Acerca de este Curso

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Resultados profesionales del estudiante

33%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

20%

consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Aprox. 12 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Habilidades que obtendrás

StatisticsStatistical Hypothesis TestingBiostatistics

Resultados profesionales del estudiante

33%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

20%

consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Aprox. 12 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

ofrecido por

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Universidad Johns Hopkins

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

3 horas para completar

Hypothesis Testing

3 horas para completar
12 videos (Total 121 minutos), 1 lectura, 2 cuestionarios
12 videos
More Hypothesis Testing12m
General Rules of Hypothesis Testing13m
Two-sided Tests9m
Confidence Intervals & P Values13m
Power2m
Calculating Power8m
T Tests & Monte Carlo14m
Two Sample Tests - Matched Data I6m
Two Sample Tests - Matched Data II8m
Two Sample Tests - Regression to the Mean12m
Two Sample Tests - Two Independent Groups17m
1 lectura
Syllabus10m
2 ejercicios de práctica
Module 1 Homework (Not counted toward final grade)30m
Module 1 Quiz30m
Semana
2

Semana 2

2 horas para completar

Two Binomials

2 horas para completar
8 videos (Total 75 minutos)
8 videos
Two Sample Binomial Tests - Exact Tests9m
Two Sample Binomial Tests - Comparing 2 Binomial Proportions17m
Relative Risks & Odds Ratios - Relative Measures4m
Relative Risks & Odds Ratios - The Relative Risk8m
Relative Risks & Odds Ratios - The Odds Ratio7m
Delta Method4m
Delta Method & Derivation9m
2 ejercicios de práctica
Module 2 Homework30m
Module 2 Quiz30m
Semana
3

Semana 3

3 horas para completar

Discrete Data Settings

3 horas para completar
7 videos (Total 98 minutos)
7 videos
Hyper-Geometric Distribution8m
Fisher's Exact Text in Practice & Monte Carlo16m
Chi Squared Testing16m
Testing Independence10m
Generalization22m
Goodness of Fit Testing11m
2 ejercicios de práctica
Module 3 Homework30m
Module 3 Quiz30m
Semana
4

Semana 4

4 horas para completar

Techniques

4 horas para completar
16 videos (Total 153 minutos)
16 videos
Simpson's Paradox, more examples9m
Weighting8m
CMH test15m
Case Control Sampling13m
Exact inference for The Odds Ratio8m
Matched 2x2 Tables3m
Dependence and Marginal Homogeneity8m
Estimation of the Marginal Difference in Proportions4m
Odds and Ends for Matched 2x2 Tables14m
the sign test7m
the sign rank test10m
the rank sum test12m
Poisson distribution9m
Poisson likelihood9m
Poisson P-value calculation7m
2 ejercicios de práctica
Module 4 Homework30m
Module 4 Quiz30m

Reseñas

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Acerca de Programa especializado: 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. This specialization starts with Mathematical Statistics bootcamps, specifically concepts and methods used in biostatistics applications. These range from probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling. This specialization also linear models for data science, starting from understanding least squares from a linear algebraic and mathematical perspective, to statistical linear models, including multivariate regression using the R programming language. These courses will give learners a firm foundation in the linear algebraic treatment of regression modeling, which will greatly augment applied data scientists' general understanding of regression models. This specialization requires a fair amount of mathematical sophistication. Basic calculus and linear algebra are required to engage in the content....
Advanced Statistics for Data Science

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