What is a data strategy? Data strategy is the one thing many companies need, especially startups, even if they don’t know it. So, what is a data strategy? How can we define it? A data strategy is defined as the strategy around the collection, storage and usage of a data, in a Read more…
Sports analytics and sports predictions In sports analytics there is quite often discussion about which sports are easier to predict. The truth is that there is not a clear answer. Since this area overlaps with sports betting, there are many companies that are using proprietary predictive models, so we don’t Read more…
Supervised PCA is a very useful, but under-utilised, model.There are many cases in machine learning where we deal with a large number of features. There are many ways to deal with this problem. If we suspect that many of these features are useless, then we can apply feature selection techniques such as: Univariate 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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