Informações sobre o curso

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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário

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

Aprox. 15 horas para completar
Inglês
Legendas: Inglês, Coreano

Habilidades que você terá

Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário

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

Aprox. 15 horas para completar
Inglês
Legendas: Inglês, Coreano

oferecido por

Logotipo de Universidade de Michigan

Universidade de Michigan

Programa - O que você aprenderá com este curso

Semana
1

Semana 1

3 horas para concluir

WEEK 1 - OVERVIEW & CONSIDERATIONS FOR STATISTICAL MODELING

3 horas para concluir
8 vídeos (Total 73 mín.), 6 leituras, 1 teste
8 videos
Fitting Statistical Models to Data with Python Guidelines5min
What Do We Mean by Fitting Models to Data?18min
Types of Variables in Statistical Modeling13min
Different Study Designs Generate Different Types of Data: Implications for Modeling9min
Objectives of Model Fitting: Inference vs. Prediction11min
Plotting Predictions and Prediction Uncertainty8min
Python Statistics Landscape2min
6 leituras
Course Syllabus5min
Meet the Course Team!10min
Help Us Learn More About You!10min
About Our Datasets2min
Mixed effects models: Is it time to go Bayesian by default?15min
Python Statistics Landscape1min
1 exercício prático
Week 1 Assessment15min
Semana
2

Semana 2

5 horas para concluir

WEEK 2 - FITTING MODELS TO INDEPENDENT DATA

5 horas para concluir
6 vídeos (Total 85 mín.), 4 leituras, 3 testes
6 videos
Linear Regression Inference15min
Interview: Causation vs Correlation18min
Logistic Regression Introduction15min
Logistic Regression Inference7min
NHANES Case Study Tutorial (Linear and Logistic Regression)17min
4 leituras
Linear Regression Models: Notation, Parameters, Estimation Methods30min
Try It Out: Continuous Data Scatterplot App15min
Importance of Data Visualization: The Datasaurus Dozen10min
Logistic Regression Models: Notation, Parameters, Estimation Methods30min
3 exercícios práticos
Linear Regression Quiz20min
Logistic Regression Quiz15min
Week 2 Python Assessment20min
Semana
3

Semana 3

4 horas para concluir

WEEK 3 - FITTING MODELS TO DEPENDENT DATA

4 horas para concluir
8 vídeos (Total 121 mín.), 2 leituras, 2 testes
8 videos
Multilevel Linear Regression Models21min
Multilevel Logistic Regression models14min
Practice with Multilevel Modeling: The Cal Poly App12min
What are Marginal Models and Why Do We Fit Them?13min
Marginal Linear Regression Models19min
Marginal Logistic Regression11min
NHANES Case Study Tutorial (Marginal and Multilevel Regression)10min
2 leituras
Visualizing Multilevel Models10min
Likelihood Ratio Tests for Fixed Effects and Variance Components10min
2 exercícios práticos
Name That Model15min
Week 3 Python Assessment20min
Semana
4

Semana 4

3 horas para concluir

WEEK 4: Special Topics

3 horas para concluir
6 vídeos (Total 105 mín.), 3 leituras, 1 teste
6 videos
Bayesian Approaches to Statistics and Modeling15min
Bayesian Approaches Case Study: Part I13min
Bayesian Approaches Case Study: Part II19min
Bayesian Approaches Case Study - Part III23min
Bayesian in Python19min
3 leituras
Other Types of Dependent Variables20min
Optional: A Visual Introduction to Machine Learning20min
Course Feedback10min
1 exercício prático
Week 4 Python Assessment20min

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Sobre Programa de cursos integrados 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....
Statistics with Python

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    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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