User:Lapark/Books/Data Science Essentials

Topics for a Data Science Degree

 * Data science
 * Design of experiments
 * Machine learning
 * Deep learning


 * Visualisation and Dimension Reduction
 * Infographic
 * Data visualization
 * Information visualization
 * Statistical model
 * Data
 * Errors and residuals
 * Multidimensional scaling
 * Principal component analysis


 * Statistics and Probability
 * Mean
 * Random variable
 * Normal distribution
 * Expected value
 * Independence (probability theory)
 * Probability distribution
 * Analysis of variance


 * Regression
 * Linear regression
 * Regression analysis
 * Logistic regression
 * Regularization (mathematics)
 * Tikhonov regularization
 * Lasso (statistics)
 * Boosting (machine learning)
 * Bootstrap aggregating
 * AdaBoost
 * Cross-validation (statistics)
 * Reinforcement learning
 * Graphical model


 * Classification
 * Decision tree
 * Random forest
 * Support-vector machine
 * Recurrent neural network
 * Convolutional neural network


 * Sequences and Series
 * Time series
 * Proportional hazards model
 * Social network
 * Text mining
 * Markov chain
 * Arrow's impossibility theorem
 * Kemeny–Young method
 * Pairwise comparison
 * Hidden Markov model
 * Dynamic time warping
 * Fourier transform
 * Wavelet transform
 * Bradley–Terry model