This course introduces the key steps involved in the data mining pipeline, including data understanding, data preprocessing, data warehousing, data modeling, interpretation and evaluation, and real-world applications.
Este curso forma parte de Programa especializado: Data Mining Foundations and Practice
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ofrecido por
Programa especializado: Data Mining Foundations and PracticeUniversidad de Colorado en BoulderAcerca de este Curso
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Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 3 en
Nivel intermedio
data science professionals or domain experts, some experience working with data
Aprox. 21 horas para completar
Inglés (English)
SubtÃtulos: Inglés (English)
Qué aprenderás
B​y the end of this course, you will be able to identify the key components of the data mining pipeline ​and describe how they're related.
Y​ou will be able to identify particular challenges presented by each component of the data mining pipeline.
Y​ou will be able to apply techniques to address challenges in each component of the data mining pipeline.
Habilidades que obtendrás
- Data Pre-Processing
- Data Warehousing
- data understanding
- data mining pipeline
Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 3 en
Nivel intermedio
data science professionals or domain experts, some experience working with data
Aprox. 21 horas para completar
Inglés (English)
SubtÃtulos: Inglés (English)
ofrecido por
Universidad de Colorado en Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
Comienza a trabajar para obtener tu maestrÃa
Este curso es parte del Master of Science in Data Science completamente en lÃnea de Universidad de Colorado en Boulder.
Si te aceptan en el programa completo, tus cursos cuentan para tu tÃtulo de grado.
Programa - Qué aprenderás en este curso
6 horas para completar
Data Mining Pipeline
This module provides an introduction to data mining and data mining pipeline, including the four views of data mining and the key components in the data mining pipeline.
6 horas para completar
2 videos (Total 88 minutos), 3 lecturas, 2 cuestionarios
5 horas para completar
Data Understanding
This module covers data understanding by identifying key data properties and applying techniques to characterize different datasets.
5 horas para completar
2 videos (Total 71 minutos)
5 horas para completar
Data Preprocessing
This module explains why data preprocessing is needed and what techniques can be used to preprocess data.
5 horas para completar
2 videos (Total 77 minutos)
5 horas para completar
Data Warehousing
This module covers the key characteristics of data warehousing and the techniques to support data warehousing.
5 horas para completar
2 videos (Total 54 minutos)
Acerca de Programa especializado: Data Mining Foundations and Practice
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project.
Preguntas Frecuentes
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