Technical posts on data science, Python, AI, machine learning and blockchain.
Machine learning, artificial intelligence and blockchain are all the rage in the tech world today. Data Scientist has written a number of technical posts on these topics. We have compiled a list of the most popular ones below.
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 used to predict football outcomes. The article can be found Read more…
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, cognitive science, mathematics and robotics. Technology review posted a short Read more…
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 of the capabilities that neural networks offer. This article presents Read more…
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. However, much has been said about the differences between the Read more…
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 been used in the past, without really judging whether they Read more…
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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