Data Science II
This is a set of questions for a data scientist who pretends for the role of a middle level or independent specialist in a consulting or offshore development company. It is split into the 10 main parts:
- Logic: proofs, fuzzy logic
- CS and Computing: databases, NumPy and OOP
- Mathematics: applications of calculus and algebra in algorithms
- Probability Theory and Statistics: Bayesian statistics and applied skills
- Optimization: numerical algorithms and deeper theory
- General Predictive Modeling: how to choose losses, metrics and underlying priors
- Machine Learning Algorithms: mathematics, boosting, bias-variance tradeoff
- Neural Networks and Deep Learning: regularization, CNNs and RNNs
- Research and Frontiers: classical workpieces
- Communication and Presentation: ML metrics vs business metrics
Don't feel overwhelmed with the number of questions, most of them are easy and there is always a chance to guess :)
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