El objetivo del curso es lograr que los estudiantes apliquen los principios básicos, las características de la Analítica y ciencias de datos así como las etapas en el proceso de desarrollo de un proyecto de análisis de datos. Los estudiantes también se familiarizarán con el preprocesamiento de datos y serán capaces de manejar a, nivel práctico, las principales técnicas componentes de este, como el análisis de faltantes, la detección de outliers, las técnicas de normalización, discretización y reducción dimensional.
Analítica y ciencia de datos para negocios
Analítica y ciencia de datos para negocios
Instructor: Eduardo Carbajal López
3,608 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.4
(40 reviews)
Beginner level
No prior experience required
20 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Aplicación de principios básicos y características de la Analítica y Ciencias de Datos en el desarrollo de proyectos de análisis de datos.
Manejo de técnicas de preprocesamiento de datos, como detección de outliers y normalización.
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Assessments
21 assignments
Taught in Spanish
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There are 5 modules in this course
Esta sección presenta los conceptos esenciales para poder desarrollar el curso con éxito. Te invitamos a revisarlo todas las veces que lo consideres necesario.
What's included
24 videos1 reading5 assignments
En este módulo, presentaré las principales técnicas de business analytics, a partir de una revisión general del universo de técnicas de analítica de datos y cuáles son la s más comúnmente usadas en los modelos de analítica aplicados en negocios.
What's included
20 videos2 readings4 assignments
En este Módulo se desarrolla el preprocesamiento de datos que corresponde a la etapa más compleja y de mayor duración de tiempo dentro de un proyecto de analítica de negocios. En particular, abordaremos todo lo referente al problema de los datos faltantes y las opciones de tratamiento frente a dicho problema.
What's included
19 videos2 readings6 assignments
En este módulo, continuaremos aprendiendo sobre la etapa del preprocesamiento, pero ahora nos enfocaremos en la detección y el tratamiento de los valores atípicos o extremos, llamados también outliers.
What's included
13 videos2 readings3 assignments
En este último módulo se continuará explicando la etapa del preprocesamiento, pero ahora nos enfocaremos en las técnicas de transformación y la reducción dimensional de datos.
What's included
23 videos3 readings3 assignments
Instructor
Instructor ratings
4.2 (12 ratings)Offered by
Recommended if you're interested in Data Analysis
Universidad Nacional Autónoma de México
Universidad Nacional Autónoma de México
Universidad Nacional Autónoma de México
Universidad de los Andes
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4.4
40 reviews
- 5 stars
65%
- 4 stars
20%
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5%
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Showing 3 of 40
AS
5
Reviewed on May 27, 2024
un curso muy completo para el analisis de datos, aprendes una buena aplicación de R para datos faltantes
EA
5
Reviewed on Feb 24, 2024
Sumamente valioso este curso para los que comenzamos en R, muchas gracias por compartirlo.
HL
5
Reviewed on Jun 6, 2024
Estupendo curso y super recomendado para entender las diferentes técnicas de análisis en R
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