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 line graphs

**Line graphs** are very useful for showing a value over time, such as a stock's price. Typically, one would use a line graph over a scatter plot if there is a connecting component between the values, such as time. Here is an example of a value growing exponentially over time:

# Introduction to line graphs

**Line graphs** are very useful for showing a value over time, such as a stock's price. Typically, one would use a line graph over a scatter plot if there is a connecting component between the values, such as time. Here is an example of a value growing exponentially over time:

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

What is Analytics

Python Basics for Analytics

Reading Data

Describing Data

Cleaning Data

Visualizing Data

Visualization with Line Graphs

Introduction to line graphs #