Introduction

What is a Data Science Workflow?

A data science workflow is a structured, repeatable process for analyzing data.
It typically includes stages such as data cleaning, exploration, modeling, and evaluation.
Rather than jumping straight into modeling, a good workflow emphasizes understanding and preparing the data first.
A clear workflow helps ensure that analyses are organized, transparent, and reproducible.
Reproducibility means that you (or someone else) can rerun the same analysis and get the same results.
Workflows are especially important when working on collaborative projects or scaling to larger datasets.
In this workshop, we will focus on a simple, practical workflow:
- Clean → Explore → Model → Evaluate → Scale

Many beginners focus on learning individual tools or models, but struggle to connect them into a coherent process.
A well-defined workflow helps you know:
- where to start
- what to do next
- how to avoid common mistakes
Most of the time in real-world data science is spent on data cleaning and preparation—not modeling.
A consistent workflow makes your work easier to debug, share, and extend.
Proper workflows help prevent common pitfalls such as:
- overfitting models
- evaluating models on the wrong data
- making unreliable predictions (extrapolation)
The same workflow you learn here can be applied to:
- different datasets
- different models
- larger computing environments (including HPC)
The goal of this workshop is not just to build models, but to build good habits for working with data in R.