Introduction to R and Phyton

The introduction to R and Python provides students with a fundamental understanding of programming languages commonly used in data science and statistical analysis. This topic introduces the characteristics, advantages, and applications of R and Python in processing, analyzing, and visualizing data. Students will learn how these programming languages support statistical investigations, facilitate data-driven decision-making, and solve real-world problems in various fields, including health, economics, environmental science, and social research.

The material covers the basic syntax and programming environments of R and Python, including variables, data types, operators, functions, and data input/output. Students will become familiar with the RStudio and Jupyter Notebook environments and learn how to write and execute simple commands. In addition, students will explore essential packages and libraries, such as dplyr, ggplot2, and tidyverse in R, as well as pandas, NumPy, Matplotlib, and SciPy in Python, which support data manipulation, numerical computation, visualization, and statistical analysis.

By the end of this topic, students are expected to understand the basic principles of programming in R and Python and apply simple commands to manage and analyze datasets. Students will also develop the ability to select appropriate functions and libraries for specific analytical tasks and interpret basic programming outputs. These foundational skills will prepare students for subsequent topics, including exploratory data analysis, probability distributions, statistical hypothesis testing, regression, classification, and machine learning.