Data Science III
This is a set of questions for a data scientist who pretends for the role of a senior level or a tech lead in a consulting or offshore development company. It is split into the 10 main parts:
- Logic: proofs and axioms
- CS and Computing: Big Data, Tensorflow, complexity
- Mathematics: advanced analysis, geometry, tensor algebra
- Probability Theory and Statistics: exponential family, applied Bayesian statistics
- Optimization: heuristics, non-differentiable functions, subgradients
- General Predictive Modeling: statistical learning theory
- Machine Learning Algorithms: leveraging basic algorithms for different uses
- Neural Networks and Deep Learning: state of the art for NLP and Computer Vision
- Research and Frontiers: recent advances :)
- Communication and Presentation: business metrics and architecture design
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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Your GitHub, blog, Google Scholar, something you'd like to share with us
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