Este curso forma parte de Programa especializado: Statistical Modeling for Data Science Applications

Brian Zaharatos

2142 ya inscrito

Ofrecido Por

Parte del título de grado Master of Science in Data Science
Más información

Programa especializado: Statistical Modeling for Data Science ApplicationsUniversidad de Colorado en Boulder

Acerca de este Curso

16.794 vistas recientes

This second course in statistical modeling will introduce students to the study of the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression model, will provide the mathematical basis for designing experiments for data science applications. Emphasis will be placed on important design-related concepts, such as randomization, blocking, factorial design, and causality. Some attention will also be given to ethical issues raised in experimentation.
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Logo adapted from photo by Vincent Ledvina on Unsplash

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 2 de 3 en

Programa especializado: Statistical Modeling for Data Science Applications

Nivel intermedio

Calculus, linear algebra, and probability theory.

Aprox. 40 horas para completar

Inglés (English)

Habilidades que obtendrás

Calculus
and probability theory.
Linear Algebra

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 2 de 3 en

Programa especializado: Statistical Modeling for Data Science Applications

Nivel intermedio

Calculus, linear algebra, and probability theory.

Aprox. 40 horas para completar

Inglés (English)

Instructor

Brian Zaharatos

Director, Professional Master’s Degree in Applied Mathematics

Department of Applied Mathematics

6096 alumnos

3 cursos

Ofrecido por

Universidad de Colorado en Boulder

Comienza a trabajar para obtener tu maestría

Este curso es parte del Master of Science in Data Science completamente en línea de Universidad de Colorado en Boulder. Si te aceptan en el programa completo, tus cursos cuentan para tu título de grado.

Más información

Programa - Qué aprenderás en este curso

Semana

Semana 1

11 horas para completar

Introduction to ANOVA and Experimental Design

11 horas para completar

9 videos (Total 87 minutos), 2 lecturas, 12 cuestionarios

9 videos

Introduction to Experimental Design10m

The One-Way ANOVA and ANCOVA Models6m

ANOVA Variance Decomposition8m

ANOVA Sums of Squares and the F-test14m

ANOVA and ANCOVA as Regression Models10m

One-Way ANOVA Interpretation in the Regression Context10m

The ANCOVA Model15m

ANCOVA with Interactions7m

ANCOVA with Interactions in R4m

2 lecturas

Welcome and Where to Find Help 10m

Information on Peer Reviews10m

9 ejercicios de práctica

Introduction to ANOVA and Experimental Design30m

The One-Way ANOVA and ANCOVA Models30m

ANOVA Variance Decomposition30m

ANOVA Sums of Squares and the F-Test30m

ANOVA and ANCOVA as Regression Models30m

One-Way ANOVA Interpretation in the Regression Context30m

The ANCOVA Model30m

ANCOVA with Interactions30m

ANCOVA with Interactions in R30m

Semana

Semana 2

9 horas para completar

Hypothesis Testing in the ANOVA Context

9 horas para completar

6 videos (Total 91 minutos), 2 lecturas, 6 cuestionarios

6 videos

Beyond the Full F-test12m

Planned Comparisons: Defining Contrasts16m

Planned Comparisons: Hypothesis Testing with Contrasts14m

Post Hoc Comparisons13m

Post Hoc Comparisons in R16m

Type II Error and Power in the ANOVA Context18m

2 lecturas

Patrizio E. Tressoldi and David Giofré: "The pervasive avoidance of prospective statistical power: major consequences and practical solutions"10m

Optional: Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors10m

4 ejercicios de práctica

Beyond the Full F-test30m

Planned Comparisons: Defining Contrasts30m

Planned and Unplanned Comparisons30m

Type II Error and Power in the ANOVA Context30m

Semana

Semana 3

9 horas para completar

Two-Way ANOVA and Interactions

9 horas para completar

7 videos (Total 79 minutos)

7 videos

Motivating the Two-way ANOVA Model10m

The two-way ANOVA model9m

The Two-way ANOVA Model as a Regression Model9m

Interaction Terms in the Two-way ANOVA Model: Definitions and Visualizations13m

Interactions in the Two-way ANOVA Model: Formal Tests15m

Two-way ANOVA Hypothesis Testing (no interaction)14m

Looking Ahead: Two-Way ANOVA and Experimental Design5m

6 ejercicios de práctica

Motivating the Two-way ANOVA Model30m

The Two-way ANOVA Model30m

The Two-way ANOVA Model as a Regression Model30m

Interaction Terms in the Two-way ANOVA Model: Definitions and Visualizations30m

Interactions in the Two-way ANOVA Model: Formal Tests30m

Two-way ANOVA Hypothesis Testing (no interaction)30m

Semana

Semana 4

10 horas para completar

Experimental Design: Basic Concepts and Designs

10 horas para completar

7 videos (Total 79 minutos), 2 lecturas, 7 cuestionarios

7 videos

The Conceptual Framework of Experimental Design19m

The Completely Randomized Design12m

The Randomized Complete Block Design (RCBD)8m

The Randomized Complete Block Design (RCBD): Hypothesis Testing8m

The Factorial Design10m

Further Issues in Experimental Design7m

Ethical Issues in Experimental Design12m

2 lecturas

Causation and Experimental Design10m

Resources on Ethics 10m

5 ejercicios de práctica

The Conceptual Framework of Experimental Design30m

The Completely Randomized Design30m

The Randomized Complete Block Design (RCBD)30m

The Factorial Design30m

Further Issues in Experimental Design30m

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.

This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash

Statistical Modeling for Data Science Applications

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

¿Tienes más preguntas? Visita el Centro de Ayuda al Estudiante.

ANOVA and Experimental Design

Acerca de este Curso

Habilidades que obtendrás