Sobre este Programa de cursos integrados
284,001

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Cronograma flexível

Definição e manutenção de prazos flexíveis.

Nível iniciante

You should have beginner level experience in Python. Familarity with regression is recommended.

Aprox. 8 meses para completar

5 horas/semana sugeridas

Inglês

Legendas: Inglês, Árabe, Francês, Chinês (simplificado), Grego, Italiano, Portuguese (Brazilian), Vietnamita, Russo, Turco, Hebraico, Japonês...

O que você vai aprender

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    Use R to clean, analyze, and visualize data.

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    Navigate the entire data science pipeline from data acquisition to publication.

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    Use GitHub to manage data science projects.

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    Perform regression analysis, least squares and inference using regression models.

Habilidades que você terá

GithubMachine LearningR ProgrammingRegression Analysis

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Cronograma flexível

Definição e manutenção de prazos flexíveis.

Nível iniciante

You should have beginner level experience in Python. Familarity with regression is recommended.

Aprox. 8 meses para completar

5 horas/semana sugeridas

Inglês

Legendas: Inglês, Árabe, Francês, Chinês (simplificado), Grego, Italiano, Portuguese (Brazilian), Vietnamita, Russo, Turco, Hebraico, Japonês...

Como o Programa de cursos integrados funciona

Fazer cursos

Um programa de cursos integrados do Coursera é uma série de cursos para ajudá-lo a dominar uma habilidade. Primeiramente, inscreva-se no programa de cursos integrados diretamente, ou avalie a lista de cursos e escolha por qual você gostaria de começar. Ao se inscrever em um curso que faz parte de um programa de cursos integrados, você é automaticamente inscrito em todo o programa de cursos integrados. É possível concluir apenas um curso — você pode pausar a sua aprendizagem ou cancelar a sua assinatura a qualquer momento. Visite o seu painel de aprendiz para controlar suas inscrições em cursos e progresso.

Projeto prático

Todos os programas de cursos integrados incluem um projeto prático. Você precisará completar com êxito o(s) projeto(s) para concluir o programa de cursos integrados e obter o seu certificado. Se o programa de cursos integrados incluir um curso separado para o projeto prático, você precisará completar todos os outros cursos antes de iniciá-lo.

Obtenha um certificado

Ao concluir todos os cursos e completar o projeto prático, você obterá um certificado que pode ser compartilhado com potenciais empregadores e com sua rede profissional.

how it works

Este Programa de cursos integrados contém 10 cursos

Curso1

As Ferramentas do Cientista de Dados

4.5
19,078 classificações
3,825 avaliações
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....
Curso2

Linguagem R

4.6
14,077 classificações
2,888 avaliações
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....
Curso3

Obtenção e Limpeza de Dados

4.6
5,992 classificações
931 avaliações
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....
Curso4

Análise Exploratória de Dados

4.7
4,564 classificações
653 avaliações
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....
Curso5

Pesquisa Reprodutível

4.5
3,166 classificações
455 avaliações
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results....
Curso6

Inferência estatística

4.2
3,201 classificações
632 avaliações
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data....
Curso7

Modelos Regressivos

4.4
2,557 classificações
439 avaliações
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing....
Curso8

Aprendizagem Automática na Prática

4.5
2,464 classificações
463 avaliações
One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation....
Curso9

Desenvolvimento de dados

4.5
1,705 classificações
326 avaliações
A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience....
Curso10

Ciência de Dados

4.5
829 classificações
220 avaliações
The capstone project class will allow students to create a usable/public data product that can be used to show your skills to potential employers. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners....

Instrutores

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Jeff Leek, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health
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Roger D. Peng, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health
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Brian Caffo, PhD

Professor, Biostatistics
Bloomberg School of Public Health

Parceiros do setor

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Sobre Universidade 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....

Perguntas Frequentes – FAQ

  • Sim! Para começar, clique na carta de curso que lhe interessa e se inscreva. Você pode se inscrever e concluir o curso para ganhar um certificado compartilhável ou você pode auditar para ver os materiais do curso de graça. Quando você se inscrever em um curso que faz parte de uma especialização, você está automaticamente inscrito para a especialização completa. Visite o seu painel de aluno para acompanhar o seu progresso.

  • Este curso é totalmente on-line, então não existe necessidade de aparecer em uma sala de aula pessoalmente. Você pode acessar suas palestras, leituras e atribuições a qualquer hora e qualquer lugar, via web ou dispositivo móvel.

  • Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 3-6 months.

  • Each course in the Specialization is offered monthly.

  • 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).

  • 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.

  • 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.

  • You’ll have a foundational understanding of the field and be prepared to continue studying data science.

  • 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.

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.