Learn Statistical Data Analysis with Python

By the end of this course, you will have achieved the following learning outcomes:

• I can explain and calculate the importance of measures of central tendency.
• I can explain and calculate the importance of measures of dispersion.
• I can identify the relative strengths and weaknesses of the measures of tendency.
• I can identify the relative strengths and weaknesses of the measures of dispersion.
• I can create and interpret a histogram, a bar chart, a box plot, and a frequency table.
• I can identify and describe scatter plots and line graphs to determine the relationships between two variables.
• I can calculate and interpret the Pearson correlation coefficient to determine the relationships between two variables.

These are some of the basics statistical data analysis techniques that you will get to use while working on data science projects. For example, in order to check for model assumptions while working on a predictive solution, you will need to apply the above techniques i.e. to test for normality of variables in a dataset, you can plot a histogram or a pair plot, to check for correlation, you can calculate the Pearson correlation coefficient etc.

In addition, these techniques will also be important while also working on data analysis projects where the creation of a descriptive analysis report will be a necessity.