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Bus Number (bŭs nŭmʹbər), noun:

The number of people that need to get hit by a bus before your data science project becomes irreproducible.

This number might be zero. In this tutorial, we aim to increase your bus number.

A typical data science process involves three main kinds of tasks:

• Munge: Fetch, process data, do EDA
• Science: Train models, Predict, Transform data
• Deliver: Analyze, summarize, publish

where our time tends to be allocated something like this:

Unfortunately, even though most of the work tends to be in the munge part of the process, when we do try and make data science reproducible, we tend to focus mainly on reprodibility of the science step.

That seems like a bad idea, especially if we're doing unsupervised learning, where often our time is spent like this:

We're going to try to improve this to a process that is reproducible from start to finish.

There are 4 steps to a fully reproducible data science flow:

• Creating a Reproducible Environment
• Creating Reproducible Data
• Building Reproducible Models
• Achieving Reproducible Results

In this series of tutorials, we will look at each of these steps in turn. This repo is all about getting you started doing Reproducible Data Science , and giving you a deeper look at some of the concepts we will cover in this tutorial. For the latest version, visit:

https://github.com/hackalog/bus_number

To get started, open Tutorial 1: Reproducible Environments.