to GitHub 06:49 Creating a.gitignore file 08:00 “Do you have to create a GitHub account before starting this kind of project?” 11:53 Staging changes and making the initial commit 13:34 Adding a collaborator on GitHub 14:25 Setting up branch protection rules 15:25 “Was that push to GitHub the initialization of the GitHub repo?” “What if you already had a GitHub repo with that same name, would you get a warning in positron?” 18:50 Working with pull requests and issues directly in Positron 20:18 Cloning an existing repository 24:09 Undoing your last commit 24:41 Stashing staged changes 25:12 Creating a new branch 26:03 Applying changes by popping a stash 27:03 Creating a pull request 28:37 How to put your activity bar in Positron to the top 29:40 Reviewing and merging a pull request 32:46 Deleting local and remote branches 36:08 “Are there benefits to doing things within positron vs the GitHub UI?” 39:13 “Do you know how to delete a branch if it’s an older rogue branch?” 41:40 Using keywords to automatically create GitHub issues 43:24 “Is the functionality similar if you’re working with an internal organization like Azure DevOps version of Git?” 44:12 “What is the best way to get an orientation into the world of Git and GitHub?” 46:42 Pulling and synchronizing branches 49:01 How to resolve merge conflicts

Isabel Zimmerman, Isabella Velásquez

Coding vs. thinking programmatically | Samia Baig | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guest was Samia Baig, Senior Data Scientist/Data Engineer at Johnson & Johnson Innovative Medicine! Some topics covered in this week’s Hangout were transitioning from a background in pharmacy and public health to a data career in pharma, distinguishing the responsibilities of data scientists versus analytics engineers, strategies for making data pipelines more robust (and convincing your team that you NEED robust pipelines in the first place), and the value of joining open-source communities like Tidy Tuesday. Resources mentioned in the video and chat: Posit Data Science Lab → https://pos.it/dslab Tidy Tuesday GitHub Repository → https://github.com/rfordatascience/tidytuesday {dbplyr} → https://dbplyr.tidyverse.org/ The Missing Semester of Your CS Education → https://missing.csail.mit.edu/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 03:22 “Do you feel like analytics engineer is a good descriptor for what you do?” 04:44 “How did you get into data from being on a pharmacist’s job path?” 11:55 “What was it like in the move that you made from public health to pharma?” 16:57 “What do you say are distinguishing factors between data science and engineering?” 20:16 “What are the most popular tools that you and your team use, in your job at J&J?” 24:00 “What do you use SQL in?” 27:40 “How would you go about convincing a team of the need for a more robust pipeline?” 31:10 “Can you define robust?” 33:31 “Do you happen to have any specific resources or strategies or examples that might help students or others with that mindset of thinking programmatically?” 37:06 “Are there any non data science skills that are very helpful in your either current or former job?” 40:23 “Is there any kind of community among data scientists across the whole company?” 45:44 “What are your biggest data challenges that you have?” 46:12 “If you had a magic wand, what problem would you solve in that area?” 49:52 “What is a piece of career advice that maybe you wish you could go back in time and give yourself?”

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Cheatsheets#

Create models with parsnip

AI chatbots with shinychat