Data Visualization in Stata

Description

Visualizing and graphing data is vital in modern data analytics. Whether you are a data scientist, student of quantitative methods, or a business user, having an understanding of how to visualize data is an important aspect of getting data information across to other stakeholders. Many ways of visualizing data have been devised and some are better than others. However, each method has advantages and disadvantages, and having a solid understanding of what visualization might be best suited is key to delivering a concise and sharp 'data message'. In this course, you will learn some of the most important data visualization methods. The author will also explain, without any equations or complex statistics, what are the advantages and disadvantages of each technique. You will be shown how each graph can be created, modified, and customized in Stata throughout the course's journey. This course will help you learn and understand the basic methods of data visualization. You will also learn, in an easy manner, variations and customizations of basic visualization methods. You will gain experience with different data visualization techniques and how to apply them. After understanding the techniques, you will learn and code many Stata graphs. Finally, you will gain confidence in your ability to modify and create bespoke data visualizations in Stata. By the end of this course, you will learn many different methods to visualize data and how to generate these yourself in Stata. All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Data-Visualization-in-Stata

What You Will Learn

Learn the basics of data visualization
Look at graphing in Stata and understand basic plot types
Explore intermediate and advanced plot types
Learn about the distribution and relationship plots
Explore the world of categorical and specialized plots
Expand your knowledge on basic to advanced Stata codes

Audience

This course is ideally for Stata users, data analysts, data scientists, quantitative degree students, and quantitative business users that have a flair to explore this arena.

Economists, social scientists, political scientists, biostatisticians, and other disciplines will also benefit from this course.

Basic Stata knowledge along with some basic Stata coding (.do files, commands, varlists, and options) is an essential requirement to extract the most out of this course.

Approach

This course is designed to provide you with a compact and easy-to-understand set of videos that focus on the basic principles behind many common data visualizations and how you can code them in Stata.

The course is modular; just click on the graphs that you are most interested in and learn away. You do not need to follow this course linearly.

Key Features

Learn basic methods of data visualization, variations, and customizations of visualization methods * Gain experience of different data visualization techniques and how to apply them * Implement code files along with bite-sized videos in a modular and easy-to-use approach

Github Repo

https://github.com/PacktPublishing/Data-Visualization-in-Stata

About the Author

Franz Buscha

Franz Buscha is a professor of economics at the University of Westminster, which he joined after completing his Ph.D. in economics at Lancaster University. He has been involved in numerous funded research projects from research councils and government departments. He has also contributed to a wide range of projects, including policy evaluation and bespoke econometric advice to UK government departments. Franz has published in leading journals and contributed to numerous policy reports. His research has even been covered by various media outlets. He is an experienced online educator and has published several online courses, including LinkedIn Learning. Franz also has a monthly radio program called Policy Matters on Share Radio.

Course Outline

1. Introduction

2. Graphs for single continuous variables

3. Plots for single discrete variables

4. Plots for two continuous variables

5. Plots for two discrete variables

6. Plots for three or more variables