mlxtend.tar.gz

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

## What is an outlier?
Another area of cleaning can be dealing with outliers. First off, how do you define an outlier? This can require domain knowledge as well as other information, but a simple way to start is by taking a look at box plots:

# What is an outlier?
Another area of cleaning can be dealing with outliers. First off, how do you define an outlier? This can require domain knowledge as well as other information, but a simple way to start is by taking a look at box plots:

This lesson explains what are outliers, why they happen and how to remove them.

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Outliers

What is an outlier?