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.

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

## Introduction to heat maps

[**Heat maps**](https://seaborn.pydata.org/generated/seaborn.heatmap.html) are  a plotted data matrix where each value in the matrix is a category.

As an example, consider the [heat map](https://www.excel-easy.com/examples/heat-map.html) shown below.

This heat map represents the average monthly temperatures at Central Park, New York for a given year (**rows**) and month (**columns**). The coloring of the heat map makes it easy to see that the warmest months are consistently from Jun to Sep.

# Introduction to heat maps

[**Heat maps**](https://seaborn.pydata.org/generated/seaborn.heatmap.html) are  a plotted data matrix where each value in the matrix is a category.

As an example, consider the [heat map](https://www.excel-easy.com/examples/heat-map.html) shown below.

This heat map represents the average monthly temperatures at Central Park, New York for a given year (**rows**) and month (**columns**). The coloring of the heat map makes it easy to see that the warmest months are consistently from Jun to Sep.

This lesson explains what a heat map is, why it is used, and how to visualize data with a heat map using Python libraries.

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Visualization with Heat Maps

Introduction to heat maps #