# Data Science Libraries - Python

Python is an accepted language for**Data Science**, and great for general-purpose programming language as well. Python has quickly become a dominant programming language for Data Science applications and

**Machine Learning**. Because Python is simple and multifaceted and has easy readability, it is an obvious language of choice in the field of

**Data Science**.

The focus of this course is to provide in depth details of some of these

**Data Science Libraries**along with some solid examples of how these libraries can be used in several

**data science applications**.

## Python computing libraries

- NumPy (Arrays & matrices)
- Panda (Data Structures & Tools)
- SciPy (Integrals, solving differential equations, optimization)

## Python Visualization Libraries

- Matplotlib (Plots and Graphsr)
- Seaborn(Plots: heatmaps, time series, violin plots)

## Python Algorithmic Libraries

- Scikit Learn (Machine Learning: regression, classification, clustering)
- Statsmodels (Explore data, estimate statistical model and perform statistical tests)

**core libraries**necessary for working with Data Science in Python. Installing all Python

**Data Science libraries**individually can be a bit difficult for beginners, so we recommend Anaconda, an all-in-one installer.

**Anaconda**, the World's Most Popular Data Science Platform, (Mac, Windows, Linux) distribution for large-scale data processing and scientific research computing in Python(includes scientific research and data analysis libraries such as

**Pandas**, Numpy, Matplotlib, scikit-learn etc., as well as IPython). This is the recommended package for this course.

**Related Topics**