AI and the new economy A topic that has come up quite recently in public discussion is the changes that Artificial Intelligence (AI) is bringing to the global economy. Increased automation is overtaking jobs and this trend is only going to go up. …
Sports analytics remains one of my favourite topics, as I have both done research in this area, but also released courses for the public. One of my favourite pieces of research, was when I researched into whether Twitter can be …
Human-level concept learning I am a huge fan of Joshua Tenenbaum and his research. Recently, he published in collaboration with Brenden Lake and Ruslan Salakhutdinov a new piece of research regarding “Bayesian Program Learning“. The paper is actually entitled “Human-level concept learning through probabilistic …
Marvin minsky: one of the fathers of artificial intelligence Marvin Minsky (who passed away in 2016) was one of the founding fathers of Artificial Intelligence. Some of his achievements include a Turing Prize (awarded in 1969) include major contributions to AI, …
Deep neural networks can be complicated to understand, train and use. Deep learning is still, to a large extent, an experimental science. This is why getting some input on the best practices can be vital in making the most out …
The differences between machine learning and statistics Machine learning and statistics are the two core disciplines for data analysis. Both fields provide the scientific background for data science and data scientists will usually have trained in one of the two. …
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 …
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.
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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“.
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