Data science is a large field and includes many different types of data processing. The most common types of data processing are: Data mining, Data modeling, Machine learning, Data visualization.
Data science is not just a buzzword. It’s a new way of solving problems, from predicting the best time to buy an airplane ticket to determining the risk of a heart attack.
Some topics you can read about here: Scatter plots, correlation, t-test, linear regression, logistic regression
Learning about data science With the increasing popularity of data science, there are now countless online tutorials on machine learning, data science and statistics, Python and R. Most of these tutorials follow the same pattern: learn some basic commands, go through a simple use case, apply some algorithms and discuss Read more…
Something I have been asked in the past is the extend to which data science can be automated. We discussed in a previous post how machine learning and AI are automating other parts of the economy. However, is it possible to actually automated data science? The answer is not a definite ‘yes’, Read more…
The power of recommender systems Recommender systems are a powerful tool that is being used in order to predict the preferences of a user in order to make appropriate suggestions. I’ve met many entrepreneurs that are not aware that recommender systems exist, and this always surprises me. Why? Because if Read more…
Rise of the data science architect I believe there is a new role in data that businesses need to start taking into account, that of the data science architect. What is a data science architect? It is a mix between a data scientist and a data engineer. Data science is (according Read more…
Election forecasting: Trump and brexit In 2016, Donald Trump became the new president of the United States, and what was once improbable, according to election forecasts, became reality. As it happened with Brexit most polls got it completely wrong. The graph below shows the poll history for the 2016 US presidential election. Read more…
Team Data Science Process I have talked in the past in this blog about the need for standardisation of data science and data science processes. Microsoft has made progress on that front. It has released a particular methodology called TDSP (Team Data Science Process). This is an attempt to formalise the way Read more…
I recently uploaded my thesis on arXiv. My thesis, called Predictive Modelling of Football Injuries, was a collaboration with Tottenham Hotspur FC and dealt with the problem of predicting injuries in football. Sports analytics is a relatively new field and there are very few people who are researching the area of injury Read more…
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…
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