The book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, then using statistical models to analyse data, writing about the results of those models, drawing conclusions from them, and finally, using the cloud to put a model into production, all done in a reproducible way.

At the moment, there are a lot of books that teach data science, but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets, cleaning and preparing them, before analysing them. There are also a lot of books that teach statistical modelling, but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics, and most of those that do, have a token ethics chapter. Finally, reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data, prepare data, analyse data, and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data, and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models, so aspects such as writing are explicitly covered. And finally, the use of GitHub and the open-source statistical language R are built in throughout the book.

Key Features:

  • Extensive code examples.
  • Ethics integrated throughout.
  • Reproducibility integrated throughout.
  • Focus on data gathering, messy data, and cleaning data.
  • Extensive formative assessment throughout.

1. Telling stories with data  2. Drinking from a fire hose  3. Reproducible workflows  Part 1. Foundations  4. Writing research  5. Static communication  Part 2. Communication  6. Farm data  7. Gather data  8. Hunt data  Part 3. Acquisition  9. Clean and prepare  10. Store and share  Part 4. Preparation  11. Exploratory data analysis  12. Linear models  13. Generalized linear models  14. Causality from observational data  15. Multilevel regression with post-stratification  16. Text as data  17. Concluding remarks