I was looking for examples of interactive data visualization for a gradient descent algorithm, and I found this app by Lili Jiang. This desktop app is based on C++ and enables simulation and visualization of different gradient descent algorithms, such as momentum, AdaGrad, RMSProp, and Adam. The app enables to compare different methods simultaneously.
James Baker on Bluesky: 'From 2025/26 University of Southampton DH will be running a new MSc in Digital Humanities (Data Science). Huge thanks to all my colleagues who've helped turn our little idea into a reality, especially the amazing
Lexi Webster https://www.southampton.ac.uk/courses/digital-humanities-data-science-masters-msc
And look out for a Humanities Data Science Lectureship we'll be advertising in the Autumn to lead this programme (ask me if you are interested in knowing more before the ad comes out).'
📦 Generalizing OOP in R core @R_Foundation
🏨 Visualizing overture map buildings data @kyle_e_walker
📝 Refactoring a test file (part 2) @maelle
You'll see awesome new features in our show like custom chapter images and boosting directly to your hosts with a modern podcast app available at https://newpodcastapps.com/
📊 Enhance Your Excel Skills with R! 📊 I wrote an R function using RDCOMClient to count sheets in an Excel workbook.
This tool automates Excel tasks, boosting productivity. Learn more techniques like this in my new book co-authored with David Kun: "Extending Excel with Python and R." Discover practical tips to enhance your data analysis skills. Get your copy here: https://packt.link/oTyZJ
Open your calendar, NumPy 2.0 is going to be out on June 16th 🚀
This is the first major release since 2006. The release includes breaking changes in the library API, and therefore, if you are planing to adopt it, some code refactoring may required.
The release includes new features, performance improvement 🏎️, improvements on the C API, and more.
(1/2) Shiny Apps for demystifying statistical models and methods 🚀
This is a cool website that explains different statistical concepts with the use of interactive Shiny Apps. Ben Prytherch made this website from the Department of Statistics at Colorado State University.
Some of the folks who signed up for my #StickersAndStamps project finally got an email from me, thanking them for letting me know when their letters arrived.
Most of them arrived on, or near, the the day I had my surgery.
My recovery is still going well, and I finally had the mental and physical energy for the correspondence.
I'll be returning to working on the data visualization and art of the project soon!
Building robust data pipelines with dbt, Airflow, and Great Expectations 🚀
I started to dive into great expectations - a Python library for data quality checks, and I found this great talk by Sam Bail about building data pipelines with dbt, Airflow, and great expectations.
The Machine Learning for Beginners by Microsoft Developer is an introductory course for classical machine learning. This crash course mainly focuses on regression analysis with Python 🐍, and it covers topics such as:
✅ General setup
✅ Cleaning data
✅ Data visualization
✅ Regression models
✅ Polynomial regression
✅ Logistic regression
Here is a short e-book with a sequence of tutorials on the scientific Python ecosystem for beginners. This includes topics such as:
✅ Working with numerical data using NumPy
✅ Data visualization with Matplotlib
✅ Scientific computing with SciPy
✅ Statistics with Python
✅ Machine learning with scikit-learn
(1/4) TIL about the plotnine library- the grammar of graphics in Python 🚀
I had never heard about the Plotnine library until I came across the Posit Plotnine contest (see the link below). The plotnine is a Python implementation of a grammar of graphics based on the ggplot2 library.
Learn how to handle rows in R containing specific strings using base R's grep() and dplyr's filter() with str_detect(). Select or drop rows efficiently and enhance your data manipulation skills. Give it a try with your datasets for better data cleaning and organization.
College Precalculus – Full Course with Python Code by Ed Pratowski and freeCodeCamp focus on the foundation of calculus with Python implementation. This 12 hours course covers the following topics:
✅ Core trigonometry
✅ Matrix operation
✅ Working with complex numbers
✅ Probability