Libraries for Data Science -

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 Scientific Computing Libraries in Python:


Pandas: Provides data structures and tools for effective data cleaning, manipulation, and analysis. It includes a two-dimensional table called a Data Frame.

NumPy: Based on arrays and matrices, allowing mathematical functions to be applied to the arrays. Pandas is built on top of NumPy.

Visualization Libraries in Python:


Matplotlib: A well-known library for data visualization, popular for creating customizable graphs and plots.

Seaborn: A high-level visualization library based on Matplotlib, generating heat maps, time series, and violin plots.

High-Level Machine Learning and Deep Learning Libraries in Python:


Scikit-learn: Contains tools for statistical modeling, including regression, classification, and clustering. Built on NumPy, SciPy, and Matplotlib.

Keras: A high-level interface for building standard deep learning models, providing a quick and simple approach to model building.

Deep Learning Libraries in Python:


TensorFlow: A low-level framework used for large-scale production of deep learning models, designed for production and deployment.

PyTorch: Used for experimentation, allowing researchers to test ideas in a simple manner.

Libraries Used in Other Languages:


Apache Spark (Scala): A general-purpose cluster-computing framework for processing data in parallel using compute clusters. Supports Python, R, Scala, and SQL.

Complementary Scala Libraries to Apache Spark:


Vegas: A Scala library for statistical data visualizations, compatible with data files and Spark Data Frames.

R Libraries for Data Science:


ggplot2: A popular library for data visualization in R.

Libraries for interfacing with Keras and TensorFlow in R.

This video emphasizes the importance of these libraries in different aspects of data science, including scientific computing, visualization, machine learning, and deep learning across various programming languages.





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