Data Elixir - Issue 440

NFL Analytics. Sequential testing. Data + Music. Managing generative AI risks. FinGPT: open-source LLM for finance. Data exploration toolkit. ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌ ‌

ISSUE 440 · June 13, 2023   Trends Managing the Risks of Generative AI Generative AI has become wildly popular but it can have unintended consequences and potentially cause real harm. Here's how to identify and mitigate the risks for your organization. Harvard Business Review | Kathy Baxter and Yoav Schlesinger   Sponsored Link Step-by-step guide to find your data ingestion tool Confused about finding the right data ingestion tool for your team? Download the free 5-day step-by-step guide to find your perfect data ingestion tool.       Reach Data Elixir readers by sponsoring an issue. Click here for details.   Posts & Tutorials Data Jockey Data Jockey is a passion project that applies a wide variety of data tools and methodologies to music collections. The goal is to do things like match similar songs, build a recommendation engine, create playlists from thousands of songs, derive clusters, etc. There's a lot here, organized as a collection of jupyter notebooks. GitHub | GeorgeMcIntire   Neural Networks In this latest post from the popular MLU-Explain series, Jared Wilber explores the fundamentals of feed-forward neural networks. This is an interactive explainer, starting with basic building blocks. MLU-Explain | Jared Wilber   Fitting many statistical models at once using dplyr A common task in applied statistics is to fit and interpret a number of statistical models at once. For instance, suppose you're using the Palmer Penguins data and you want to compare bill length, bill depth, flipper length, and body mass between species. You could manually run a separate model for each, but here’s an easier way that automates the process using R and produces plots and a nice table of results. Cameron Patrick   Sequential Testing at Booking.com Group Sequential Testing allows for intermediate opportunities to judge the outcome of A/B tests and act accordingly. That means decisions can be made faster and, as Nils Skotara explains in this post, it can also significantly increase the quality of those decisions. Booking Data Science | Nils Skotara   TEMPLATE | Data planning with your business teams Stakeholders should be your partners in building, testing, and iterating on metrics. Record how metrics like leads, users, and customers are defined, and use it to document how other North Star metrics, like ARR, are being tracked. Get the fillable template here. // sponsored   Tools & Code Weave - Interactive Data Exploration Toolkit Weave is an interactive toolkit for ML and data app development. Start with a table of data and create beautiful and performant interactive dashboards, easily switching between code and UI along the way. Get started with Weave open source with just two lines of code. Weights & Biases   Changes to popular R packages for spatial data Three popular R packages for handling spatial data won't be available after October. Whether you're a user or a developer, if you use these packages, your workflows will break. Here's what you need to know, including solutions and resources. geocompx | Jakub Nowosad   Resources An Introduction to 🏈 NFL Analytics with R This new book by Brad Congelio shows how to do analytics with NFL data. It uses R but if you're not an R user, there's a section that covers what you need for this book. Covers NFL data access, data wrangling, NFL analytics, visualization and advanced modeling. Free to read online. Brad Congelio   Papers FinGPT: Open-Source Financial Large Language Models This paper introduces FinGPT, an open-source large language model for the finance sector. FinGPT takes a data-centric approach to help researchers and practitioners develop FinLLMs that are much more practical than proprietary models like BloombergGPT. arXiv | Hongyang Yang, Xiao-Yang Liu, Christina Dan Wang     How did you like this issue of Data Elixir? 👎    1     2     3     4     5   👍       Was this email forwarded to you?  Sign up here >>     Data Elixir, LLC P.O. Box 21255 Boulder, CO 80308 Data Elixir® is curated and maintained by Lon Riesberg. If you have questions or suggestions, just reply back to this email. Unsubscribe