A university wants to keep track of the popularity of different programs over time, to ensure that programs are allocated enough space and resources. You work in the admissions office and are asked to put together a set of visuals that show these trends to interested applicants. How can we calculate these distributions? Would we be able to see trends and predict the popularity of certain programs in the future? How would we show this information?
In this lesson, we are going to learn how to use NumPy to analyze different distributions, generate random numbers to produce datasets, and use Matplotlib to visualize our findings.
This lesson will cover:
- How to generate and graph histograms
- How to identify different distributions by their shape
- Normal distributions
- How standard deviations relate to normal distributions
- Binomial distributions
To your right, you’ll find a Jupyter notebook with some example calculations using NumPy. We won’t be using Jupyter notebooks in this lesson, but they’re a great way of combining text, code, and visualization. You can find more about them on the Jupyter website.
Don’t worry about understanding the individual lines of code; this example is just meant to show you the types of things that you’ll be learning in this lesson.
When you’re ready, continue to the first exercise.