Brief Introduction
Every model used to predict a future outcome depends upon the quality of features used. This course focuses on developing better features to create better models.Description
How can data-intensive research methods be used to create more equitable and effective learning environments? In this course, you will learn how data from digital learning environments and administrative data systems can be used to help better understand relevant learning environments, identify students in need of support, and assess changes made to learning environments.
This course pays particular attention to the ways in which researchers and data scientists can transform raw data into features (i.e., variables or predictors) used in various machine learning algorithms. We will provide strategies for using prior research, knowledge from practice, and logic to create features, as well as build and evaluate machine learning models. The process of building features will be discussed within a broader data-intensive research workflow using R.
Knowledge
- How to transform and visualize data using R
- How to apply selected machine learning algorithms (e.g., logistic regression and decision trees) to regression and classification tasks in R
- Strategies for applying data-intensive research workflows for feature engineering and model building
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