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

# The importance of data visualization

So far, we have looked at understanding data via descriptive statistics and tables. Another useful tool is visualization. 

Visualizations of data can provide the following benefits:

* A better understanding of the data
* A more compelling story when explaining the data
* An easier to comprehend medium

Data visualization is a core skill necessary for any analyst or data scientist. Being able to use great visualizations to help tell a data story often significantly adds to the comprehension of others. That added understanding can be the difference in driving a project forward.

# Data visualization tools in Python

In Python, two of the most popular tools for visualizing data are **Matplotlib** and **Seaborn**. These are the tools we will focus on, but there are many others including **Bokeh**, **ggpy**, and **D3**.

**Matplotlib** is sort of the base plotting library in Python. Think of it as a low-level library that allows you to do all sorts of things, but this flexibility can sometimes make it hard to work with. Matplotlib has been around for a while and sometimes can look a bit dated in style.

**Seaborn** was created to help deal with some of these issues. It is built on top of Matplotlib and in its own words, "provides a high-level interface for drawing attractive statistical graphics." For the most part, when possible, I lean towards using Seaborn. When I need more low-level control, I pull in Matplotlib. Since Seaborn is built on top of Matplotlib, it is pretty easy to mix the two.

# Our dataset

Before we get started with visualization, I will show how to load in the dataset we will be using:

This lesson will cover the importance of visualizing data, and why visualization is required in data science as well as common tools.

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Introduction to Visualization

The importance of data visualization #