Build your ultimate AI agent
Course Description
When your dataset is represented as a table or a database, it's difficult to observe much about it beyond its size and the types of variables it contains. In this course, you'll learn how to use graphical and numerical techniques to begin uncovering the structure of your data. Which variables suggest interesting relationships? Which observations are unusual? By the end of the course, you'll be able to answer these questions and more, while generating graphics that are both insightful and beautiful.
Prerequisites
Curriculum
Course outline
1
Exploring Categorical Data
In this chapter, you will learn how to create graphical and numerical summaries of two categorical variables.
- Exploring categorical data50 XP
- Bar chart expectations50 XP
- Contingency table review100 XP
- Dropping levels100 XP
- Side-by-side bar charts100 XP
- Bar chart interpretation50 XP
- Counts vs. proportions50 XP
- Conditional proportions50 XP
- Counts vs. proportions (2)100 XP
- Distribution of one variable50 XP
- Marginal bar chart100 XP
- Conditional bar chart100 XP
- Improve pie chart100 XP
2
Exploring Numerical Data
In this chapter, you will learn how to graphically summarize numerical data.
3
Numerical Summaries
Now that we've looked at exploring categorical and numerical data, you'll learn some useful statistics for describing distributions of data.
4
Case Study
Apply what you've learned to explore and summarize a real world dataset in this case study of email spam.
R
Exploratory Data Analysis in R
Course
Complete
