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I am preparing a number of online courses for data science training, based on what my personal experience in the field has revealed as a critical set of skills for advanced users. These will cover diverse but crucial topics such as measuring classifier performance, machine learning on graphs, and anomaly detection. Stay tuned by following me on LinkedIn or getting in touch.
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Companies often find themselves recruiting junior data scientists without necessarily having a senior scientist supervising them. This is a reasonable strategy for companies where data science plays a supportive, rather than a central role. Nevertheless, data science is a vast field, with a significant empirical component, and hence inevitably university education cannot possibly prepare junior scientists for all eventualities. Offering tailored single-day or two-day workshops to your data science / quantitative team can be transformative for their performance. I am available to organise and deliver workshops tailored to your business, in any European country. Get in touch.
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Probability theory lies at the very foundation of data science, and much of the difficulty in detecting the signal from the noise in real-world data analysis is down to the often paradoxical nature of probabilistic events. However, formal training in probability theory can be tedious, and even expert users of probabilistic calculus find it cumbersome and time-consuming to use in everyday situations, even when warranted. Instead, we usually resort on our "gut feelings" about random events, which are unfortunately notoriously unreliable. In response to this challenge, some friends and I built a tabletop game that helps train your probabilistic intuition, while simultaneously remaining fun and engaging, and suitable for anyone, including those without any mathematical training. It is named Borel after one of my personal heroes, the famous French mathematician Emile Borel. You can find more information at playborel.com or buy it at Amazon.
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