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
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
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
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
github.com/pytorch/pytorch

PyTorch is an open-source framework built for developing machine learning and deep learning models.

Tensorflow/Keras
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
github.com/huggingface/transformers

Hugging Face Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models.

Gradio
github.com/gradio-app/gradio

The Gradio python library was developed to create web demos from machine learning models.

Scikit-learn
github.com/scikit-learn/scikit-learn

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
github.com/apache/superset

Superset is a must-try project for any ML engineer, data scientist, or data analyst.

BERT
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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