Delve into Python data analysis and visualization using NumPy, SciPy, Pandas, and Seaborn. Gain insights into reading, cleaning, and visualizing data to generate actionable insights.

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visualization

This course assumes no prior experience and can teach anyone how to use Python to analyze and visualize data. Stop using Excel and start harnessing the power of Python.

Data analysis is one of the hottest careers of the 21st century. As an analyst, your goal is to peel back layers of data in order to answer questions of interest; that is the power of analytics. It allows you to take raw data and create meaningful, actionable insights.

In this course, you’ll learn how to use Python, NumPy, SciPy, Pandas, and Seaborn to perform data analysis and visualization. You’ll explore the four crucial steps for any data analysis project: reading, describing, cleaning, and visualizing data. In each step, you will work with the most common and popular tools that data analysts use every day. By the end of the course, you will be able to confidently extract knowledge and answers from data.

Python 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

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Scipy an External Library

Calculating correlations #