We are delighted to announce our complete list of keynote speakers for the upcoming DataTech19 conference (organised by The Data Lab as part of DataFest), which now includes Debbie Bard, expert in machine learning at scale and data-intensive computing for experimental science, from the National Energy Research Scientific Computing . . .
We have further updates on DataTech19, Scotland’s first technical data science conference!
We are pleased to announce our second confirmed keynote speaker for DataTech19: Mine Çetinkaya-Rundel (Associate Professor of the Practice, Duke University, and Data Scientist + Professional Educator at RStudio). Mine’s work focuses . . .
Join us for DataTech19, Scotland’s first technical data science conference as part of DataFest
The Data Lab is delighted to introduce a new event to the DataFest family: DataTech (14 March 2019, Edinburgh). This technical, one-day data science conference will cater to a diverse audience including analysts, developers, . . .
Recently I've finished work on a project intended to visualise the traffic flow within a subsidised transport service, operated by a Scottish council. This visualisation needed to display variations in traffic flow conditional on factors such as the time of day, day of the week, journey purpose, as well as other criteria. . . .
Some options and tips
Why would you need to do this? Say, for instance, you are dealing with sensitive data that should not leave a specific system, or quite simply that you are away on a work retreat - but your laptop is far less powerful than your work desktop computer which you left behind - so you want to keep using it from a distance. For such reasons, . . .
Tracking cooperation & conflict patterns over space and time in R
For this post, I've managed to find some extremely interesting historical event data offered by the Cline Center on this page. As you will see, this dataset can be quite challenging because of the sheer number of dimensions you could look at. With so many options, it becomes tricky to create visualisations with the 'right' level . . .
A set of recommendations for clean and usable data
The extent to which a dataset follows a set of commonly expected guidelines will often determine how much time you have left to spend thinking about your analysis. Ideally, you might intend to spend 20% of your time cleaning the data for a project, and 80% planning and carrying out your actual analysis. But often, it might turn out to be the . . .