The below course by Dhaval Patel is a beginner-level course for Deep Learning in Python with Tensorflow 2.0 and Kares. The course covers the foundations of neural network and deep learning, which includes the following topics: 🧵👇🏼
🎉 SatRDays London 2024 at King's College, Bush House! Featuring keynotes like Andrie de Vries & Nicola Rennie. A must-attend for those passionate about #RStats and #DataScience. From survival analysis to using R in education, there's something for everyone.
(1/2)Statistical Inference via Data Science - New Edition 📚👇🏼
The Statistical Inference via Data Science by Chester Ismay and Prof. Albert Y. Kim recently released a new edition. This book focuses on the data analysis workflow and how to answer questions with data. This includes the following topics:
✅ Data wrangling
✅ Data visualization
✅ Simple and multivariate regression analysis
✅ Sampling and bootstrap
✅ Hypothesis testing
Just spent 6 frustrating hours working a specific data visualizations. Then took a break, deleted my code, and nailed it in 20 mins. Moral of the story: Sometimes you NEED to step back and clear your mind. 💡#Programming#DataScience
Calling all data enthusiasts: ever heard of Orange (https://orangedatamining.com/)? Recently stumbled upon this tool for data mining and machine learning. It's Python-based and completely open-source. Sounds pretty good to me? Any users here?
What are some of the biggest threats to open-source data science today? Posit's Wes McKinney joins Ken Jee's Nearest Neighbors podcast, discussing that topic & more.
If you're curious about where open-source tooling for analytics is headed, and threats to keeping analytics best practice open, you should definitely have a listen.
(1/2) Statistics for Applications - MIT Course 🚀👇🏼
If you are looking for a good intro to statistics, I recommend checking MIT's Statistics for Applications course by Prof. Philippe Rigollet. The course is a hard-core intro to applied statistics ❤️ and provides an in-depth of the math and theoretical foundation of statistical methods.
Crazy about cherry blossoms in Seattle? Also crazy about maps?
Seattle geographer Nat Henry has mapped the data about trees in the genus Prunus (which include cherrys, plums, and other flowering and non-flowering trees) from five data sources and made this great map of public access trees.
(1/2) I created the second tutorial on the series of running RStudio inside a container 🚀. This tutorial focuses on formalizing the run command from the first tutorial with Docker Compose using the Rocker RStudio image 🐳 👇🏼
Setting and running RStudio inside a containerized environment is easier than it seems, thanks to the Rocker project.
The Lessons in Statistical Thinking is a new book by Prof. Daniel Kaplan focusing on statistical reasoning. The book covers the following topics:
✅ Handling data
✅ Describing relationships
✅ Randomness and noise
✅ Casual modeling
✅ Hypothetical thinking
Hello Fediverse! We're happy to be joining mstdn.science. Patterns is an #openaccess#datascience journal from @cellpress. We cover data science in the broadest possible sense, including all fields of computational and data-heavy research as well as associated topics in ethics, philosophy and science management. Check us out at https://www.cell.com/patterns/
🎉 R/Medicine Conference 2024 Announcement! 🎉 June 10-14 - Mark your calendars for an immersive experience in health data analysis with R. Featuring keynotes from Stephanie Hicks & Gundula Bosch, plus workshops, demos, and more. Abstract submissions are open! Join us to explore the frontier of medical research and R programming.
If I need to describe data science in one word, it would be optimization, and in two words, convex optimization. Convex optimization is the mathematical mechanizing beyond many data science algorithms, from least squares to neural network. The Convex Optimization course by Prof. Stephen Boyd (Stanford University) focuses on methods for identifying and solving convex optimization problems.