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Understanding Data Visualization

An introduction to data visualization with no coding involved.

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2 Hours14 Videos43 Exercises80,019 Learners2550 XPData Analyst TrackUnderstanding Data Topics Track

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Course Description

Visualizing data using charts, graphs, and maps is one of the most impactful ways to communicate complex data. In this course, you’ll learn how to choose the best visualization for your dataset, and how to interpret common plot types like histograms, scatter plots, line plots and bar plots. You'll also learn about best practices for using colors and shapes in your plots, and how to avoid common pitfalls. Through hands-on exercises, you'll visually explore over 20 datasets including global life expectancies, Los Angeles home prices, ESPN's 100 most famous athletes, and the greatest hip-hop songs of all time.

  1. 1

    Visualizing distributions

    Free

    In this chapter you’ll learn the value of visualizations, using real-world data on British monarchs, Australian salaries, Panamanian animals, and US cigarette consumption, to graphically represent the spread of a variable using histograms and box plots.

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    A plot tells a thousand words
    50 xp
    Motivating visualization
    50 xp
    Continuous vs. categorical variables
    100 xp
    Histograms
    50 xp
    Interpreting histograms
    100 xp
    Adjusting bin width
    50 xp
    Box plots
    50 xp
    Interpreting box plots
    100 xp
    Ordering box plots
    50 xp
  2. 2

    Visualizing two variables

    You’ll learn how to interpret data plots and understand core data visualization concepts such as correlation, linear relationships, and log scales. Through interactive exercises, you’ll also learn how to explore the relationship between two continuous variables using scatter plots and line plots. You'll explore data on life expectancies, technology adoption, COVID-19 coronavirus cases, and Swiss juvenile offenders. Next you’ll be introduced to two other popular visualizations—bar plots and dot plots—often used to examine the relationship between categorical variables and continuous variables. Here, you'll explore famous athletes, health survey data, and the price of a Big Mac around the world.

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  3. 3

    The color and the shape

    It’s time to make your insights even more impactful. Discover how you can add color and shape to make your data visualizations clearer and easier to understand, especially when you find yourself working with more than two variables at the same time. You'll explore Los Angeles home prices, technology stock prices, math anxiety, the greatest hiphop songs, scotch whisky preferences, and fatty acids in olive oil.

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  4. 4

    99 problems but a plot ain't one of them

    In this final chapter, you’ll learn how to identify and avoid the most common plot problems. For example, how can you avoid creating misleading or hard to interpret plots, and will your audience understand what it is you’re trying to tell them? All will be revealed! You'll explore wind directions, asthma incidence, and seats in the German Federal Council.

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In the following tracks

Data AnalystUnderstanding Data Topics

Collaborators

lis-sulmont
Lis Sulmont
Richie Cotton Headshot

Richie Cotton

Data Evangelist at DataCamp

Richie is a Data Evangelist at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.
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