Data Science

doddle-model is an in-memory machine learning library for Scala, built on Breeze with a scikit-learn-like API using typeclasses.

PyTorch Lightning supports pretraining and fine tuning AI models across GPU setups.

mlxtend adds helper modules for Python data analysis and machine learning libraries.

Machine learning pipelines can use whylogs, an open-source library for logging data quality and model performance.

mljar-supervised provides tabular data AutoML with feature engineering, hyperparameter tuning, explanations, and documentation.

Python developers can use gensim for topic modeling and related natural language processing tasks.

For Python data preparation, DataPrep offers an open-source low-code library to collect, clean, and visualize data.

Azure for Students provides cloud services, developer tools, and credits for building apps and exploring AI.

This repository curates references and resources for machine learning operations.

Radicalbit AI Monitoring provides a solution for monitoring machine learning models in production.

The first edition of R for Data Science teaches data import, transformation, visualization, and modeling with R.

PandasAI lets users query databases and data lakes conversationally with LLMs and retrieval augmented generation.

Data Science 101 is Ryan Swanstrom’s blog about learning to become a data scientist.

Featureform turns existing data infrastructure into a virtual feature store.

PyCM provides a Python library for working with multiclass confusion matrices.

Great Learning offers a free introductory data science course covering foundational analytical skills, with a completion certificate.

Butterfree provides tools for building feature stores and transforming raw data.

Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.

DataComPy compares Pandas, Polars, Spark, and Snowpark dataframes, with output that includes comparison statistics.

DataDeps.jl provides reproducible data setup for scientific work, with a Julia package for fetching and using data dependencies.

DataTalks.Club brings data science, machine learning, and AI practitioners together, with courses, podcasts, webinars, and workshops.

The Journal of Data Science publishes research on statistical methods and their applications.

Koalas implements the pandas DataFrame API on Apache Spark and is deprecated; for Apache Spark 3.2 and above, use PySpark directly.

GLM.jl provides generalized linear models in Julia, with documentation and examples for the package.