This course will provide a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse.
Este curso forma parte de Programa especializado: Statistical Modeling for Data Science Applications
13 calificaciones
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ofrecido por
Programa especializado: Statistical Modeling for Data Science ApplicationsUniversidad de Colorado en BoulderAcerca de este Curso
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Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Calculus, linear algebra, and probability theory.
Aprox. 45 horas para completar
Inglés (English)
SubtÃtulos: Inglés (English)
Habilidades que obtendrás
- Linear Model
- R Programming
- Statistical Model
- regression
Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Calculus, linear algebra, and probability theory.
Aprox. 45 horas para completar
Inglés (English)
SubtÃtulos: Inglés (English)
ofrecido por
Universidad de Colorado en Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
Comienza a trabajar para obtener tu maestrÃa
Este curso es parte del Master of Science in Data Science completamente en lÃnea de Universidad de Colorado en Boulder.
Si te aceptan en el programa completo, tus cursos cuentan para tu tÃtulo de grado.
Programa - Qué aprenderás en este curso
8 horas para completar
Introduction to Statistical Models
In this module, we will introduce the basic conceptual framework for statistical modeling in general, and for linear regression models in particular.
8 horas para completar
8 videos (Total 82 minutos), 2 lecturas, 5 cuestionarios
8 horas para completar
Linear Regression Parameter Estimation
In this module, we will learn how to fit linear regression models with least squares. We will also study the properties of least squares, and describe some goodness of fit metrics for linear regression models.
8 horas para completar
9 videos (Total 134 minutos)
9 horas para completar
Inference in Linear Regression
In this module, we will study the uses of linear regression modeling for justifying inferences from samples to populations.
9 horas para completar
8 videos (Total 121 minutos), 1 lectura, 5 cuestionarios
6 horas para completar
Prediction and Explanation in Linear Regression Analysis
In this module, we will identify how models can predict future values, as well as construct interval estimates for those values. We will also explore the relationship between statistical modelling and causal explanations.
6 horas para completar
6 videos (Total 82 minutos)
Acerca de Programa especializado: Statistical Modeling for Data Science Applications
Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language.
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