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

# Introduction to categorical data

Sometimes you get [categorical data](https://en.wikipedia.org/wiki/Categorical_variable) which are variables with a limited and usually fixed number of values. For example, **male** and **female**. Machine learning algorithms need numbers to work, so how do you deal with these? We will discuss two ways: 

* Label encoding 
* One-hot encoding a.k.a. dummy variables.

# Dealing with categorical data

## Label encoding

**Label encoding** works by converting the unique values to a numeric representation. For example, if we have two categories **male** and **female**, we can categorize them as numbers:

* male as **0**
* female **1**

Pandas provides an easy way to do this by using the `category` type.

This lesson discusses what is categorical data and how Pandas provide support dealing with it.

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Categorical Data

Introduction to categorical data #