Introducing Daniel Fake

We are excited to introduce Daniel Fake, our newest member of the team. Daniel joins us as a data consultant who will be helping out on a range of exciting projects. 

Taranaki te maunga, Aotea te waka, Ngaa Ruahine me Ngaati Ruanui te iwi, Waiokura te marae. 

Ko Daniel Aronui Fake tooku ingoa

Daniel was born in rural Taranaki and schooled in Hawera. Following a passion for sport he moved to Palmerston North in 2010 and completed a Bachelor of Exercise Science. He fell in love with study and decided on another 4 years of education to pursue a Physiotherapy Degree in Auckland at AUT. Following this, Dan worked for 3 years onsite with Westlake Boy's High School and spent a season learning from the Warriors NRL physiotherapist. 

Just prior to Covid he moved into a vocational physiotherapy role with BodyCare who operated on site with corporate clients. This role quickly changed as the business pivoted in response to the pandemic. They organised, supplied and implemented onsite rapid-antigen testing services to clients in various packages to meet government standards while trying to optimise and maintain business performance as best as possible. This process was very much data driven as every test was recorded digitally through an app/dashboard developed prior to the government roll out. It was here that his interest in data peaked as he saw the potential which ultimately led to the decision to leave his Physiotherapy career and study the subject formally. 

Dan enrolled in a full immersion Data Science and Artificial Intelligence course with the Institute of Data hosted by AUT. This course began with a deep dive into Statistics and Calculus and then moved to applying these principles as algorithms using multiple Python libraries. Dan now has experience in the entire data flow from wrangling, cleaning, munging, EDA, feature engineering through to model application and optimisation. These models included both supervised and unsupervised learning, regression, support vector machines, decision trees, classification (including text and image data), clustering, and deep, recurrent and convolutional neural networks.  This was then packaged into insights and applied to business/contextual problems as formal reports and presentations to simulate real world application. With these tools Dan is more passionate than ever in solving complex problems he originally would have not considered. From here he hopes to help contribute in a real way to the fast-paced, ever-changing new world.