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