Data Scientist Knowledge and Skills Evaluation towards a Data-Driven Research Methodology




professional practice, knowledge and skills, modelling and evaluation, research methodology, Data science degree apprenticeship,


The modern business world increasingly requires a higher level of data science expertise as well as abilities in problem solving and data analytics. Data science is a broad and fast-moving field of methods, processes, algorithms and systems to extract insights from data. The University of Winchester has followed the guidance of the Institute for Apprenticeships and Technical Education (IfATE) and worked in partnership with a number of international, national and regional employers in the design and development of its Data Scientist (integrated degree) programme, which leads to a Bachelor’s degree in Data Science. This degree apprenticeship supports students in gaining the knowledge and skills that are in demand by employers today and into the future, where working in multi-disciplinary teams alongside domain experts will often be the norm. IfATE specifies an End-point Assessment (EPA) plan to enable the apprenticeship to be completed in accordance with its Data Scientist degree apprenticeship standard. This paper considers professional practice and competence in data science and links the processes used in completing the EPA with domain-based knowledge and expertise. It reviews representative solution methodologies before demonstrating the applicability of a data-driven research methodology to discover insights and achieve organisational goals.

Author Biography

Jing Lu, University of Winchester

Dr Jing Lu joined the University of Winchester Business School in 2016 as a Senior Lecturer in Data Analytics. Her research activities have extended across data-driven frameworks and process methodology through computer applications and visualisation to business analytics and data science, including modelling of real-world problems using digital technologies.