data science

There is a new wave of educators and practitioners who are contributing some incredible resources to help aspiring data scientists get up to speed. In this post we’ve collated a few of those resources that we think are not only highlighting the principles, techniques or ethics of data science, but are also helping to shape the conversation about what makes data science different as a discipline. Also we recently did a Q&A with Sam Leach, our data scientist. You can read the blog post here.


Introduction to Data Science, Columbia University. The course serves as a solid introduction to the field of data science. Students learn to combine tools and techniques from statistics, computer science, data visualization and social sciences. Although the course is for students of Columbia University, much of the course material has been written up and is available online. The blog and related book Doing Data Science by Rachel Schutt and Cathy O’Neil is also a very interesting read.

General Assembly offer an eleven week evening course on data science that aims to teach students how to apply mathematics and programming skills to get meaning and insight out of large data sets using Python.

The Insight data science fellows program, based in the US, offers a six week postdoctoral course aimed at bridging the gap between academia and data science.

Massive Open Online Courses

John Hopkins Data Science Specialization is an excellent collection of Coursera courses, with instructors including Roger Peng and Jeff Leek. The course is a relatively new way of educating hundreds of thousands of people at a time, with the help of videos, quizzes, assignments, forums and peer instruction. Our data scientist Sam Leach followed their R and data analysis course and found them useful and enjoyable.

Coursera has a number of other data science courses – it’s worth taking advantage of them! Andrew Ng, one of the founders of Coursera, offers a useful course on machine learning.


Simply Statistics – although not a ‘data science blog’, the authors Jeff Leek, Roger Peng, and Rafa Irizarry are aware of the broader impact of statistics and computation on society and are passionate educators and communicators of science.

We are looking forward to following the new FiveThirtyEight blog by Nate Silver and his team which should be re-launching early this year.


Data Science For Business by Foster Provost  and Tom Fawcett

Thinking with Data by Max Schron

Lean Analytics by Alistair Croll and Benjamin Yoskowitz

We’re excited to review and discuss these books in more detail in a future blog post!


Data Science London is a London-based meetup with consistently high quality talks from a diverse set of practitioners. They also organise data science hackathons like their upcoming Urban Data Hack, which looks like it’ll be well worth checking out.

The London Big-O Meetup is also London-based, and looks at the algorithmic side of data science. There’s a talk coming up soon by Jason Davies, coauthor of the D3.js data visualisation framework, on February 19th.

What are your favourite data science resources? We’d love to hear from you on Twitter at @Inquiron.