# (PART) Communicate {-}

# Introduction

The successful completion of a data science project you will have built up a good understand of what is going on with the data. It doesn't matter how brilliant your understand is unless you can communicate it with others. You will need to share your work in a way that your audience can understand. Your audience might be fellow scientists who will want to reproduce the work, non-scientists who will want to understand your findings in plain terms, or yourself (in the future) who will be thankful if you make your work easy to re-learn and recreate. __Part 5__ discusses communication, and how you can use RMarkdown to generate reproducible artefacts that combine prose and code.

```{r echo = FALSE, out.width = "75%"}
knitr::include_graphics("diagrams/data-science-communicate.png")
```

Reproducible, literate code is the data science equivalent of the Scientific Report (i.e, Intro, Methods and materials, Results, Discussion).

Recommendations for learning more about communication:

For writing: [Style: Lessons in Clarity and Grace](http://amzn.com/0321898680), <http://www.americanscientist.org/issues/id.877,y.0,no.,content.true,page.1,css.print/issue.aspx>

For presentations: [slide:ology](http://amzn.com/0596522347), <http://www.howtogiveatalk.com>, <https://github.com/jtleek/talkguide> (academic), http://speaking.io, https://www.coursera.org/learn/public-speaking

For expository visulisations: WSJ guide?

Design: [The Non-Designer's Design Book](http://amzn.com/0133966151)
