El Arte del A/B Testing

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Este curso es un programa en línea completo y práctico diseñado para dotar a las personas de los conocimientos y habilidades necesarios para realizar pruebas A/B eficaces. En este curso, los participantes aprenderán los principios fundamentales de las pruebas A/B, incluido el diseño experimental, la determinación del tamaño de la muestra, la comprobación de hipótesis y el análisis estadístico.

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Intermediate

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31 chapters

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What you will learn

Descubre la ciencia detrás de las pruebas A/B, dominando técnicas para tomar decisiones informadas con base en evidencia estadística.
Adquiere la habilidad de diseñar experimentos robustos, asegurando la validez y fiabilidad de tus pruebas.
Desarrolla habilidades para calcular correctamente tamaños de muestra y evitar errores comunes en la comprobación de hipótesis.
Aprende a interpretar los resultados de las pruebas con precisión, transformando datos en conocimientos empresariales accionables.

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There are 6 modules in this course

Bienvenido al curso de A/B testing. En esta sección, le contaremos qué son los experimentos controlados, dónde se utilizan y por qué se utilizan. Veremos un ejemplo concreto de experimento controlado y hablaremos de A/A testing. El lenguaje de programación Python nos ayudará con esto. Bueno, ¡empecemos!

Antes de realizar un experimento controlado, debemos determinar las características de la muestra. De estas características dependerá el tipo de pruebas que realizaremos un poco más adelante. Aprenderás a construir histogramas de distribución, gráficos de caja y a comprobar la normalidad de la distribución. ¡Empecemos ahora mismo!

Lo último que hay que hacer antes de realizar un experimento controlado es comprobar las varianzas muestrales. Algunos tipos de gráficos y la prueba de Levene nos ayudarán a ello. Hablemos de ello con más detalle en esta sección.

Así llegamos a las pruebas A/B. Pronto harás tus primeras pruebas paramétricas, o como también se les llama - pruebas T. Utilizaremos todos los conocimientos que hemos aprendido en las secciones anteriores: definición de métricas, pruebas de normalidad, pruebas de varianza y trazado de gráficos. Todo esto nos ayudará a obtener resultados estadísticamente significativos. Bueno, ¡a trabajar!

Resulta que a veces las muestras no tienen una distribución normal. Esto significa que no podemos realizar pruebas paramétricas. ¿Qué hacer en tal situación? Pruebas no paramétricas. Hablaremos de él en la última sección.

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Why people choose Codefinity for their career

Kwizera Mugisha

The teaching methodology at Codefinity is excellent, and I particularly appreciate how it has prepared me to handle real-world coding problems. Currently, I am delving into Node.js and eagerly anticipate building full-stack projects that integrate all the knowledge I have gained.

Sherry Barnes-Fox

My first course was 4 hours, I did it in a few days, "nugget-style. The instructions are very clear and easy to understand. There is even a hint to help you get the answer, and if you still cannot get the answer, then you can display the answer. I love the learning style that is used, it engages me.

Bill Wagner

I have really liked the browser-based lessons that allow me to code within the lesson. The RUN button allows me to test the code I write before submitting for a grade.

Stephanie Chan

As I went through the first course of the Python track, I liked the way the course was lay out (in easy and digestible modules) with little exercises at the end of each concept.

Daniel Chinea

I have gained a lot of practical and logical thinking skills, along with patience for myself and confidence in myself that I can learn programming.

Steve Bruening

The learning was progressive and made it easy to follow along and make progress. I could feel my skills increasing and building on each other as the course went along.

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Haitam Kadri

Reviewed on Mar 3, 2024

amazing and practical course

Recommended if you're interested in learning Python

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