Saltar al contenido principal
InicioPythonCustomer Analytics and A/B Testing in Python

Customer Analytics and A/B Testing in Python

Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.

Comience El Curso Gratis
4 horas16 vídeos49 ejercicios31.152 aprendicesTrophyDeclaración de cumplimiento

Crea Tu Cuenta Gratuita

GoogleLinkedInFacebook

o

Al continuar, acepta nuestros Términos de uso, nuestra Política de privacidad y que sus datos se almacenan en los EE. UU.
Group

¿Entrenar a 2 o más personas?

Pruebe DataCamp para empresas

Preferido por estudiantes en miles de empresas


Descripción del curso

The most successful companies today are the ones that know their customers so well that they can anticipate their needs. Customer analytics and in particular A/B Testing are crucial parts of leveraging quantitative know-how to help make business decisions that generate value. This course covers the ins and outs of how to use Python to analyze customer behavior and business trends as well as how to create, run, and analyze A/B tests to make proactive, data-driven business decisions.
Empresas

Group¿Entrenar a 2 o más personas?

Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más
Pruebe DataCamp Para EmpresasPara obtener una solución a medida, reserve una demostración.

En las siguientes pistas

Análisis de marketing en Python

Ir a la pista
  1. 1

    Key Performance Indicators: Measuring Business Success

    Gratuito

    This chapter provides a brief introduction to the content that will be covered throughout the course before transitioning into a discussion of Key Performance Indicators or KPIs. You'll learn how to identify and define meaningful KPIs through a combination of critical thinking and leveraging Python tools. These techniques are all presented in a highly practical and generalizable way. Ultimately these topics serve as the core foundation for the A/B testing discussion that follows.

    Reproducir Capítulo Ahora
    Course introduction and overview
    50 xp
    Understanding the key components of an A/B test
    50 xp
    Defining meaningful KPIs
    50 xp
    Identifying and understanding KPIs
    50 xp
    Loading & examining our data
    100 xp
    Merging on different sets of fields
    100 xp
    Exploratory analysis of KPIs
    50 xp
    Practicing aggregations
    100 xp
    Grouping & aggregating
    100 xp
    Calculating KPIs - a practical example
    50 xp
    Calculating KPIs
    100 xp
    Average purchase price by cohort
    100 xp
  2. 2

    Exploring and Visualizing Customer Behavior

    This chapter teaches you how to visualize, manipulate, and explore KPIs as they change over time. Through a variety of examples, you'll learn how to work with datetime objects to calculate metrics per unit time. Then we move to the techniques for how to graph different segments of data, and apply various smoothing functions to reveal hidden trends. Finally we walk through a complete example of how to pinpoint issues through exploratory data analysis of customer data. Throughout this chapter various functions are introduced and explained in a highly generalizable way.

    Reproducir Capítulo Ahora
  3. 3

    The Design and Application of A/B Testing

    In this chapter you will dive fully into A/B testing. You will learn the mathematics and knowledge needed to design and successfully plan an A/B test from determining an experimental unit to finding how large a sample size is needed. Accompanying this will be an introduction to the functions and code needed to calculate the various quantities associated with a statistical test of this type.

    Reproducir Capítulo Ahora
  4. 4

    Analyzing A/B Testing Results

    After running an A/B test, you must analyze the data and then effectively communicate the results. This chapter begins by interleaving the theory of statistical significance and confidence intervals with the tools you need to calculate them yourself from the data. Next we discuss how to effectively visualize and communicate these results. This chapter is the culmination of all the knowledge built over the entire course.

    Reproducir Capítulo Ahora
Empresas

Group¿Entrenar a 2 o más personas?

Obtenga acceso de su equipo a la biblioteca completa de DataCamp, con informes centralizados, tareas, proyectos y más

En las siguientes pistas

Análisis de marketing en Python

Ir a la pista

conjuntos de datos

Customer datasetIn-App Purchases datasetDaily Revenue datasetUser Demographics Paywall datasetAB Testing Results

colaboradores

Collaborator's avatar
Lore Dirick
Collaborator's avatar
Yashas Roy
Collaborator's avatar
Eunkyung Park
Ryan Grossman HeadshotRyan Grossman

Data Scientist at EDO Inc.

Ver Más

¿Qué tienen que decir otros alumnos?

¡Únete a 14 millones de estudiantes y empieza Customer Analytics and A/B Testing in Python hoy mismo!

Crea Tu Cuenta Gratuita

GoogleLinkedInFacebook

o

Al continuar, acepta nuestros Términos de uso, nuestra Política de privacidad y que sus datos se almacenan en los EE. UU.