(1/3) I am excited to share that my course - Data Pipeline Automation with GitHub Actions Using R and Python 🚀, is now available on LinkedIn Learning!
The course provides an introduction to setting up automation with GitHub Actions with both R and Python. Throughout the course, we will use a real-life example by working with the U.S. Energy Information Administration (EIA) API for data automation. 🧵👇🏼
Request for help from anyone with #rstats package development experience or knowledge of time data, especially if you've worked with .ical files before: checks failing in the {calendar} package preventing updated on CRAN and I'm not sure why 🤷 . Thanks to new contributors for reviving this package after ~5 years dev hiatus! Please spread the word @rOpenSci and anyone in this #foss for #DataScience (or at least dates) space! Details: https://github.com/ATFutures/calendar/issues/50
I think in PowerShell and can manage in Python. I want to learn Rust to the degree I can write in it directly, rather than prototyping in PowerShell and then converting.
A lot of what I do is data manipulation and analysis. (Take several CSV files as input, and output new CSV files that answer business questions based on the inputs.) I'm seriously impressed with Rust's performance here.
If you've made this transition, advice on where to begin?
:gt: @graph_tool is a comprehensive and efficient :python: Python library to work with networks, including structural, dynamical and statistical algorithms, as well as visualization.
It uses :cpp: C++ under the hood for the heavy lifting, making it quite fast.
(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.
Version 1.7.1 of the NeuralForecast #Python library was released last month by Nixtla. The NeuralForecast library, as the name implies, provides a neural network framework for time series forecasting. 🧵👇🏼
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
The MLX is Apple's framework for machine learning applications on Apple silicon. The MLX examples repository provides a set of examples for using the MLX framework. This includes examples of:
✅ Text models such as transformer, Llama, Mistral, and Phi-2 models
✅ Image models such as Stable Diffusion
✅ Audio and speech recognition with OpenAI's Whisper
✅ Support for some Hugging Face models
I recently concluded 2 urban #datascience projects that I dreamt of doing since a long time, with 2 super talented MSc students who visited us @nerdsitu last year from Germany, Carlson @cbueth and Henrik @supergrobi.
(1/2) Prompt Fuzzer - a new open-source project for LLM security 👇🏼
Prompt Fuzzer is a new open-source project that provides a set of functions for assessing the security of GenAI applications. This CLI-based tool enables you to run and test your system prompts to identify security vulnerabilities against potential dynamic LLM-based attacks.
I've ended up with an inquiry from a #DataScience student interested in examples of #harm coming to people because of #data collection (especially in a war context, but open to anything). Do folks have any favorite pointers / examples I could pass along?
(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.
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.
The End To End Data Science With R is a new book by Rene Essomba. The book, as the name implies, focuses on the core data science applications using R ❤️. This book covers the following topics:
✅ Exploratory data analysis
✅ Data visualization
✅ Supervised learning
✅ Unsupervised learning
✅ Time series
✅ Natural language processing
✅ Image classification
(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.
(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.
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
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.
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
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