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This document provides concise visual explanations of various math and data science concepts in one picture each. These concepts include deep learning vs machine learning, traditional programming vs machine learning, p tests, support vector machines, logistic regression, regression analysis, naive bayes, bayes theorem, statistics and machine learning, correlation coefficients, r-square metrics, evaluation metrics, type 1 and 2 errors, comparing and ensemble datasets, parametric and non-parametric analyses, determining sample size, ROC curves, z-tests and t-tests, ANOVA, predictive analytics, time series methods, cross validation, confidence intervals, unsupervised learning, KNN, number of clusters selection, AB testing, EM algorithm, and number representation systems.Read less