The Math and Statistics Behind Data Science
The Math and Statistics Behind Data Science: A Comprehensive Guide To Start Developing your math and statistics skills, are crucial for a data scientist. Here are some of the key mathematical and statistical topics you should focus on: 1. Linear Algebra : Vectors and vector operations Matrices and matrix operations Eigenvalues and eigenvectors Matrix factorization (e.g., Singular Value Decomposition) 2. Calculus : Differentiation and integration Partial derivatives and gradients Optimization techniques (used in training machine learning models) 3. Probability : Basic probability concepts (e.g., probability distributions, conditional probability) Bayes' theorem Random variables and expected values Probability distributions (e.g., normal, binomial, Poisson) 4. Statistics : Descriptive statistics (mean, median, mode, variance, standard deviation, kurtsis, skew, correlation, covariance) Inferential statistics (hypothesis testing, confidence intervals ) Regression analysis (linear and ...