The Applied Data Science Lab develops novel analytical applications to improve the real world around us. Pritzker and May (2015:7) define Data Science as “the extraction of actionable knowledge directly from data through a process of discovery, or hypothesis formulation and hypothesis testing”. In addition, they also relate the skills needed in Data Science.

Based on their observations Spruit and Jagesar (2016) propose to define Applied Data Science as “the knowledge discovery process in which analytical applications are designed and evaluated to improve the daily practices of domain experts”. This is in contrast to fundamental data science which aims to develop novel statistical and machine learning techniques for performing Data Science.

From the perspective of Pritzker and May’s (2015:9) Data Science Venn diagramme as shown in the left figure above, Applied Data Science focuses on the Analytical applications (i.e. Analytic systems) intersection between Domain expertise and Engineering capabilities, as shown in the right figure above.

Finally, we observe an analogy with the ubiquitous people-process-technology model where technology aligns with machine learning algorithms, organisational processes are operationalised through analytic systems, and domain expertise is captured from, and enriched for, skilled professionals. Hence our motto: power to the people!

We welcome research visitors who aim to conduct research aligned with our themes. Please contact us if you want to discuss opportunities for collaboration.

References 

Pritzker, P. and May, W. (2015). NIST Big Data interoperability Framework (NBDIF): Volume 1: Definitions. NIST Special Publication 1500-1. Final Version 1, September 2015.
Spruit, M. and Jagesar, R. (2016). Power to the People! Meta-algorithmic modelling in applied data science. In Fred, A. et al. (Eds.), Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 400-406). KDIR 2016, November 11-13, 2016, Porto, Portugal: ScitePress.