本课程是 Tidyverse Skills for Data Science in R 专项课程专项课程的一部分

Carrie Wright, PhD 另外 +3 位授课教师

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Tidyverse Skills for Data Science in R 专项课程约翰霍普金斯大学

课程信息

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Developing insights about your organization, business, or research project depends on effective modeling and analysis of the data you collect. Building effective models requires understanding the different types of questions you can ask and how to map those questions to your data. Different modeling approaches can be chosen to detect interesting patterns in the data and identify hidden relationships.
This course covers the types of questions you can ask of data and the various modeling approaches that you can apply. Topics covered include hypothesis testing, linear regression, nonlinear modeling, and machine learning. With this collection of tools at your disposal, as well as the techniques learned in the other courses in this specialization, you will be able to make key discoveries from your data for improving decision-making throughout your organization.

In this specialization we assume familiarity with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.

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第 5 门课程（共 5 门）

Tidyverse Skills for Data Science in R 专项课程

可灵活调整截止日期

根据您的日程表重置截止日期。

完成时间大约为21 小时

英语（English）

字幕：英语（English）

您将学到的内容有

Describe different types of data analytic questions

Conduct hypothesis tests of your data

Apply linear modeling techniques to answer multivariable questions

Apply machine learning workflows to detect complex patterns in your data

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

第 5 门课程（共 5 门）

Tidyverse Skills for Data Science in R 专项课程

可灵活调整截止日期

根据您的日程表重置截止日期。

完成时间大约为21 小时

英语（English）

字幕：英语（English）

讲师

提供方

约翰霍普金斯大学

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

教学大纲 - 您将从这门课程中学到什么

周

周 1

完成时间为 4 小时

Modeling Data Basics

Developing insights about your organization, business, or research project depends on effective modeling and analysis of the data you collect. Building effective models requires understanding the different types of questions you can ask and how to map those questions to your data. Different modeling approaches can be chosen to detect interesting patterns in the data and identify hidden relationships.

完成时间为 4 小时

15 个阅读材料

15 个阅读材料

The Purpose of Data Science5分钟

Types of Data Science Questions10分钟

Data Needs5分钟

Number of observations is too small5分钟

Dataset does not contain the exact variables you are looking for10分钟

Variables in the dataset are not collected in the same year5分钟

Dataset is not representative of the population that you are interested in10分钟

Some variables in the dataset are measured with error5分钟

Variables are confounded10分钟

Descriptive and Exploratory Data Analysis15分钟

Missing Values10分钟

Shape25分钟

Identifying Outliers20分钟

Evaluating Variables20分钟

Evaluating Relationships30分钟

1 个练习

Modeling Data Basics Quiz30分钟

周

周 2

完成时间为 1 小时

Inference

Inferential Analysis is what analysts carry out after they’ve described and explored their dataset. After understanding your dataset better, analysts often try to infer something from the data. This is done using statistical tests. We discussed a bit about how we can use models to perform inference and prediction analyses. What does this mean?

完成时间为 1 小时

3 个阅读材料

3 个阅读材料

Inference10分钟

Uncertainty10分钟

Random Sampling10分钟

1 个练习

Inference Quiz30分钟

周

周 3

完成时间为 2 小时

Linear Modeling

Linear models are the most commonly used models in data analysis because of their computational efficiency and their ease of interpretation. Having a solid understanding of linear models and how they work is critical for any work in data science. The tidyverse provides a set of tools for making linear modeling more efficient and streamlined.

完成时间为 2 小时

12 个阅读材料

12 个阅读材料

Linear Regression15分钟

Assumptions20分钟

Association15分钟

Association Testing in R10分钟

Fitting the Model2分钟

Model Diagnostics10分钟

Tree Girth and Height Example10分钟

Interpreting the Model10分钟

Variance Explained5分钟

Using broom5分钟

Correlation Is Not Causation7分钟

Confounding10分钟

1 个练习

Linear Regression Quiz30分钟

周

周 4

完成时间为 1 小时

Multiple Linear Regression

Multiple linear regression is needed when you want to include confounding factors or other predictors in your model for the response. R provides a straightforward way to do this via the formula interface to the lm() function.

完成时间为 1 小时

1 个阅读材料

1 个阅读材料

Multiple Linear Regression15分钟

1 个练习

Multiple Linear Regression Quiz30分钟

周

周 5

完成时间为 23 分钟

Beyond Linear Regression

While we’ve focused on linear regression in this lesson on inference, linear regression isn’t the only analytical approach out there. However, it is arguably the most commonly used. And, beyond that, there are many statistical tests and approaches that are slight variations on linear regression, so having a solid foundation and understanding of linear regression makes understanding these other tests and approaches much simpler. For example, what if you didn’t want to measure the linear relationship between two variables, but instead wanted to know whether or not the average observed is different from expectation?

