Get started with the basics of analytics in Python, the language of choice for data science.

Python Data Analysis and Visualization

Get started with the basics of analytics in Python, the language of choice for data science using Python data structures,  descriptive statistics, and much more.

Data Analysis and Visualization

## Calculating correlations

[Scipy](https://www.scipy.org/scipylib/index.html) is a Python library for scientific computing. Scipy and Numpy are the core libraries that Pandas is built upon. We will discuss Pandas later in the course, but having an understanding of Scipy and Numpy before discussing Pandas is useful.

A **correlation** is a numerical measure of the statistical relationship between two variables. For us, those variables will usually be two columns of data, for example, the temperature outside and the likelihood of rain.

One way to calculate the correlation between two vectors of data is with **Pearson's r-value**. This value ranges between -1 and 1. Where -1 means there is a total negative correlation, 0 means no correlation, and 1 means total positive. 

>**Note**: these are all linear correlations.

In the image below, you can see a graphical representation of correlation. [Source](https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/correlation-analysis/).


# Calculating correlations

[Scipy](https://www.scipy.org/scipylib/index.html) is a Python library for scientific computing. Scipy and Numpy are the core libraries that Pandas is built upon. We will discuss Pandas later in the course, but having an understanding of Scipy and Numpy before discussing Pandas is useful.

A **correlation** is a numerical measure of the statistical relationship between two variables. For us, those variables will usually be two columns of data, for example, the temperature outside and the likelihood of rain.

One way to calculate the correlation between two vectors of data is with **Pearson's r-value**. This value ranges between -1 and 1. Where -1 means there is a total negative correlation, 0 means no correlation, and 1 means total positive. 

>**Note**: these are all linear correlations.

In the image below, you can see a graphical representation of correlation. [Source](https://www.statisticshowto.datasciencecentral.com/probability-and-statistics/correlation-analysis/).


This lesson introduces an external scipy library by discussing in detail how scipy provides support to handle statistics and probabilistic functionalities.

What is Analytics

Python Basics for Analytics

Describing Data

Cleaning Data

Visualizing Data

Scipy an External Library

Calculating correlations #