Applied Data Science with R

Introduction to R Programming for Data Science introduces learners to the manipulation of primitive data types in R. Participants will also learn to control program flow, construct and manipulate R data structures, and perform tasks such as reading, writing, and saving data files as well as web scraping using R.

SQL for Data Science with R focuses on creating and accessing a database instance on the cloud and executing basic SQL statements. Participants will learn to analyze data from Jupyter using R and SQL by combining both skill sets to query real-world datasets.

Data Analysis with R covers data preparation, handling missing values, formatting, and normalizing data. Learners will compare and contrast predictive models, examine data using descriptive statistics, and evaluate model performance using regularization and grid search.

Data Visualization with R introduces learners to creating various charts and plots using R and related packages. Participants will also design customized charts, create maps using the Leaflet package, and build interactive dashboards using the Shiny package for R.

In the Data Science with R - Capstone Project module, learners will write a web scraping program to extract data from an HTML file, prepare data for modeling, interpret data using exploratory data analysis techniques, and build a Shiny app containing a Leaflet map and an interactive dashboard.