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.
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