A showcase for data science in action

Welcome to the home of Real World Data Science, a new project from the Royal Statistical Society. This site and its content are being created and curated by data science practitioners and leaders with a single goal in mind: to help you deliver high quality, ethical, impactful data science in your workplace.

Latest updates

OpenAI’s text classifier won’t calm fears about AI-written homework

Educators are worried about ChatGPT being using by students for homework assignments, so OpenAI has released a tool to classify whether text is human- or AI-written. But relying on the classifier’s results is ill-advised, as some basic statistics shows.

Mar 15, 2023

Brian Tarran

US legislators get their data science act together

A bill introduced in the US Congress wants to make funds available to develop data science and data literacy education across the United States. We sit down with education and policy experts to discuss the challenges and opportunities ahead.

Mar 6, 2023

Brian Tarran

Using ‘basket complementarity’ to make product recommendations

Purchase suggestions – e.g., “if you are buying that, you might also want this” – are, to a large extent, informed by the concept of complementarity: that certain products are often bought and/or used together. A journal paper by Puka and Jedrusik sheds light on how these product recommendations can be derived, as Moinak Bhaduri explains.

Mar 2, 2023

Moinak Bhaduri

What are our aims?

Real World Data Science aims to be a trusted, go-to source for high-quality, engaging and inspiring content which helps data science students, practitioners and leaders to:

discover and learn more efficiently;
acquire practical problem-solving skills;
share their knowledge and accomplishments publicly;
work smarter, ethically, and more effectively.

What will we provide?

Resources will be created to meet the needs of our target audiences. These include:

Case studies – showing how data science is used to solve real-world problems in business, public policy and beyond.
Explainers – interrogating the underlying assumptions and limitations of data science tools and methods, to help data scientists make smarter, more informed analytical choices.
Exercises – to challenge and develop the analytical mindset that all data scientists need to succeed.
Advice – interviews, Q&As, and FAQs on such topics as data science ethics, career paths, and communication, to support professional development.

Resources will also be curated to help data scientists identify trustworthy, high-quality content. These include:

Training guides – step-by-step approaches and recommended sources for learning new skills and methods.
Datasets – tagged and sorted to help educators and practitioners find data to meet their teaching and training needs.
Feeds – who and what to follow to keep up with new ideas and developments.

How you can get involved

See our open call for contributions.