Mastering Data Science: Top 10 GitHub Repos You Need to Know

This page summarizes the projects mentioned and recommended in the original post on

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  • scikit-learn

    scikit-learn: machine learning in Python

    1. Scikit-learn Scikit-learn is a must-know Python library for any data scientist. It offers a wide range of machine learning algorithms, data preprocessing tools, and model evaluation metrics that are easy to use and highly efficient. Whether you’re working on regression, classification, or clustering tasks, Scikit-learn has got you covered.

  • tensorflow

    An Open Source Machine Learning Framework for Everyone

    2. TensorFlow Developed by the Google Brain team, TensorFlow is a powerful open-source machine learning framework that’s perfect for deep learning and neural network projects. With TensorFlow, you can build and train complex models using an intuitive and flexible API, making it an essential tool for any data scientist looking to delve into deep learning.

  • InfluxDB

    Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.

  • Keras

    Deep Learning for humans

    3. Keras Keras is a high-level neural networks API written in Python that’s built on top of TensorFlow. It’s designed to enable fast experimentation with deep learning, allowing you to build and train models with just a few lines of code. If you’re new to deep learning or just want a more user-friendly interface, Keras is the way to go.

  • Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

    4. Pandas When it comes to data manipulation and analysis, Pandas is an absolute must-have. This powerful Python library provides data structures like DataFrames and Series, along with a host of functions for cleaning, transforming, and visualizing your data. With Pandas, wrangling data has never been easier.

  • NumPy

    The fundamental package for scientific computing with Python.

    5. Numpy Another essential tool in a data scientist’s toolkit is Numpy, a fundamental package for scientific computing with Python. Numpy provides support for large, multi-dimensional arrays and matrices, as well as various mathematical functions to perform operations on your data.

  • jupyter

    Jupyter metapackage for installation, docs and chat

    6. Jupyter Jupyter is a collection of tools and applications designed for interactive computing and data visualization. At the heart of the Jupyter ecosystem is the Jupyter Notebook, an interactive web-based platform that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It’s an excellent tool for exploratory data analysis, model prototyping, and creating reproducible data science workflows.

  • PythonDataScienceHandbook

    Python Data Science Handbook: full text in Jupyter Notebooks

    7. Data Science Handbook Are you looking for a comprehensive guide to data science with Python? Look no further than the Data Science Handbook by Jake VanderPlas. This repository contains the entire book, which introduces essential tools and techniques used in data science, including IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn. It’s a fantastic resource for anyone looking to deepen their understanding of data science concepts and best practices.

  • Mergify

    Updating dependencies is time-consuming.. Solutions like Dependabot or Renovate update but don't merge dependencies. You need to do it manually while it could be fully automated! Add a Merge Queue to your workflow and stop caring about PR management & merging. Try Mergify for free.

  • seaborn

    Statistical data visualization in Python

    8. Seaborn Data visualization is a crucial aspect of data science, and Seaborn is an excellent library to help you create beautiful and informative plots. Built on top of Matplotlib, Seaborn provides a high-level interface for creating statistical graphics that are both visually appealing and easy to understand.

  • awesome-datascience

    :memo: An awesome Data Science repository to learn and apply for real world problems.

    9. Awesome Data Science If you’re on the hunt for data science resources, Awesome Data Science is a goldmine. This curated list includes MOOCs, books, courses, blogs, podcasts, software, and more, all related to data science.

  • awesome-deep-learning-papers

    The most cited deep learning papers

    10. Deep Learning Papers Last but not least, Deep Learning Papers is a must-visit repository for anyone interested in deep learning research. This curated list features the most influential and important deep learning papers, organized by topic and publication date.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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