Data science has become an indispensable part of modern businesses and research. To effectively analyze and make sense of large datasets, data scientists rely on a wide range of libraries and tools. In this post, we have provided a brief overview of 24 popular data science libraries and tools that are commonly used for data analysis, machine learning, natural language processing, visualization, and more.

Pandas and NumPy are two essential libraries for data manipulation and numerical computing in Python. For dealing with big data, Dask and PySpark are commonly used for distributed computing and processing. XGBoost and LightGBM are popular libraries for building accurate and efficient machine-learning models. TensorFlow Probability and Hugging Face Transformers are widely used for probabilistic programming and natural language processing tasks. Gensim and NLTK are popular libraries for topic modelling and text analysis.

When it comes to data visualization, Matplotlib, Seaborn, Bokeh, and Plotly provide a range of tools for creating static and interactive visualizations. Overall, these libraries and tools provide data scientists with the necessary tools to extract insights and make informed decisions from complex data.