python-tidytuesday

PydyTuesday is a Python-focused extension of the weekly TidyTuesday data science challenge, providing resources and guidance for exploring TidyTuesday datasets using Python instead of R. It helps Python users participate in the Data Science Learning Community’s weekly challenges by offering tutorials, setup guides, and examples for data exploration, visualization, modeling, and dashboard creation.

The repository solves the problem of Python users wanting to participate in TidyTuesday but lacking Python-specific resources and examples. It includes step-by-step video tutorials covering environment setup, library installation, data import workflows (particularly with Positron IDE), and deployment options. The project bridges the gap between R-centric TidyTuesday and the Python data science community, making the weekly challenges accessible to a broader audience.

Resources featuring python-tidytuesday#

Four steps for managing data teams | Toby Hall | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Toby Hall, Executive Vice President and CIO at Delta Dental of Michigan, to chat about all kinds of things, including: data science team management, AI governance and the ethical use of data in healthcare, insurance data types and use cases, and hiring for traits vs. skills in data science. In this Hangout, among other topics, Toby mentions Delta Dental's "guilty until proven innocent" approach to AI. This involves a two-stage approval process that critically examines use cases for ethical implications, data handling (ensuring data stays within dedicated tenants and doesn't train public models), and tool appropriateness. The discussion also touches on the regulatory skepticism towards AI from departments of insurance and the broader environmental considerations of AI use. Toby also gives loads of career advice, but our favorite was his four-step process for managing a data team: - Put the right people in the right role - Give them the right vision of what you're trying to accomplish, ensuring it's clear and everyone is aligned on "what that is and why we're doing it" - Give them the right culture to get it done - Get out of their way! Resources mentioned in the video and zoom chat: Posit Conference → https://posit.co/conference/ Delta Dental Tooth Fairy Poll → https://www.deltadental.com/us/en/tooth-fairy/the-original-poll.html TidyTuesday Data Repo (get your reps in!) → https://github.com/rfordatascience/tidytuesday Resources for doing TidyTuesday stuff in Python (PydyTuesday?) → https://github.com/posit-dev/python-tidytuesday If you didn’t join live, one great discussion you missed from the zoom chat was about the Delta Dental Tooth Fairy data, including its outrageously expensive rates and the humorous debate around the inflation of self-reported flossing data. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! 00:00 Introduction 02:28 "Tell us a little bit about your day to day, but also the data teams at Delta Dental of Michigan, what is the type of data that they work with, and what is it that they solve with it, or what decisions are made based on it?" 06:04 "What about third party data?" 07:57 "How do you all decide which language to use and for what applications?" 10:37 "How do you make sure your AI pipeline is accurate in the SQL and the regulation because you are working with health health care data?" 23:22 "What are some tips for leaning into those challenge assignments as the IC, the individual contributor?" 23:31 "How do you identify the challenge assignments? Like, how do you know this is the right thing with the stakes that are right to give to somebody?" 25:54 "Do you share your data as an API or for research?" 36:45 "Coming from a core statistical background, how much would you expect from a fresh data science graduate about statistical knowledge if you were an interviewer for a modeling role? Any specific concepts that you might ask them about in the interview?" 38:17 "How do you make sure your team stays current with new data science tools or skills or methods?" 40:18 "Did you have a ranking of necessary roles that you wanted to hire first, like data engineer versus data scientist or analyst or maybe dedicated reporting staff? And what tools or platforms did you consider essential from day one?" 43:53 "How much of your background in teaching high school students has spilled over into managing your team?" 46:21 "Do you have any advice for how someone could maybe emphasize those traits on their resume?" 49:19 "Do you have a piece of career advice that has either really, really helped you or that you try to give to everybody?"

