Machine LearningGithubR ProgrammingRegression AnalysisData Visualization (DataViz)StatisticsStatistical InferenceStatistical Hypothesis TestingModel SelectionGeneralized Linear ModelLinear RegressionRandom Forest
oferecido por
Programa de cursos integrados Data Science: Statistics and Machine LearningUniversidade Johns HopkinsO que você vai aprender
Perform regression analysis, least squares and inference using regression models.
Build and apply prediction functions
Develop public data products
Understand the process of drawing conclusions about populations or scientific truths from data
Habilidades que você terá
Sobre este Programa de cursos integrados
Build models, make inferences, and deliver interactive data products.
This specialization continues and develops on the material from the Data Science: Foundations using R specialization. It covers statistical inference, regression models, machine learning, and the development of data products. In the Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, learners will have a portfolio demonstrating their mastery of the material.
The five courses in this specialization are the very same courses that make up the second half of the Data Science Specialization. This specialization is presented for learners who have already mastered the fundamentals and want to skip right to the more advanced courses.
Projeto de Aprendizagem Aplicada
Each course in this Data Science: Statistics and Machine Learning Specialization includes a hands-on, peer-graded assignment. To earn the Specialization Certificate, you must successfully complete the hands-on, peer-graded assignment in each course, including the final Capstone Project.
Resultados de carreira do aprendiz
37%
Começou uma nova carreira depois de completar o Programa de cursos integrados.
19%
Conseguiu um aumento ou uma promoção.
Certificados compartilháveis
Tenha o certificado após a conclusão
Cursos 100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Cronograma flexível
Definição e manutenção de prazos flexíveis.
Nível intermediário
É necessário ter alguma experiência prévia.É necessária alguma experiência prévia.
Approximately 6 months to complete
Suggested pace of 6 hours/week
Inglês
Legendas: Inglês, Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Coreano, Alemão, Russo, Espanhol
Resultados de carreira do aprendiz
37%
Começou uma nova carreira depois de completar o Programa de cursos integrados.
19%
Conseguiu um aumento ou uma promoção.
Certificados compartilháveis
Tenha o certificado após a conclusão
Cursos 100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Cronograma flexível
Definição e manutenção de prazos flexíveis.
Nível intermediário
É necessário ter alguma experiência prévia.É necessária alguma experiência prévia.
Approximately 6 months to complete
Suggested pace of 6 hours/week
Inglês
Legendas: Inglês, Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Coreano, Alemão, Russo, Espanhol
Este Programa de cursos integrados contém 5 cursos
Inferência estatística
4,181 classificações
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.
Modelos Regressivos
3,203 classificações
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.
Aprendizagem Automática na Prática
3,107 classificações
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.
Desenvolvimento de dados
2,177 classificações
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.
oferecido por
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
Vou ganhar créditos universitários por concluir a Especialização?
Can I just enroll in a single course?
Posso me inscrever em um único curso?
Can I take the course for free?
Posso fazer o curso gratuitamente?
Este curso é realmente 100% on-line? Eu preciso assistir alguma aula pessoalmente?
Quanto tempo é necessário para concluir a Especialização?
Do I need to take the courses in a specific order?
Will I earn university credit for completing the Specialization?
Vou ganhar créditos universitários por concluir a Especialização?
Can I sign up for the course without paying or applying for financial aid?
Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.
