Lab | Discrete and Continuous Probability Distributions

Introduction

As a data analyst, you might need to use probability distribution for several reasons:

• To discover meaningful relationships between events.

• To make better data-driven decisions by answering questions like 'how likely something has happened out of pure coincidence'.

Getting Started

Open discrete.ipynb and continuous.ipynb in the your-code directory. There are exercises on uniform, normal, exponential, Bernoulli's, binomial, and poisson distributions. In each exercise please read the question carefully and provide your solutions below the question. All the calculations must be performed using Python. The dataset to be used in the exercise has been provided on GitHub here. Also please keep in mind that you might also need to use some of the functions you saw in the previous lessons. Please refer the notes.

Happy Learning!!

Deliverables

• discrete.ipynb and continuous.ipynb with your responses.

Submission

Upon completion, add your deliverables to git. Then commit git and push your branch to the remote.

Other Resources

Scipy module Stats

Probability distribution cheat sheet

Poisson Distribution: Predict the score in soccer betting

scipy.stats.poisson