Data Elixir - Data Elixir - Issue 404
ISSUE 404 · September 13, 2022InsightOrganizations need to deliberately create dataPeople sometimes say that "data is the new oil," but that line of thinking confines models to the data that's available for extraction. A better approach is to figure out what data you need and then figure out how to create it. This is a great post that explores the limitations of extracted data and how teams gain by deliberately creating the data they need. Takeaways from Gartner Data & Analytics SummitNice overview of highlights and four big ideas from the recent Gartner Data & Analytics Summit. Sponsored LinkRegister: TechCrunch x iMerit ML DataOps SummitJoin 2,000+ data scientists, engineers, and ML Professionals virtually at the iMerit ML DataOps Summit to hear from leaders at the forefront of deploying ML DataOps solutions that power machine learning and artificial intelligence. Register for free. Tutorials, Projects & Opinions5 questions to categorize machine learning interpretability approachesAfter reading hundreds of papers and writing a book on machine learning interpretation, Christoph Molnar has identified some useful categories of interpretation techniques. In this post, he organizes his thinking into five simple questions that will help you assess the ML interpretation approaches that are suitable for different use-cases. Getting Started with Apache Arrow in RNice collection of R resources, cheatsheets, and a tutorial for using Apache Arrow to work with data that's larger than memory. This is aimed at experienced R users who are new to Arrow. Want a data science project?This is the best take I've seen on the recently released treasure trove of hospital pricing data. Over 100TB of data was released and by all accounts, it's a mess. In this post, Randy Au explores what's available, what needs to happen next and how, ultimately, the lack of tools and structure has more to do with real-world data handling issues than maliciousness. There are important problems and opportunities here. Djinn by Tonic.ai - AI-driven synthetic data modelsWhether it's privacy controls or a lack of high quality data slowing you down, Djinn's AI-driven synthetic data models create private and augmented data within minutes of setup. Answer nuanced scientific questions, optimize business processes, and make better decisions. Code & ToolsPySearch: Python Function Search by DescriptionPySearch is a free search engine for querying python libraries using natural language descriptions. Just select the libraries you want to search and then use natural language or keywords to describe what you're looking for. Check out the examples to see it in action. Data VisualizationMapping wind data with RGreat R tutorial that shows how to access, reshape and visualize wind data as streamlines. This is a step-by-step tutorial that includes code and links to key resources along the way. Which fonts to use for your charts and tablesSans-serif or serif typefaces? Lining or oldstyle figures? Narrow or wide? With lots of examples, this post explains which fonts work best for various types of data visualizations. CareerThe Difficult Life of the Data LeadAs data teams get bigger, more Data Leads are needed but Data Leads have one of the hardest roles in data. They have to manage a team, work with stakeholders and still stay hands-on. In this post, Mikkel Dengsøe explores the challenges and ideas for making the role better. Join the Data Elixir Talent CollectiveThe Data Elixir Talent Collective is a reverse job board where top companies apply to you. Choose to be anonymous or public and get matched with opportunities that fit your specific interests. This is a free resource but membership is limited. To apply, you need 3+ years experience in data science, analytics, machine learning, visualization, or a related field. For more info, APPLY HERE. If you’re hiring, apply now to find top candidates faster, sourced from the Data Elixir community. We're creating the highest signal-to-noise hiring resource for roles in the data ecosystem. Already, there are more than 100 mid to senior level candidates from a wide variety of organizations; from fast moving startups to big companies, like Google, Amazon, Apple, NVIDIA and more. If you're hiring, APPLY HERE. Was this email forwarded to you? Sign up here >> |
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Data Elixir - Issue 403
Tuesday, September 6, 2022
Semantic layers: a deep dive. Communicating A/B test results. Using an attribution framework. Bayesian age-period-cohort models in Python.
Data Elixir - Issue 402
Tuesday, August 30, 2022
Modeling and analytics for ⚽. Practical causal forecasting. Intro to data contracts. Expressive analytics w/ Python.
Data Elixir - Issue 401
Tuesday, August 23, 2022
Homegrown auth w/ ML. Intro to backprop. Key-value DBs. GPT-3 for science. Data product canvas. R-spatial ecosystem.
Data Elixir - Issue 400
Tuesday, August 16, 2022
Deep dive into SVD. Smart paywalls. Idea to funding. Bayesian inference at scale. Logistic regression explainer.
Data Elixir - Issue 399
Tuesday, August 9, 2022
The 8 slide resume. Intro to streaming for data scientists. Random Forest explainer.
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