Learn Data Science With R Part 1 of 10

data science integrates data from diverse sources, handling big data, cleaning data, and modeling, while combining mathematics, statistics, and business domain expertise to drive insights.

Learn how to install R across Windows, Mac, and Linux, launch the R console and RStudio, and work with core data types, variables, and type conversion to analyze data.

Explore vectors in R, covering numeric, boolean, and character data; learn to create and manipulate vectors with sequence and length, understand type coercion, and inspect objects with ls and names.

Explore practical data frame operations in R, including selecting rows and columns and viewing data frames. Use dollar notation to access columns and learn how to rank by salary.

Explore the fundamentals of lists in R, including mixed data types, vectors, matrices, and naming and indexing elements for data science workflows.

Learn how to import data from files into R using read.table, handling separators, headers, and whitespace. Explore using packages to import Excel, SAS, and SPSS data.

Learn how to import data from files and from Oracle, using scan and replace operations, read line-by-line, map results, manage file connections, and integrate with Excel and odbc databases.