This chapter kicks the course off by reviewing conditional probabilities, Bayes' theorem, and central limit theorem. Along the way, you will learn how to handle questions that work with commonly referenced probability distributions.

Conditional probabilities

Setting up problems

Bayes' theorem applied

Central limit theorem

Samples from a rolled die

Simulating central limit theorem

Probability distributions

Bernoulli distribution

Binomial distribution

Normal distribution

Probability and Sampling Distributions

In this chapter, you will prepare for statistical concepts related to exploratory data analysis. The topics include descriptive statistics, dealing with categorical variables, and relationships between variables.  The exercises will prepare you for an analytical assessment or stats-based coding question.

Descriptive statistics

Mean or median

Standard deviation by hand

Categorical data

Encoding techniques

Exploring laptop prices

Two or more variables

Types of relationships

Pearson correlation

Sensitivity to outliers

Exploratory Data Analysis

Prepare to dive deeper into crucial concepts regarding experiments and testing by reviewing confidence intervals, hypothesis testing, multiple tests, and the role that power and sample size play. We'll also discuss types of errors, and what they mean in practice.

Intervalos de confianza

Intervalo de confianza a mano

Aplicar intervalos de confianza

Pruebas de hipótesis

Prueba z de una cola

t-test de dos colas

Potencia y tamaño de muestra

Efecto sobre el error de tipo II

Calcular el tamaño de la muestra

Visualizar la relación

Pruebas múltiples

Calculating error rates

Corrección de Bonferroni

Statistical Experiments and Significance Testing

Wrapping up, we'll address concepts related closely to regression and classification models. The chapter begins by reviewing fundamental machine learning algorithms and quickly ramps up to model evaluation, dealing with special cases, and the bias-variance tradeoff. 

Regression models

Linear regression

Logistic regression

Evaluating models

Regression evaluation

Classification evaluation

Missing data and outliers

Handling null values

Identifying outliers

Bias-variance tradeoff

Test and training error

Visualizing the tradeoff

Wrapping up

Regression and Classification

¿Quieres conseguir tu próximo trabajo o pulir tus habilidades para entrevistas de estadística y mantenerte en forma? Prepárate para dominar conceptos clásicos de entrevista, desde probabilidades condicionales hasta pruebas A/B y el compromiso sesgo-varianza, ¡y mucho más! Trabajarás con una colección diversa de conjuntos de datos, incluidos resultados de experimentos web y datos meteorológicos de Australia. Al finalizar el curso, podrás ir con confianza a tu próxima entrevista y afrontar cualquier pregunta de estadística con la ayuda de Python.

Hypothesis Testing in Python

Supervised Learning with scikit-learn

Prepárate para tu entrevista de estadística revisando probabilidades, pruebas A/B y sesgo-varianza.

Practicando preguntas de entrevistas de estadística en Python

Efecto sobre el error de tipo II

Practicando preguntas de entrevistas de estadística en Python

ejercicio interactivo práctico