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.
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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
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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.
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16 개의 읽기 자료
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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?
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3 개의 읽기 자료
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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.
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12 개의 읽기 자료
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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.
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1 개의 읽기 자료
Tidyverse Skills for Data Science in R 특화 과정 정보
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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