Machine Learning: Paint it Black

As both a mathematician and Data Scientist, I can understand the power of Deep Learning, and Machine Learning more broadly, and the breadth and scale of what it affords us these days with access to cheap computational power and huge amounts of data. However, I can’t help but also feel a sense of unease with…

Deep Learning – Deeply Limited?

From a mathematical perspective, Deep Learning can effectively be defined as: The application of a set of complex geometric transformations to map the input space to the output space. In more detail, we are simply doing the following when developing a Deep Learning model: Vectorisation: convert input and output data into vectors ie positions of…

Building a Successful Data Science Practice

I recently had a great chat with Kirill Eremenko as a guest of his SuperDataScience Careers Podcast. I shared some of my insights on how to build a successful Data Science practice, and also discussed how to become an effective Data Scientist. Link to the podcast and a downloadable infographic below: https://www.superdatascience.com/podcast-building-successful-data-science-practice-effective-data-scientist/

Becoming An Effective Data Scientist

I recently had a great chat with Kirill Eremenko as a guest of his SuperDataScience Careers Podcast. I shared some of my insights on becoming an effective Data Scientist, and also discussed how to build a successful Data Science practice. Link to the podcast and a downloadable infographic below: https://www.superdatascience.com/podcast-building-successful-data-science-practice-effective-data-scientist/

7 Questions to Ask Before Taking on a Data Science Role

Most of us seasoned Data Scientists have been in the situation where we get excited by a new role, seemingly offering new challenges and learning opportunities, only to be disappointed soon after we begin. We quickly realise that we can’t easily get access to the data we need, that it’s being guarded like some treasure,…