完成时间为 23 分钟

3 个阅读材料

3 个阅读材料

Beyond Linear Regression3分钟

Mean Different From Expectation?5分钟

Testing Mean Difference From Expectation in R15分钟

周

周 6

完成时间为 1 小时

Hypothesis Testing

Hypothesis testing describes a family of statistical techniques for determining whether the data you collect provides evidence for the value of an unknown parameter of interest. The goal of hypothesis tests is to make inferences while accounting for variability in the data that can lead to spurious results.

完成时间为 1 小时

3 个阅读材料

3 个阅读材料

More Statistical Tests2分钟

Hypothesis Testing10分钟

The infer Package15分钟

1 个练习

Hypothesis Testing Quiz30分钟

周

周 7

完成时间为 3 小时

Prediction Modeling

Prediction modeling is an essential activity in data science and involves building systems for making predictions based on previously observed data. These models are typically very flexible (much more than linear models) and can capture a range of different relationships.

完成时间为 3 小时

12 个阅读材料

12 个阅读材料

Prediction Modeling10分钟

What is Machine Learning?10分钟

Machine Learning Steps10分钟

Data Splitting10分钟

Train, Test, Validate10分钟

Train3分钟

Test5分钟

Validate10分钟

Variable Selection15分钟

Model Selection5分钟

Regression vs. Classification30分钟

Model Accuracy15分钟

1 个练习

Prediction and Machine Learning Quiz30分钟

周

周 8

完成时间为 2 小时

The tidymodels Ecosystem

There are incredibly helpful packages available in R thanks to the work of RStudio. As mentioned above, there are hundreds of different machine learning algorithms. The tidymodels R packages have compiled all of them into a single framework, allowing you to use many different machine learning models easily.

完成时间为 2 小时

5 个阅读材料

5 个阅读材料

The tidymodels Ecosystem5分钟

Benefits of tidymodels5分钟

Packages of tidymodels15分钟

Example of Continuous Variable Prediction45分钟

Example of Categorical Variable Prediction30分钟

1 个练习

tidymodels Quiz30分钟

周

周 9

完成时间为 5 小时

Case Studies

This case study will demonstrate an approach to building a prediction model for predicting outdoor air pollution concentrations in the United States.

完成时间为 5 小时

17 个阅读材料

17 个阅读材料

Case Study #1: Predicting Annual Air Pollution5分钟

The Data5分钟

Data Import5分钟

Data Exploration and Wrangling20分钟

Evaluate Correlation15分钟

Splitting the Data10分钟

Making a Recipe30分钟

Running Preprocessing30分钟

Specifying the Model20分钟

Assessing the Model Fit15分钟

Model Performance: Getting Predicted Values15分钟

Visualizing Model Performance5分钟

Quantifying Model Performance10分钟

Assessing Model Performance on v -folds Using tune30分钟

Random Forest30分钟

Model Tuning30分钟

Final model performance evaluation30分钟

周

周 10

完成时间为 5 分钟

Summary of tidymodels

The tidymodels collection of packages can be overwhelming at first glance. Here, we provide a quick summary chart to help navigate all of the packages and when they should be used.

完成时间为 5 分钟

1 个阅读材料

1 个阅读材料

Summary of tidymodels5分钟

周

周 11

完成时间为 2 小时

Project: Modeling Data in the Tidyverse

In this project, you will practice building models with the tidyverse for classifying consumer complaints data from the Consumer Financial Protection Bureau (CFPB). This project includes both a Peer Review step in which you'll upload R Markdown and knitted HTML files AND a Quiz step in which you'll answer questions about the predictions made by your classification algorithm.

完成时间为 2 小时

1 个阅读材料

1 个阅读材料

Important information before you start the quiz10分钟

1 个练习

Course Project Prediction Quiz30分钟

关于 Tidyverse Skills for Data Science in R 专项课程

This Specialization is intended for data scientists with some familiarity with the R programming language who are seeking to do data science using the Tidyverse family of packages. Through 5 courses, you will cover importing, wrangling, visualizing, and modeling data using the powerful Tidyverse framework. The Tidyverse packages provide a simple but powerful approach to data science which scales from the most basic analyses to massive data deployments. This course covers the entire life cycle of a data science project and presents specific tidy tools for each stage.

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Modeling Data in the Tidyverse

课程信息

您将学到的内容有

讲师

Carrie Wright, PhD

Shannon Ellis, PhD

Stephanie Hicks, PhD

Roger D. Peng, PhD

提供方

约翰霍普金斯大学

教学大纲 - 您将从这门课程中学到什么

周 1

Modeling Data Basics

周 2

Inference

周 3

Linear Modeling

周 4

Multiple Linear Regression

周 5

Beyond Linear Regression

周 6

Hypothesis Testing

周 7

Prediction Modeling

周 8

The tidymodels Ecosystem

周 9

Case Studies

周 10

Summary of tidymodels

周 11

Project: Modeling Data in the Tidyverse

关于 Tidyverse Skills for Data Science in R 专项课程

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