Data Science: Visualization

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Course Features

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Duration

8 weeks

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Beginner

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Effort

2 hours per week

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Teaching Type

Self Paced

Course Description

As part of our Professional Certificate Program in Data Science, this course covers the basics of data visualization and exploratory data analysis. We will use three motivating examples and ggplot2, a data visualization package for the statistical programming language R. We will start with simple datasets and then graduate to case studies about world health, economics, and infectious disease trends in the United States.

We'll also 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.

Course Overview

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International Faculty

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Case Based Learning

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Post Course Interactions

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Case Studies,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

An up-to-date browser is recommended to enable programming directly in a browser-based interface

What You Will Learn

Data visualization principles

How to communicate data-drivenfindings

How to use ggplot2 to create custom plots

The weaknesses of several widely-used plots and why you should avoid them

Course Instructors

Rafael Irizarry

Professor of Biostatistics

Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute. For th...
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