This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
Este curso faz parte do Programa de cursos integrados Advanced Statistics for Data Science
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Programa de cursos integrados Advanced Statistics for Data ScienceUniversidade Johns HopkinsInformações sobre o curso
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Aprox. 13 horas para completar
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Sua empresa se beneficiaria do treinamento dos funcionários em habilidades sob demanda?
Experimente o Coursera for BusinessHabilidades que você terá
- Statistics
- Confidence Interval
- Statistical Hypothesis Testing
- Biostatistics
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Aprox. 13 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Inglês, Espanhol
Sua empresa se beneficiaria do treinamento dos funcionários em habilidades sob demanda?
Experimente o Coursera for Business
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.
Programa - O que você aprenderá com este curso
4 horas para concluir
Introduction, Probability, Expectations, and Random Vectors
You are about to undergo an intense and demanding immersion into the world of mathematical biostatistics. Over the next few weeks, you will learn about probability, expectations, conditional probabilities, distributions, confidence intervals, bootstrapping, binomial proportions, and much more. Module 1 covers experiments, probability, variables, mass functions, density functions, cumulative distribution functions, expectations, variations, and vectors.
4 horas para concluir
13 vídeos (Total 179 mín.), 2 leituras, 2 testes
4 horas para concluir
Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics
This module covers Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics. These are the most fundamental core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.
4 horas para concluir
7 vídeos (Total 157 mín.)
3 horas para concluir
Confidence Intervals, Bootstrapping, and Plotting
This module covers Confidence Intervals, Bootstrapping, and Plotting. These are core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.
3 horas para concluir
7 vídeos (Total 121 mín.)
2 horas para concluir
Binomial Proportions and Logs
This module covers Binomial Proportions and Logs. These are core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.
2 horas para concluir
3 vídeos (Total 71 mín.)
Avaliações
- 5 stars63,61%
- 4 stars23,88%
- 3 stars7,81%
- 2 stars2,45%
- 1 star2,23%
Principais avaliações do BIOESTATÍSTICA MATEMÁTICA: TREINAMENTO 1
por MC30 de ago de 2017
The content is in-depth and the instructor is knowledgeable, but the quiz demands a quite wide knowledge base and does not provide feedback.
por MS27 de mar de 2018
I liked the course. Need good maths skills. Would prefer to have a review/HW after each lecture than at the end of each week.
por VV8 de set de 2018
Great course, excellent homework/quizzes and comparatively rigorous treatment by a great Prof.
por NB17 de jan de 2016
I enjoyed the course. I wish there was perhaps a little more evaluation along the way (maybe occasional in-lecture questions), but otherwise very nice.
Sobre Programa de cursos integrados Advanced Statistics for Data Science
Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression.
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