In this course on Data Science: Visualization, you’ll learn basic data visualization principles and how to apply them using ggplot2.
About this course
As part of this Professional Certificate Program in Data Science, this course covers the basics of data visualization and exploratory data analysis. You will be able to use three motivating examples and ggplot2, a data visualization package for the statistical programming language R. You can get started with simple datasets and then graduate to case studies about world health, economics, and infectious disease trends in the United States.
Also, you will be looking at how mistakes, biases, systematic errors, and other unexpected problems often lead to data that should be handled with care. The fact that it can be difficult or impossible to notice a mistake within a dataset makes data visualization particularly important.
The growing availability of informative datasets and software tools has led to increased reliance on data visualizations across many areas. Data visualization provides a powerful way to communicate data-driven findings, motivate analyses, and detect flaws. This course will give you the skills you need to leverage data to reveal valuable insights and advance your career.
What you will learn from Data Science: Visualization?
- Data visualization principles.
- How to communicate data-driven findings.
- How to use ggplot2 to create custom plots.
- The weaknesses of several widely-used plots and why you should avoid them.
- An up-to-date browser is recommended to enable programming directly in a browser-based interface.
Introduction and Welcome
Section_1: Introduction to Data Visualization and Distributions
Section_2: Data Science: Visualization using ggplot2
Section_3: Data Science: Visualization: Summarizing with dplyr
Section_4: Data Science: Visualization using Gapminder
Section_5: Data Visualization Principles
Comprehensive Assessment and End of Course Survey
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- Harvard University
- Online Course
- 1-3 Months
- Free Course (Affordable Certificate)
- Data Analysis Data Science