Performance Measures in Predictive Modelling

Issues with performance measures in machine learning When testing a predictive model, choosing the correct performance measure is imperative for making sure our model works correctly. In machine learning literature, however, it is common to use measures because they have always …

Engineering can be of different types

Data Science Protocols

Standardising data science

One of the main problems in data science practice is the lack of standardisation regarding procedures and techniques. Coming out of education and moving into the industry you can find yourself with knowledge of various methods and approaches, but no clear guide on best practices. Indeed, data science still largely remains an endeavour largely based on intuition and personal experience.

In other engineering disciplines there are standards to ensure the quality of the final result. Of course, data science is different to engineering disciplines such as civil engineering or computer engineering, where the final output is a physical product. Data science is closer to software engineering, where the lack of physical components means there are smaller  construction costs, and considerably more room to experiment and try different things out.


The data scientist: Welcome!

My name is Stylianos (Stelios) Kampakis and I am a data scientist.

I have been academically active in the area of data science for around 5 years, and 3 years as a professional. Coming from a diverse background (that includes soft sciences such as Psychology and music studies, to a PhD in Computer Science and a BSc in Mathematics and Statistics) has given me a unique perspective in the world of data science.

Throughout my career my main drives has been scientific curiosity and the search for knowledge and objective truth. In our age, the best method by far we’ve come up with until now is data analysis. The different perspectives on the use of data (statistics, machine learning, data mining, qualitative methods, etc.) gave rise to the term “data science“.