User:Stuffofstuff/Books/introDS

Intro to data science

 * Data science
 * Probability
 * Probability theory
 * Independence (probability theory)
 * Random variable
 * Expected value
 * Probability density function
 * Probability distribution
 * Joint probability distribution
 * Normal distribution
 * Multivariate normal distribution
 * Standard deviation
 * Cumulative distribution function
 * Variance
 * Covariance
 * Confusion matrix
 * Receiver operating characteristic
 * Covariance and correlation
 * Central limit theorem
 * K-nearest neighbors algorithm
 * Linear model
 * Coefficient of determination
 * Linear regression
 * Dimensionality reduction
 * Kernel method
 * Regression analysis
 * Least squares
 * Hyperplane separation theorem
 * Support vector machine
 * Hyperplane
 * Nearest centroid classifier
 * Mathematical optimization
 * Feature (machine learning)
 * Quantization (signal processing)
 * Vector quantization
 * Naive Bayes classifier
 * Naive Bayes spam filtering
 * Graph theory
 * Misleading graph
 * Tree (graph theory)
 * Hierarchical database model
 * Adjacency list
 * XML database
 * Nested set model
 * Root-finding algorithm
 * Lowest common ancestor
 * Connected component (graph theory)
 * Cluster analysis
 * Principal component analysis
 * Feature selection
 * Feature extraction
 * Big data
 * Deep learning
 * Data visualization
 * Machine learning
 * Statistical classification
 * Correlation and dependence
 * Supervised learning
 * Pattern recognition
 * Matrix (mathematics)
 * Covariance matrix
 * Data mining
 * Linear combination
 * Vector space
 * Dependent and independent variables
 * Real number
 * Errors and residuals