![]() He was also one of the first researchers in the area of blockchain to study and discuss tokenomics. He is a member of the Royal Statistical Society, honorary research fellow at the UCL Centre for Blockchain Technologies, a data science advisor for London Business School and CEO of The Tesseract Academy. He has also helped many people follow a career in data science and technology. He has worked with many different types of technologies, from statistical models, to deep learning to blockchain and he has 2 patents pending to his name. His work expands multiple sectors including fintech (fraud detection and valuation models), sports analytics, health-tech, general AI, medical statistics, predictive maintenance and others. He has worked with decision makers from companies of all sizes: from startups to organisations like, the US Navy, Vodafone and British Land. If you want to learn more book a call with my team now or get in touch.ĭr Stylianos (Stelios) Kampakis is a data scientist with more than 10 years of experience. We apply to jobs for you and we help you land a job, by preparing you for interviews (value at $50k+ per year).Get premium mentoring (value at $1k/hour).Learn all the basics of data science (value $10k+).Do you want to become data scientist?ĭo you want to become a data scientist and pursue a lucrative career with a high salary, working from anywhere in the world? I have developed a unique course based on my 10+ years of teaching experience in this area. Using the concept of quality assurance testing in data science could go a long way towards improving the final outcome and reducing the risk of model-based decision making that is inherent in predictive analytics. It also entails a proper understanding of the underlying business problem and reporting results. In domains, like finance, these kind of mistakes can cost lots of money.īeing a data scientist entails more than just being good in statistics or machine learning. Quite often, these problems go unnoticed for some time, until they are fixed. Maybe you have built a forecasting model for a retailer, but the market has shifted and the model no longer works as it was expected. This term describes the case where the underlying system we are modelling has changed. An error of 100 units could translate to $1m or $10.ĥ) Concept drift. How does the performance translate in economic terms. For example, what is a 99% confidence interval? It could be or. ![]() ![]() Reporting a prediction of let’s say 2000 units sold in the next quarter can be useless without additional information. It is one of the most pervasive issues in machine learning.Ĥ) Not understanding how the model will translate in business terms. This takes place because the model has confused the signal with the noise. Overfitting describes the phenomenon where a machine learning model performs significantly worse in the real world, than when it was trained. Overfitting and underfitting are two common problems in machine learning that we need to guard against. We use cross-validation to guard against that, but only through excessive testing can we be sure of our model’s performance. Think for example how much different the economy and investor behavior was before the 2008 financial crisis and after that. Applying a model built in 2006 to data from 2009 would probably yield wrong results.ģ) Overfitting or underfitting are two other very common problems in machine learning and predictive analytics. The concept that is being modelled has changed due to external factors. We’ve already discussed about this issue in the past in the article about performance measures in predictive modelling.Ģ) Not making sure that the dataset at hand is representative of the real world. To that end, many data scientists fall to these mistakes.ġ) Not choosing an appropriate metric for the task at hand. It’s not just about making sure that the system works properly, but also that it achieves the goal to a certain standard. QA testing has an inherent business component in it.
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