Ask the right questions, manipulate data sets, and create visualizations to communicate results.
272,083 ya inscrito
ofrecido por
#1 Specialization
Acerca de este Programa Especializado
324,570 vistas recientes
Resultados profesionales del estudiante
43%
Comenzaste una nueva carrera profesional después de completar este programa especializado.
19%
Conseguiste un aumento de sueldo o ascenso.
Qué aprenderás
Use R to clean, analyze, and visualize data.
Navigate the entire data science pipeline from data acquisition to publication.
Use GitHub to manage data science projects.
Perform regression analysis, least squares and inference using regression models.
Habilidades que obtendrás
GithubMachine LearningR ProgrammingRegression Analysis
Resultados profesionales del estudiante
43%
Comenzaste una nueva carrera profesional después de completar este programa especializado.
19%
Conseguiste un aumento de sueldo o ascenso.
Cursos 100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Cronograma flexible
Establece y mantén fechas de entrega flexibles.
Nivel principiante
You should have beginner level experience in Python. Familarity with regression is recommended.
Aprox. 8 meses para completar
Sugerido 6 horas/semana
Inglés (English)
Subtítulos: Inglés (English), Coreano, Árabe (Arabic), Francés (French), Chino (simplificado), Portugués (de Brasil), Vietnamita, Español (Spanish), Japonés, Ruso (Russian)
Cómo funciona el programa especializado
Toma cursos
Un programa especializado de Coursera es un conjunto de cursos que te ayudan a dominar una aptitud. Para comenzar, inscríbete en el programa especializado directamente o échale un vistazo a sus cursos y elige uno con el que te gustaría comenzar. Al suscribirte a un curso que forme parte de un programa especializado, quedarás suscrito de manera automática al programa especializado completo. Puedes completar solo un curso: puedes pausar tu aprendizaje o cancelar tu suscripción en cualquier momento. Visita el panel principal del estudiante para realizar un seguimiento de tus inscripciones a cursos y tu progreso.
Proyecto práctico
Cada programa especializado incluye un proyecto práctico. Necesitarás completar correctamente el proyecto para completar el programa especializado y obtener tu certificado. Si el programa especializado incluye un curso separado para el proyecto práctico, necesitarás completar cada uno de los otros cursos antes de poder comenzarlo.
Obtén un certificado
Cuando completes todos los cursos y el proyecto práctico, obtendrás un Certificado que puedes compartir con posibles empleadores y tu red profesional.
Hay 10 cursos en este Programa Especializado
The Data Scientist’s Toolbox
22,433 calificaciones
In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.
Programación R
16,160 calificaciones
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
Getting and Cleaning Data
6,762 calificaciones
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.
análisis exploratorio de datos
5,145 calificaciones
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.
ofrecido por
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.
Revisiones
Principales revisiones sobre CIENCIA DE DATOS
por WHFeb 2, 2016
"R Programming" forces you to dive in deep.\n\nThese skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!
por AKJul 13, 2016
It's a very introductory course and in a sense I don't feel like I learnt something useful, except the part that shows how to install all the tools that are needed for the rest of the Specialization.
por GOJul 25, 2016
Consistent yet very basic course. I would only recommend this course if you are willing to complete the whole Data Science specialization or if you have troubles with the basic functioning of GitHub.
por SSMar 28, 2017
Wow i finally managed to finish the specialization!! definitely learned a lot and also found out difficulties in building predictors by trying to balancing speed, accuracy and memory constraints!!!
por WNAug 19, 2018
A very short (could be completed in a day) course to get you started on the rest of the Specialization. You will learn very basics of installing R and playing around with GitHub. That's it really.
por JCMay 4, 2016
Compared to the other classes in the JHU Data Science specialization, this one is pretty laid back. It's useful information, and teaches a few nice tricks on how to present data analysis results.
por ASAug 26, 2017
A very easy course, can be completed in 3-4 days at max. Having done R programming course first, this was a breeze taking a couple days. Good for people starting off with the specialization.
por MPJan 12, 2016
The course is primarily aimed to get you primed and set-up for the other courses in the Data Science Specialization. This is a fairly lightweight course and can be completed pretty quickly.
Preguntas Frecuentes
¿Cuál es la política de reembolsos?
¿Puedo inscribirme en un solo curso?
¡Sí! Para empezar, haz clic en la tarjeta del curso que te interesa e inscríbete. Puedes inscribirte y completar el curso para obtener un certificado que puedes compartir o puedes acceder al curso como oyente para ver los materiales del curso de manera gratuita. Cuando cancelas la suscripción de un curso que forma parte de un programa especializado, se cancela automáticamente la suscripción de todo el programa especializado. Visita el panel del estudiante para realizar un seguimiento de tu progreso.
¿Hay ayuda económica disponible?
¿Puedo tomar este curso de manera gratuita?
¿Este curso es 100 % en línea? ¿Necesito asistir a alguna clase en persona?
Este curso es completamente en línea, de modo que no necesitas ir a un aula en persona. Puedes acceder a tus lecciones, lecturas y tareas en cualquier momento y cualquier lugar a través de Internet o tu dispositivo móvil.
¿Cuánto tiempo se necesita para completar un programa especializado?
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 3-6 months.
¿Con qué frecuencia se ofrece cada curso del programa especializado?
Each course in the Specialization is offered monthly.
What background knowledge is necessary?
Some programming experience (in any language) is recommended. We also suggest a working knowledge of mathematics up to algebra (neither calculus or linear algebra are required).
Do I need to take the courses in a specific order?
Begin by taking The Data Scientist's Toolbox and Introduction to R Programming, in order. The other courses may be taken in any order, and in parallel if desired.
¿Recibiré crédito universitario por completar el programa especializado?
Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
What will I be able to do upon completing the Specialization?
You’ll have a foundational understanding of the field and be prepared to continue studying data science.
Can I sign up for the course without paying or applying for financial aid?
Yes, you can access the course for free via www.coursera.org/jhu. This will allow you to explore the course, watch lectures, and participate in discussions for free. To be eligible to earn a certificate, you must either pay for enrollment or qualify for financial aid.
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