I do research in statistics with the focus on causality, MCMC methods and modeling. My job focuses on machine learning in marketing and teaching professionals and students to understand and enjoy data. Oh, and I also write sometimes.
My focus on my alma mater focuses on bayesian causality through Direct acyclic graphs (DAGs), Bayesian networks and state-space models. Secondary goal is the popularization of bayesian techniques which can offer significant advantages compared to standard frequentistic methods, such as the null hypothesis significance (NHST) framework.
I have extensive experience teaching data science, statistics, and programming at university level and in professional workshops.
Resources for my teaching courses:
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A few extracts from student reviews from my tutoring on doucuji.eu, courses lead on Coders Lab and Data Academy.
When it comes to statistical analysis, unbiased estimators play a critical role in obtaining accurate results. But what exactly is an unbiased estimator, and how can it be applied in practical situations?
Consistent estimators hold a pivotal role in delivering precise statistical outcomes. Let's unravel their mystery and reveal how they shape real-world prediction scenarios.
