Course Category: Data Science (R)

This course intermediate concepts in natural language processing, equipping learners with the ability to clean and process large amounts of text data,…

This course covers the basics of natural language processing, equipping learners with the ability to clean and process large amounts of text data requ…

This course covers an overview of ensemble learning methods like random forest and boosting. At the end of this course, students will be able to im…

Decision tree models are classification algorithms that sort novel data into categories based on iterative splitting, like the branches of a tree, …

Logistic regression is a classification algorithm useful for sorting data into two classes: either this, or that. In this course, learners will identi…

Classification is a machine learning technique that can be used to sort novel data into labeled categories. In this course, learners will identify use…

This course covers the basics of Principal Component Analysis (PCA), the need for PCA for better interpretability of large datasets by applying dimesi…

In this course, learners will encounter more sophisticated methods for generating clusters within unlabeled data using R. The first method, hierarchic…

Clustering is a machine learning technique that can be used to group unlabeled data based on shared features. In this course, learners will identify u…

Multiple linear regression is a supervised learning technique that allows analysts to model the relationship between a certain number of labeled featu…