# 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)

This course begin with the **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.