# Lecture 10 – Visualization I

Presented by Fernando Pérez

Content by Fernando Pérez, Suraj Rampure, Ani Adhikari, Sam Lau, Yifan Wu, Deborah Nolan

A reminder – the right column of the table below contains *Quick Checks*. These are **not** required but suggested to help you check your understanding.

Video | Quick Check | |
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10.0 Formal definition of visualization. The purpose of visualization in the data science lifecycle. |
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10.1 Formal definition of visualization. The purpose of visualization in the data science lifecycle. |
10.1 | |

10.2 Different ways we can map from data to properties of a visualization. |
10.2 | |

10.3 Defining distributions, and determining whether or not given visualizations contain a distribution. |
10.3 | |

10.4 Bar plots as a means of displaying the distribution of a qualitative variable, as well as for plotting a quantitative variable across several different categories. |
10.4 | |

10.5 Rug plots. Histograms, where areas are proportions. Reviewing histogram calculations from Data 8. Density curves as smoothed versions of histograms. |
10.5 | |

10.6 Describing distributions of quantitative variables using terms such as modes, skew, tails, and outliers. |
10.6 | |

10.7 Using box plots and violin plots to visualize quantitative distributions. Using overlaid histograms and density curves, and side by side box plots and violin plots, to compare multiple quantitative distributions. |
10.7 | |

10.8 Using scatter plots, hex plots, and contour plots to visualize the relationship between pairs of quantitative variables. Summary of visualization thus far. |
10.8 |