Yet, these uncertainties and risks – for the business leader and the data scientist alike – can be controlled and managed if approached in a sensible manner. For the data scientist the situation can be equally unnerving, with uncertainties about how to deliver a successful project when the path is not clear. Many business leaders are left feeling unsettled, balancing the need for innovation and the adoption of revolutionary technologies with an uncomfortable degree of uncertainty and risk of failure.

Often these projects, while successful from a scientific standpoint, miss the mark in terms of business impact. Yet for these benefits to be realised, data science initiatives must be designed and executed in a sensible way. Read more → 18 Tidy Finance with R by Christoph Scheuch (wikifolio Financial Technologies) and Stefan Voigt (University of Copenhagen and Danish Finance Institute) and Patrick Weiss (Reykjavik University and Vienna University of Economics and Business)ĭata science, machine learning and artificial intelligence (AI) can have game-changing impacts for businesses, empowering them to increase operational efficiency, improve the quality of their services and understand their customers better. The third section also features a chapter for anyone wishing to start contributing to rOpenSci packages. The third and last section features our best practice for nurturing your package once it has been onboarded: how to collaborate with other developers, how to document releases, how to promote your package and how to leverage GitHub as a development platform. The second section is dedicated to rOpenSci’s software peer review process: what it is, our policies, and specific guides for authors, editors and reviewers throughout the process. The first section of the book contains our guidelines for creating and testing R packages.

This book is a guide for authors, maintainers, reviewers and editors of rOpenSci. Extended version of the rOpenSci packaging guide.

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