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.
Ce cours fait partie de la Spécialisation Statistics with Python
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Spécialisation Statistics with PythonUniversité du MichiganÀ propos de ce cours
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Cours 3 sur 3 dans le
Niveau intermédiaire
Completion of the first two courses in this specialization; high school-level algebra
Approx. 15 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Coréen, Allemand, Russe, Anglais, Espagnol
Compétences que vous acquerrez
- Bayesian Statistics
- Python Programming
- Statistical Model
- statistical regression
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 3 sur 3 dans le
Niveau intermédiaire
Completion of the first two courses in this specialization; high school-level algebra
Approx. 15 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Coréen, Allemand, Russe, Anglais, Espagnol
Offert par
Université du 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.
Programme de cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
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 heures pour terminer
8 vidéos (Total 73 min), 6 lectures, 1 quiz
5 heures pour terminer
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.
5 heures pour terminer
6 vidéos (Total 85 min), 4 lectures, 3 quiz
4 heures pour terminer
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 heures pour terminer
8 vidéos (Total 121 min), 2 lectures, 2 quiz
3 heures pour terminer
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 heures pour terminer
6 vidéos (Total 105 min), 4 lectures, 1 quiz
Avis
- 5 stars65,12 %
- 4 stars20,82 %
- 3 stars8,09 %
- 2 stars3,47 %
- 1 star2,47 %
Meilleurs avis pour FITTING STATISTICAL MODELS TO DATA WITH PYTHON
par VO17 sept. 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
par JL14 oct. 2020
Overall it's very good for someone who has a fair background in statistics, except for some small mistakes in slides and notebooks.
par NU4 juin 2020
The course is great, the only improvement I would make is to be a little more didactic in the last two units because it is a more complicated subject.
par JJ23 mai 2020
Very informative. But had few confusions in the last course. Also the python code explanations were not good as the instructor was rushing through it without explaining.
À propos du Spécialisation 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.
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