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Специализация

Курс 3 из 3 в программе

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Промежуточный уровень

Промежуточный уровень

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

Часов на завершение

Прибл. 16 часа на выполнение

Предполагаемая нагрузка: 4 weeks; 4-6 hours/week...
Доступные языки

Английский

Субтитры: Английский

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Bayesian StatisticsPython ProgrammingStatistical Modelstatistical regression
Специализация

Курс 3 из 3 в программе

100% онлайн

100% онлайн

Начните сейчас и учитесь по собственному графику.
Гибкие сроки

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Промежуточный уровень

Промежуточный уровень

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

Часов на завершение

Прибл. 16 часа на выполнение

Предполагаемая нагрузка: 4 weeks; 4-6 hours/week...
Доступные языки

Английский

Субтитры: Английский

Программа курса: что вы изучите

Неделя
1
Часов на завершение
3 ч. на завершение

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....
Reading
7 видео ((всего 67 мин.)), 6 материалов для самостоятельного изучения, 1 тест
Video7 видео
What Do We Mean by Fitting Models to Data'?18мин
Types of Variables in Statistical Modeling13мин
Different Study Designs Generate Different Types of Data: Implications for Modeling9мин
Objectives of Model Fitting: Inference vs. Prediction11мин
Plotting Predictions and Prediction Uncertainty8мин
Python Statistics Landscape2мин
Reading6 материала для самостоятельного изучения
Course Syllabus5мин
Meet the Course Team!10мин
Help Us Learn More About You!10мин
About Our Datasets2мин
Mixed effects models: Is it time to go Bayesian by default?15мин
Python Statistics Landscape1мин
Quiz1 практическое упражнение
Week 1 Assessment15мин
Неделя
2
Часов на завершение
5 ч. на завершение

WEEK 2 - FITTING MODELS TO INDEPENDENT DATA

In this second week, we’ll introduce you to the basics of two types of regression: linear regression and logistic regression. You’ll get the chance to think about how to fit models, how to assess how well those models fit, and to consider how to interpret those models in the context of the data. You’ll also learn how to implement those models within Python....
Reading
6 видео ((всего 85 мин.)), 4 материалов для самостоятельного изучения, 3 тестов
Video6 видео
Linear Regression Inference15мин
Interview: Causation vs Correlation18мин
Logistic Regression Introduction15мин
Logistic Regression Inference7мин
NHANES Case Study Tutorial (Linear and Logistic Regression)17мин
Reading4 материала для самостоятельного изучения
Linear Regression Models: Notation, Parameters, Estimation Methods30мин
Try It Out: Continuous Data Scatterplot App15мин
Importance of Data Visualization: The Datasaurus Dozen10мин
Logistic Regression Models: Notation, Parameters, Estimation Methods30мин
Quiz3 практического упражнения
Linear Regression Quiz20мин
Logistic Regression Quiz15мин
Week 2 Python Assessment20мин
Неделя
3
Часов на завершение
4 ч. на завершение

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. ...
Reading
8 видео ((всего 121 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
Video8 видео
Multilevel Linear Regression Models21мин
Multilevel Logistic Regression models14мин
Practice with Multilevel Modeling: The Cal Poly App12мин
What are Marginal Models and Why Do We Fit Them?13мин
Marginal Linear Regression Models19мин
Marginal Logistic Regression11мин
NHANES Case Study Tutorial (Marginal and Multilevel Regression)10мин
Reading2 материала для самостоятельного изучения
Visualizing Multilevel Models10мин
Likelihood Ratio Tests for Fixed Effects and Variance Components10мин
Quiz2 практического упражнения
Name That Model15мин
Week 3 Python Assessment20мин
Неделя
4
Часов на завершение
3 ч. на завершение

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....
Reading
6 видео ((всего 105 мин.)), 3 материалов для самостоятельного изучения, 1 тест
Video6 видео
Bayesian Approaches to Statistics and Modeling15мин
Bayesian Approaches Case Study: Part I13мин
Bayesian Approaches Case Study: Part II19мин
Bayesian Approaches Case Study - Part III23мин
Bayesian in Python19мин
Reading3 материала для самостоятельного изучения
Other Types of Dependent Variables20мин
Optional: A Visual Introduction to Machine Learning20мин
Course Feedback10мин
Quiz1 практическое упражнение
Week 4 Python Assessment20мин

Преподавателя

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Brenda Gunderson

Lecturer IV and Research Fellow
Department of Statistics
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Brady T. West

Research Associate Professor
Institute for Social Research
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Kerby Shedden

Professor
Department of Statistics

О University of Michigan

The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future....

О специализации ''Statistics with Python'

This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis, and use of the Python programming language to conduct data analyses. Learners will learn where data come from, what types of data can be collected, how to effectively summarize and visualize data, how to utilize data for estimation and assessing theories, proper interpretations of inferential results, and how to apply more advanced statistical modeling procedures....
Statistics with Python

Часто задаваемые вопросы

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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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