R Programming for Data Analysis, Statistics, and Applied Math

Data analysis and applied statistics require robust tools capable of handling complex mathematical operations and datasets. This text-based course introduces you to the R programming language, giving you the skills needed to analyze data, compute statistical metrics, and solve quantitative problems in domains like economics and biology. You will begin by learning fundamental syntax, essential terminology, and basic data structures before progressing to practical statistical modeling and modern data manipulation techniques. What you'll learn: - Understand core R syntax, variables, vectors, and foundational data structures. - Perform exploratory data analysis using modern data manipulation packages like dplyr. - Apply statistical methods, hypothesis testing, and probability distributions to datasets. - Clean, reshape, and prepare raw data for mathematical and economic modeling. - Write reproducible analytical scripts and structured functions to automate calculations. - Solve applied quantitative problems tailored for fields like economics, biology, and mathematics. The course begins with clear written explanations of foundational R concepts and syntax, followed by practical data processing workflows, statistical analysis, and modern script organization techniques. This introductory course is designed for students, researchers, and beginners in mathematics, economics, or biology who want to learn R from scratch. No prior programming experience is required. Start reading today to master data analysis with R.