Data Science Projects | Top 10 Data Science githunb repositories of 2022
Every year, the data science landscape changes. With new tools and packages, it’s always interesting to see what people are using. Here is the list of ten most important and popular ones that made an impact this year.
Pandas
https://github.com/pandas-dev/pandas
Pandas is a popular data analysis library built on top of the Python programming language, and getting started with Pandas is an easy task.
Prefect
https://github.com/prefecthq/prefect
Prefect 2 has been designed from the ground up to handle the dynamic, scalable workloads that the modern data stack demands.
Dash
https://github.com/plotly/dash
Built on top of Plotly.js, React, and Flask, Dash ties modern UI elements like dropdowns, sliders, and graphs directly to your analytical Python code.
PyTorch
https://github.com/pytorch/pytorch
PyTorch is an open-source framework built for developing machine learning and deep learning models.
Tensorflow/Keras
https://github.com/keras-team/keras
Developed by Google, Keras is the high-level API of TensorFlow 2: an approachable, highly-productive interface for solving machine learning problems, with a focus on modern deep learning.
Hugging Face Transformers
https://github.com/huggingface/transf...
Hugging Face Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models.
Gradio
https://github.com/gradio-app/gradio
The Gradio python library was developed to create web demos from machine learning models.
Scikit-learn
https://github.com/scikit-learn/sciki...
Scikit-learn is a machine learning library for Python that will help you access just about any ML algorithm you may need for the project you’re working on.
Apache Superset
https://github.com/apache/superset
Superset is a must-try project for any ML engineer, data scientist, or data analyst.
BERT
https://github.com/google-research/bert
Proposed in a 2018 paper and credited with over 46,500 citations, you probably already know of BERT and its transformational role in revolutionizing NLP. BERT’s architecture allows it to understand bi-directional content that delivers state-of-the-art results on NER, language understanding, question answering, and several other general NLP tasks.
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