Este curso forma parte de Programa especializado: Statistics with Python

4.4
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636 calificaciones

87 %

Brenda Gunderson +2 instructores más

29.639 ya inscrito

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Programa especializado: Statistics with PythonUniversidad de Míchigan

Acerca de este Curso

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In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations.
This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python).

During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera.

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Curso 3 de 3 en

Programa especializado: Statistics with Python

Nivel intermedio

Completion of the first two courses in this specialization; high school-level algebra

Aprox. 15 horas para completar

Inglés (English)

Habilidades que obtendrás

Bayesian Statistics
Python Programming
Statistical Model
statistical regression

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 3 de 3 en

Programa especializado: Statistics with Python

Nivel intermedio

Completion of the first two courses in this specialization; high school-level algebra

Aprox. 15 horas para completar

Inglés (English)

Instructores

Calificación del instructor

4,6/5 (88 calificaciones)

Ofrecido por

Universidad de Míchigan

Programa - Qué aprenderás en este curso

Calificación del contenido87%(2,348 calificaciones)

Semana

Semana 1

3 horas para completar

WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING

We begin this third course of the Statistics with Python specialization with an overview of what is meant by “fitting statistical models to data.” In this first week, we will introduce key model fitting concepts, including the distinction between dependent and independent variables, how to account for study designs when fitting models, assessing the quality of model fit, exploring how different types of variables are handled in statistical modeling, and clearly defining the objectives of fitting models.

3 horas para completar

8 videos (Total 73 minutos), 6 lecturas, 1 cuestionario

8 videos

Welcome to the Course!3m

Fitting Statistical Models to Data with Python Guidelines5m

What Do We Mean by Fitting Models to Data?18m

Types of Variables in Statistical Modeling13m

Different Study Designs Generate Different Types of Data: Implications for Modeling9m

Objectives of Model Fitting: Inference vs. Prediction11m

Plotting Predictions and Prediction Uncertainty8m

Python Statistics Landscape2m

6 lecturas

Course Syllabus10m

Meet the Course Team!10m

Help Us Learn More About You!10m

About Our Datasets2m

Mixed effects models: Is it time to go Bayesian by default?15m

Python Statistics Landscape1m

1 ejercicio de práctica

Week 1 Assessment15m

Semana

Semana 2

5 horas para completar

WEEK 2 - FITTING MODELS TO INDEPENDENT DATA

5 horas para completar

6 videos (Total 85 minutos), 4 lecturas, 3 cuestionarios

6 videos

Linear Regression Introduction11m

Linear Regression Inference15m

Interview: Causation vs Correlation18m

Logistic Regression Introduction15m

Logistic Regression Inference7m

NHANES Case Study Tutorial (Linear and Logistic Regression)17m

4 lecturas

Linear Regression Models: Notation, Parameters, Estimation Methods30m

Try It Out: Continuous Data Scatterplot App15m

Importance of Data Visualization: The Datasaurus Dozen10m

Logistic Regression Models: Notation, Parameters, Estimation Methods30m

3 ejercicios de práctica

Linear Regression Quiz20m

Logistic Regression Quiz15m

Week 2 Python Assessment20m

Semana

Semana 3

4 horas para completar

WEEK 3 - FITTING MODELS TO DEPENDENT DATA

In the third week of this course, we will be building upon the modeling concepts discussed in Week 2. Multilevel and marginal models will be our main topic of discussion, as these models enable researchers to account for dependencies in variables of interest introduced by study designs. We’ll be covering why and when we fit these alternative models, likelihood ratio tests, as well as fixed effects and their interpretations.

4 horas para completar

8 videos (Total 121 minutos), 2 lecturas, 2 cuestionarios

8 videos

What are Multilevel Models and Why Do We Fit Them?17m

Multilevel Linear Regression Models21m

Multilevel Logistic Regression models14m

Practice with Multilevel Modeling: The Cal Poly App12m

What are Marginal Models and Why Do We Fit Them?13m

Marginal Linear Regression Models19m

Marginal Logistic Regression11m

NHANES Case Study Tutorial (Marginal and Multilevel Regression)10m

2 lecturas

Visualizing Multilevel Models10m

Likelihood Ratio Tests for Fixed Effects and Variance Components10m

2 ejercicios de práctica

Name That Model15m

Week 3 Python Assessment20m

Semana

Semana 4

3 horas para completar

WEEK 4: Special Topics

In this final week, we introduce special topics that extend the curriculum from previous weeks and courses further. We will cover a broad range of topics such as various types of dependent variables, exploring sampling methods and whether or not to use survey weights when fitting models, and in-depth case studies utilizing Bayesian techniques to derive insights from data. You’ll also have the opportunity to apply Bayesian techniques in Python.

3 horas para completar

6 videos (Total 105 minutos), 4 lecturas, 1 cuestionario

6 videos

Should We Use Survey Weights When Fitting Models?13m

Bayesian Approaches to Statistics and Modeling15m

Bayesian Approaches Case Study: Part I13m

Bayesian Approaches Case Study: Part II19m

Bayesian Approaches Case Study - Part III23m

Bayesian in Python19m

4 lecturas

Other Types of Dependent Variables20m

Optional: A Visual Introduction to Machine Learning20m

Course Feedback10m

Keep Learning with Michigan Online10m

1 ejercicio de práctica

Week 4 Python Assessment20m

Reseñas

4.4

121 reseña

5 stars
65,56 %
4 stars
20,28 %
3 stars
8,33 %
2 stars
3,45 %
1 star
2,35 %

Principales reseñas sobre FITTING STATISTICAL MODELS TO DATA WITH PYTHON

por SK27 de may. de 2020

Great course. It really improved my understanding of statistical modeling methodologies.

por ST15 de jun. de 2021

It was very technical and a lot of the mathematics behind the models were not explained properly. The codes were also not explained properly

por KR12 de ene. de 2021

These whole three certifications lays the foundation for learning Machine Learning a more in-depth way.

por VO17 de sep. de 2019

Good course, but the last of three was the most difficult one. I hope that it were a good introduction to the fascinating world of statistics and data science

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Acerca de Programa especializado: Statistics with Python

This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.

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.

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Fitting Statistical Models to Data with Python

Acerca de este Curso

Habilidades que obtendrás

Instructores

Brenda Gunderson

Brady T. West

Kerby Shedden

Ofrecido por

Universidad de Míchigan

Programa - Qué aprenderás en este curso

WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING

WEEK 2 - FITTING MODELS TO INDEPENDENT DATA

WEEK 3 - FITTING MODELS TO DEPENDENT DATA

WEEK 4: Special Topics

Reseñas

Principales reseñas sobre FITTING STATISTICAL MODELS TO DATA WITH PYTHON

Acerca de Programa especializado: Statistics with Python

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Comienza o impulsa tu carrera profesional

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Fitting Statistical Models to Data with Python

Acerca de este Curso

Habilidades que obtendrás

Instructores

Brenda Gunderson

Brady T. West

Kerby Shedden

Ofrecido por

Universidad de Míchigan

Programa - Qué aprenderás en este curso

WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING

WEEK 2 - FITTING MODELS TO INDEPENDENT DATA

WEEK 3 - FITTING MODELS TO DEPENDENT DATA

WEEK 4: Special Topics

Reseñas

Principales reseñas sobre FITTING STATISTICAL MODELS TO DATA WITH PYTHON

Acerca de Programa especializado: Statistics with Python

Preguntas Frecuentes

Coursera Footer

Comienza o impulsa tu carrera profesional

Cursos y certificaciones populares

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Obtén un título de grado o un certificado en línea

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