- [Instructor] For your challenge, you will use another data set from Hadley Wickham's article. His ES3 file is included among your exercise files. The data set comes from the World Health Organization and it records the number of confirmed tuberculosis cases by country, year, and demographic group. The demographic groups are broken down by sex and age. Let me import some packages and then load the file.
However, we see that this data frame is not tidy. The sex and the age groups are recorded somewhat confusingly in the column names. For instance, MO4 stands for male from zero to four years of age. Also, there are many missing values, which appeared as NAN, not a number. The challenge for you is to transform this data frame into this clean version. Here, only actual observations are recorded and the age range and sex are recorded as separate variables.
Let's do it.
- Installing and setting up Python
- Importing and cleaning data
- Visualizing data
- Describing distributions and categorical variables
- Using basic statistical inference and modeling techniques
- Bayesian inference
Skill Level Intermediate
R Statistics Essential Trainingwith Barton Poulson5h 59m Intermediate
SPSS Statistics Essential Trainingwith Barton Poulson4h 57m Beginner
1. Installation and Setup
2. Importing and Cleaning Data
3. Visualizing and Describing Data
4. Introduction to Statistical Inference
5. Introduction to Statistical Modeling
Next steps1m 55s
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