Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data.
Este curso forma parte de Programa Especializado - Ciencia de Datos
Getting and Cleaning Data
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Acerca de este Curso
103,841 vistas recientes
Qué aprenderás
Apply data cleaning basics to make data "tidy"
Obtain usable data from the web, APIs, and databases
Understand common data storage systems
Use R for text and date manipulation
Habilidades que obtendrás
Data ManipulationRegular Expression (REGEX)R ProgrammingData Cleansing
Los estudiantes que toman este Course son
- Data Analysts
- Geologists
- Biostatisticians
- Risk Managers
- Data Scientists
Programa - Qué aprenderás en este curso
Semana
12 horas para completar
Week 1
In this first week of the course, we look at finding data and reading different file types.
9 videos (Total 67 minutos), 4 lecturas, 1 cuestionario
9 videos
Raw and Processed Data7m
Components of Tidy Data9m
Downloading Files7m
Reading Local Files4m
Reading Excel Files3m
Reading XML12m
Reading JSON5m
The data.table Package11m
4 lecturas
Welcome to Week 110m
Syllabus10m
Pre-Course Survey10m
Practical R Exercises in swirl Part 110m
1 ejercicio de práctica
Week 1 Quiz10m
Semana
21 hora para completar
Week 2
Welcome to Week 2 of Getting and Cleaning Data! The primary goal is to introduce you to the most common data storage systems and the appropriate tools to extract data from web or from databases like MySQL.
5 videos (Total 41 minutos), 1 cuestionario
5 videos
Reading from HDF56m
Reading from The Web6m
Reading From APIs7m
Reading From Other Sources4m
1 ejercicio de práctica
Week 2 Quiz10m
Semana
310 horas para completar
Week 3
Welcome to Week 3 of Getting and Cleaning Data! This week the lectures will focus on organizing, merging and managing the data you have collected using the lectures from Weeks 1 and 2.
7 videos (Total 60 minutos), 1 lectura, 4 cuestionarios
7 videos
Summarizing Data11m
Creating New Variables10m
Reshaping Data9m
Managing Data Frames with dplyr - Introduction3m
Managing Data Frames with dplyr - Basic Tools12m
Merging Data6m
1 lectura
Practical R Exercises in swirl Part 210m
1 ejercicio de práctica
Week 3 Quiz10m
Semana
46 horas para completar
Week 4
Welcome to Week 4 of Getting and Cleaning Data! This week we finish up with lectures on text and date manipulation in R. In this final week we will also focus on peer grading of Course Projects.
5 videos (Total 34 minutos), 2 lecturas, 3 cuestionarios
5 videos
Regular Expressions I5m
Regular Expressions II8m
Working with Dates6m
Data Resources3m
2 lecturas
Practical R Exercises in swirl Part 410m
Post-Course Survey10m
1 ejercicio de práctica
Week 4 Quiz10m
42%
comenzó una nueva carrera después de completar estos cursos
38%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Principales revisiones sobre Getting and Cleaning Data
This course is really a challenging and compulsory for any one who wants to be a data scientist or working in any sort of data. It teaches you how to make very palatable data-set fro ma messy data.
Easy, mostly instructive Course. The Assignments and quizzes are quite good, and illustrates the lessons very well.\n\nSee the videos for general presentation, but use the energy on the excersizes.
Instructores
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Acerca de Universidad Johns Hopkins
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Acerca de Programa especializado Ciencia de Datos
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
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