(1/2) Hands-On Mathematical Optimization with Python 🚀
The Hands-On Mathematical Optimization with Python book by Krzysztof Postek, Alessandro Zocca, Joaquim Gromicho, and Jeffrey Kantor provides the foundation for mathematical optimization. As the name implies, the book is hands-on with Python examples, mainly using Pyomo.
Data Wrangler is a new Microsoft VScode extension for data exploratory analysis. It supports Python 🐍 and Pandas 🐼 DataFrame objects and is integrated into VScode Jupyter Notebooks. Here are some of the functionalities of Data Wrangler:
✅ Data review
✅ Column filtering
✅ Summary statistics
✅ Data cleaning and transformation
✅ Hadeling missing values
✅ Creating new fields
Last December we published a paper in the "Datasets and Benchmarks" track at NeurIPS 2023, detailing some of our ideas of how @renku could used for a more sustainable practice around data sets in data science, machine learning and beyond. It was quite well received, earning a "spotlight" acceptance! 🎉 More details here: https://blog.renkulab.io/neurips-2023
Join #PyData#Pittsburgh for a casual gathering of the local, national, and international PyData community on the sidelines of #PyCon US 2024! Meet up with fellow #DataScience, #MachineLearning, and scientific computing enthusiasts when the world's largest Python conference comes to town.
MIT launched the 2024 edition of the Introduction to Deep Learning course by Prof. Alexander Amini and Prof.Ava Amini. The course started at the end of April and will run until June. The course lectures are published weekly. The course syllabus keeps changing from year to year, reflecting the rapid changes in this field.
(1/2) I recently posted a few posts about Rust 🦀 and my intention to leverage it for data science applications. Multiple people asked if Rust is a substitute for R or Python, and the short answer (in my opinion) is no. I see Rust as a complementary or supporting language that could make languages like R and Python faster.
Polaris 🐻❄️ is one example of a Python 🐍 application that uses Rust on the backend. 🧵👇🏼
For all that #dataScience seems like a specialized corner of academia, I end up teaching on a wide swath from #qualitative coding and #groundedTheory to high-performance computing. Yesterday it was #dataHygiene in #Excel - today the pendulum swings and I'm teaching #webscraping with #Python. At least it keeps my #adhd from turning into boredom though.
Stanford University released a new course last week focusing on Deep Generative Models. The course, by Prof. Stefano Ermon, focuses on the models beyond GenAI models.
Want to check duplicate values across columns of a data.frame? Well you can do that in a basic way with TidyDensity and the check_duplicate_rows() function, or you can go through todays blog post for some other ideas with #BaseR#dplyr and #datatable
(1/2) Google released a new foundation model for time series forecasting 🚀
The TimeFM (Time Series Foundation Model) is a foundation model for time series forecasting applications. This pre-trained model was developed by the Google Research team. It joins the recent trend of leveraging foundation models for time series forecasting, which includes Salesforce's Moirai and Amazon's Chronos.
Join BetaNYC and the MTA Open Data team on June 5th at 9:30am to explore recently published MTA operating budget datasets. We’ll learn about the MTA’s open data program and conduct insightful analyses with their data.
(1/2) I have been following the work of @stevensanderson and David Kum for a few years now, and I am excited to see the release of their new book 🥳- Extending Excel with Python and R 🚀.
The book focuses on the common conjunction and collaboration between data scientists and Excel users. This includes scaling and automating #Excel tasks with #RStats and #Python and core data science applications such as data wrangling, working with APIs, data visualization, and modeling.