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Data Science in R: From Basics to Machine Learning

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1.Why R?

Course Overview What is R?Why Data Scientists Need R Quiz: R Overview

2.R Fundamentals

Basic R Syntax Flow Control Data Frames Matrices Getting Organized—Libraries and Working Directories Loading External Data Quiz: R Fundamentals

Mini Project

R Fundamentals Exercises

3.Readable Coding with tidyverse

Tidyverse Overview Special Data Type—tibbles Compounding Commands with the Pipe Operator Joins and Lookups Filtering Datasets Stacking and Unstacking Data Applying Complex Row-by-Row Operations Important Tidyverse Functions for Data Science Quiz: Tidyverse

Mini Project

Tidyverse Exercises

4.Importing More Data Sources

Database Connections Web Data Microsoft Excel Quiz: Data Import

5.Data Visualization with ggplot2

Why ggplot2?Basic Plots in ggplot2 Customizing ggplot2 Plots Faceting and Saving Plots Quiz: The ggplot2 Package

6.Best Practices for Data Scientists

Variable Naming Conventions Creating Functions Project Structure

7.Statistical Analysis and Machine Learning with tidymodels

Getting Started with tidymodels Linear Regression Random Forest Hyperparameter Tuning with Random Forest Neural Networks Quiz: The tidymodels Package

Mini Project

Exploring tidymodels through Exercises

8.Useful Libraries for Data Science

Date and Time—Lubridate String Manipulations—The stringr Package Mapping—The ggmap Package Checking Data—The skimr Package Quiz: Useful Libraries

9.Git Integration

Git and GitHub Overview R Projects Using Git with R Next Steps with R and GitHub Quiz: Git Integration

10.Getting The Most Out of R

Efficient Coding Practices Analytical Best Practices Conclusion

11.Appendix

RStudio Orientation Getting Support in R

Project

Credit Card Fraud Detection using the R Language

Data Science in R: From Basics to Machine Learning

Quiz: Git Integration

Test your knowledge of Git integration with R.

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