Open Source in Pharma | Harvey Lieberman | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Harvey Lieberman, Associate Director of Data Science at Novartis, to chat about R/Pharma, automating processes, career advice, and data science in drug discovery vs. development. In this Hangout, Harvey talks about a lot of things, like the power of automating processes. He shares examples of how automating mundane tasks can save significant time and identify errors that humans might miss (we all know human error is a thing!). For instance, he automated the analysis of data from 48 Excel sheets that had previously taken a colleague about three months to process by hand; Harvey completed the automated analysis in one hour over lunch and found copying and pasting errors in the original manual process! Automating processes not only increases efficiency but can also help move people into more data-focused roles. Harvey suggests demonstrating that automation speeds things up and, most importantly, removes errors, which is when people start to pay attention and get interested. Resources mentioned in the video and zoom chat: R/Pharma website → https://rinpharma.com/ Cecilia Baldoni's scrollytelling project (on shrews!) → https://cecibaldoni.github.io/projects.html Advent of Code → https://adventofcode.com/ Pharmaverse.org (pharmaceutical R packages) → https://pharmaverse.org GSK's Journey to R → https://www.youtube.com/watch?v=xDrt6txplek Roche's Journey to R → https://www.youtube.com/watch?v=BlJNILSoZlM R/Pharma March 2025 newsletter (LinkedIn) → https://www.linkedin.com/pulse/rpharma-march-2025-newsletter-open-source-in-pharma-wmf5c/ ggplot2 extenders club → https://ggplot2-extenders.github.io/ggplot-extension-club/ Coursera: Making Data Science Work for Clinical Reporting Course → https://www.coursera.org/learn/making-data-science-work-for-clinical-reporting hiring.cafe (for finding R jobs) → https://hiring.cafe/ Posit's PydyTuesday GitHub → https://github.com/posit-dev/python-tidytuesday Joy's Law (management concept) Wikipedia → https://en.wikipedia.org/wiki/Joy%27s_law_(management) If you didn’t join live, one great discussion you missed from the zoom chat was about the diverse backgrounds of attendees. Many participants shared that they came to data science "sideways," holding degrees in fields such as sociology, psychology, mathematics, atmospheric science, education, history, chemistry, and various engineering disciplines, rather than traditional statistics or computational degrees. so many data scientists have non-traditional paths into the field! But we're all better together. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

People Analytics at Pinterest | Trevor Fry | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We'd love to see you! We were recently joined by Trevor Fry, Lead Data Analyst at Pinterest, to chat about people analytics, bridging the data science practice gap, the future of the field including AI, and measuring the value of data teams. We also celebrated a birthday In this Hangout, we explore bridging the data science practice gap—the challenge of effectively translating research findings and data insights back to business leaders and stakeholders. Trevor discusses how the nature of people data is particularly sensitive, requiring careful handling, and emphasizes the importance of adapting your communication and language to your specific audience. He also shares insights into the future of people analytics, especially the potential of AI and LLMs for analyzing unstructured data like survey comments, while noting the challenges of ensuring reliability for scientific applications. Trevor also touched upon the difficulty of measuring the return on investment (ROI) for people analytics functions and discussed common analytical methods used in the field, such as Structural Equation Modeling (SEM) and relative weight analysis (RWA). Resources mentioned in the video and zoom chat: Storytelling with Data by Cole Nussbaumer Knaflic → https://www.storytellingwithdata.com/ Society of Industrial Organizational Psychology (SIOP) → https://www.siop.org/events/the-annual-conference/ Relative Weight Analysis (RWA) R package → https://martinctc.github.io/rwa/ People Analytics: Regression Modeling by Keith McNulty → https://peopleanalytics-regression-book.org/ Structural Equation Modeling (SEM) info → https://stats.oarc.ucla.edu/r/seminars/rsem/ Ollama (local LLMs) → https://ollama.com/ Posit blog: Secure AI-Assisted data science in R with Posit and Snowflake → https://posit.co/blog/ai-assisted-ai-posit-snowflake-cortex/ TidyTuesday GitHub repo → https://github.com/rfordatascience/tidytuesday TidyTuesday in Python GitHub repo → https://github.com/posit-dev/python-tidytuesday Forecasting: Principles and Practice (Python version) → https://otexts.com/fpp3/ (R version also mentioned) Causal Inference: The Mixtape by Scott Cunningham → https://mixtape.scunning.com/ Text Analysis using Quanteda (R package) YouTube video → https://www.youtube.com/watch?v=tf5FmXiwEQE If you didn’t join live, one great discussion you missed from the zoom chat was about the definition of a "data scientist" and whether individuals feel like they are "really" data scientists, highlighting the varied backgrounds and nonlinear paths in the field. If you have any imposter syndrome around data work, you're not alone! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!