"Statistical Learning for Data Science" is an advanced course designed to equip working professionals with the knowledge and skills necessary to excel in the field of data science. Through comprehensive instruction on key topics such as shrink methods, parametric regression analysis, generalized linear models, and general additive models, students will learn how to apply resampling methods to gain additional information about fitted models, optimize fitting procedures to improve prediction accuracy and interpretability, and identify the benefits and approach of non-linear models. This course is the perfect choice for anyone looking to upskill or transition to a career in data science.
Resampling, Selection and Splines
Dieser Kurs ist Teil von Spezialisierung Statistical Learning for Data Science
Dozent: Osita Onyejekwe
Bei enthalten
Empfohlene Erfahrung
Was Sie lernen werden
Apply resampling methods in order to obtain additional information about fitted models.
Optimize fitting procedures to improve prediction accuracy and interpretability.
Identify the benefits and approach of non-linear models.
Kompetenzen, die Sie erwerben
- Kategorie: Statistics
- Kategorie: Data Science
- Kategorie: Selection
- Kategorie: Resampling
- Kategorie: Splines
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In diesem Kurs gibt es 5 Module
Welcome to our Resampling, Selection, and Splines class! In this course, we will dive deep into these key topics in statistical learning and explore how they can be applied to data science. The module provides an introductory overview of the course and introduces the course instructor.
Das ist alles enthalten
6 Videos2 Lektüren1 Diskussionsthema
In this module, we will turn our attention to generalized least squares (GLS). GLS is a statistical method that extends the ordinary least squares (OLS) method to account for heteroscedasticity and serial correlation in the error terms. Heteroscedasticity is the condition where the variance of the errors is not constant across all levels of the predictor variables, while serial correlation is the condition where the errors are correlated across time or space. GLS has many practical applications, such as in finance for modeling asset returns, in econometrics for modeling time series data, and in spatial analysis for modeling spatially correlated data. By the end of this module, you will have a good understanding of how GLS works and when it is appropriate to use it. You will also be able to implement GLS in R using the gls() function in the nlme package.
Das ist alles enthalten
1 Video1 Lektüre1 Programmieraufgabe1 Unbewertetes Labor
In this module, we will explore ridge regression, LASSO, and principal component analysis (PCA). These techniques are widely used for regression and dimensionality reduction tasks in machine learning and statistics.
Das ist alles enthalten
7 Videos3 Lektüren3 Programmieraufgaben
This week, we will be exploring the concept of cross-validation, a crucial technique used to evaluate and compare the performance of different statistical learning models. We will explore different types of cross-validation techniques, including k-fold cross-validation, leave-one-out cross-validation, and stratified cross-validation. We will discuss their strengths, weaknesses, and best practices for implementation. Additionally, we will examine how cross-validation can be used for model selection and hyperparameter tuning.
Das ist alles enthalten
1 Video1 Lektüre1 Programmieraufgabe
For our final module, we will explore bootstrapping. Bootstrapping is a resampling technique that allows us to gain insights into the variability of statistical estimators and quantify uncertainty in our models. By creating multiple simulated datasets through resampling, we can explore the distribution of sample statistics, construct confidence intervals, and perform hypothesis testing. Bootstrapping is particularly useful when parametric assumptions are hard to meet or when we have limited data. By the end of this week, you will have an understanding of bootstrapping and its practical applications in statistical learning.
Das ist alles enthalten
1 Video1 Lektüre1 Programmieraufgabe
Dozent
Empfohlen, wenn Sie sich für Probability and Statistics interessieren
University of Colorado Boulder
Fudan University
Scrimba
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