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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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

# Introduction to distributions

A [probability distribution](https://en.wikipedia.org/wiki/Probability_distribution) is a mathematical function that provides the probabilities of the occurrence of different possible outcomes. 

For example, you might have a program that returns **1** with a 50% probability and **0** with a 50% probability. Thus, 50% of your probability distribution would be assigned to 1 and 50% to 0.

If you were to plot this expected distribution, you would have two bars of equal height for 1 and 0.

Often, with data you don't know the mathematical function which generated your data, so instead you observe the empirical distribution. You might sample 10 colored balls from a bag and get 2 red, 3 yellow, and 5 green. That would then be your empirical distribution and you could graphical represent it with 3 bars. One of height 2 for red, one of height 3 for yellow, and one of height 5 for green.

Seaborn has a few ways to plot distributions:

* Histograms
* Box plots
* Violin plots
* Joint plots

# Types of distributions

## Histogram

As explained by Wikipedia, a [**histogram**](https://en.wikipedia.org/wiki/Histogram) is an estimate of the probability distribution of a continuous variable. To construct a histogram, the first step is to **bin** the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable.

You create a histogram with the `distplot()` function is Seaborn. You only need to pass one argument which is the continuous variable for which you would like to construct a histogram.

Histograms, though, have a very important parameter - the number of bins. You specify the number of bins with the `bin` parameter. If unspecified, Seaborn tries to find a useful number of bins to use. It is important to remember, though that the more bins, the higher variance your plot will have; the fewer bins, the more bias. Be thoughtful when choosing the number of bins as it might change the way you view your data.

Let's take a look:

This lesson introduces us with types of distributions used to visualize data , and how Python provides support with its libraries.

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Visualization with Distributions

Introduction to distributions #