This is only a preview of the course. Although it includes all course content, it will not grade responses or provide feedback to students. Want to learn more? Request a demo with a CourseKata instructor..
Getting Started (Don't Skip This Part)
Algebra + Data Science
Chapter 1 - Exploring Variation in Data
- 1.1 Getting Started with R
- 1.2 Introduction to R Functions
- 1.3 Data Frames
- 1.4 Relationships Between Two Variables
- 1.5 Adding Points and Colors to a Scatter Plot
- 1.6 Quantitative and Categorical Variables
- 1.7 Exploring Multivariate Hypotheses with Visualizations
- 1.8 Manipulating Data Frames: select() and filter()
- 1.9 Manipulating Data Frames: arrange() and mutate()
Chapter 2 - Modeling Data with Functions
Chapter 3 - Assessing How Well Models Fit the Data
- 3.1 Better Models Make Better Predictions
- 3.2 Better Models Have Less Error
- 3.3 Variables as Vectors
- 3.4 Summing Residuals From a Model
- 3.5 Residuals are Perfectly Balanced at the Mean
- 3.6 Models with Perfectly Balanced Residuals
- 3.7 The Beauty of Sum of Squares (SSE)
- 3.8 The Best-Fitting Model
- 3.9 SSE, MSE, and RMSE
- 3.10 Proportional Reduction in Error (PRE)