All in one picture data science central tutorial at one place

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.