Big Data Knowledge Repos »
A prevailing characteristic of data scientists is deep intellectual curiosity a trait that drives them to be passionate learners, always picking up new skills on their own volition.
Many of these fascinating but difficult techniques of data science are grounded in hard math and machine learning e.g. Bayesian inference, nonparametric regression, neural net classifiers, hidden markov models, evolutionary algorithms, content/collaborative filters, NLP, etc. Data science is so broad and deep that even the most seasoned experts always have something new to learn; there is simply too much collective knowledge out there.
The purpose of the "Data Science Knowledge Repo" is to provide a central resource that data scientists can revisit frequently to refresh knowledge or learn new skills. If you have any recommended additions guides, technical papers, and other resources email email@example.com.
Decision Tree Learning
Fixed Effects Models
Hidden Markov Models
Hierarchical Bayes Models
Independent Component Analysis (ICA)
Linear Discriminant Analysis (LDA)
Markov Chain Monte Carlo (MCMC)
Natural Language Processing (NLP)
Principal Component Analysis (PCA)
R (Statistical Computing Software)
SAS (Statistical Computing Software)
Singular Value Decomposition (SVD)
Support Vector Machines (SVM)
Newcomers to data?