A while back James Kobielus wrote the article, Data Scientist: Consider the Curriculum. It contains one of the best descriptions of a data science curriculum I have seen. Also the article includes a list of algorithms/modeling techniques that should be known by a data scientist. Below is the list from the article.

- linear algebra
- basic statistics
- linear and logistic regression
- data mining
- predictive modeling
- cluster analysis
- association rules
- market basket analysis
- decision trees
- time-series analysis
- forecasting
- machine learning
- Bayesian and Monte Carlo Statistics
- matrix operations
- sampling
- text analytics
- summarization
- classification
- primary components analysis
- experimental design
- unsupervised learning
- constrained optimization

The list almost looks overwhelming.

Do you think anything is missing from the list?

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