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Data Science Visualization - Module 2

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Updated 12/2024

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Course Description

Find the complete course on edx.org here.

Prerequisites

There are no prerequisites for this course

1

Data Types

Exercise 1. Variable names

Exercise 2. Variable type

Exercise 3. Numerical values

Exercise 4. Tables

Exercise 5. Indicator variables

Exercise 6. Data types - heights

End of Assessment: Data Types

2

Quantiles, Percentiles, and Boxplots

Exercise 1. Vector lengths

Exercise 2. Percentiles

Exercise 3. Interpreting Boxplots - 1

Exercise 4. Interpreting Boxplots - 2

Exercise 5. Interpreting Boxplots - 3

Exercise 6. Low quantiles

Exercise 7. Interquantile Range (IQR)

End of Assessment: Quantiles, Percentiles, and Boxplots

3

Distributions

Exercise 1. Distributions - 1

Exercise 2. Distributions - 2

Exercise 3. Empirical Cumulative Distribution Function (eCDF)

Exercise 4. eCDF Male Heights

Exercise 5. eCDF of Murder Rates

Exercise 6. eCDF of Murder Rates - 2

Exercise 7. Histograms

Exercise 8. Histograms - 2

Exercise 9. Density plots

Exercise 10. Density plots - 2

End of Assessment: Distributions

4

Normal Distributions

Exercise 1. Proportions

Exercise 2. Averages and Standard Deviations

Exercise 3. Approximations

Exercise 4. Seven footers and the NBA

Exercise 5. Estimating the number seven footers

Exercise 6. How many seven footers are in the NBA?

Exercise 7. Lebron James' height

Exercise 8. Interpretation

End of Assessment: Normal Distribution

5

Robust Summaries with Outliers

Exercise 1. Exploring the Galton Dataset - Average and Median

Exercise 2. Exploring the Galton Dataset - SD and MAD

Exercise 3. Error impact on average

Exercise 4. Error impact on SD

Exercise 5. Error impact on median

Exercise 6. Error impact on MAD

Exercise 7. Usefulness of EDA

Exercise 8. Using EDA to explore changes

End of Assessment: Robust Summaries with Outliers

6

Introduction to ggplot2

7

Summarizing with dplyr

Practice Exercise. National Center for Health Statistics

Exercise 1. Blood pressure 1

Exercise 2. Blood pressure 2

Exercise 3. Summarizing averages

Exercise 4. Min and max

Exercise 5. group_by

Exercise 6. group_by example 2

Exercise 7. group_by example 3

Exercise 8. Arrange

End of Assessment: Summarizing with dplyr

8

Exploring the gapminder dataset

Exercise 1. Life expectancy vs fertility - part 1

Exercise 2. Life expectancy vs fertility - part 2 - coloring your plot

Exercise 3. Life expectancy vs fertility - part 3 - selecting country and region

Exercise 4. Life expectancy and the Vietnam War - part 1

Exercise 5. Life expectancy and the Vietnam War - part 2

Exercise 6. Life expectancy in Cambodia

Exercise 7. Dollars per day - part 1

Exercise 8. Dollars per day - part 2

Exercise 9. Dollars per day - part 3 - multiple density plots

Exercise 10. Dollars per day - part 4 - stacked density plot

Exercise 11. Infant mortality scatter plot - part 1

Exercise 12. Infant mortality scatter plot - part 2 - logarithmic axis

Exercise 13. Infant mortality scatter plot - part 3 - adding labels

Exercise 14. Infant mortality scatter plot - part 4 - comparison of scatter plots

End of Assessment: Exploring the gapminder dataset

9

Data Visualization Principles - Part 1

Exercise 1: Customizing plots - Pie charts

Exercise 2. Customizing plots - What's wrong?

Exercise 3: Customizing plots - What's wrong 2?

End of Assessment: Data Visualization Principles, Part 1

10

Data Visualization Principles - Part 2

Exercise 1: Customizing plots - watch and learn

Exercise 2: Customizing plots - redefining

Exercise 3: Showing the data and customizing plots

Exercise 4: Making a box plot

End of Assessment: Data Visualization Principles, Part 2

11

Data Visualization Principles - Part 3

Exercise 1: Tile plot - measles and smallpox

Exercise 2. Time series plot - measles and smallpox

Exercise 3: Time series plot - all diseases in California

Exercise 4: Time series plot - all diseases in the United States

End of Assessment: Data Visualization Principles, Part 3

Data Science Visualization - Module 2

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