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🧾 Course Requirements: R Programming – Data Analysis & Statistics

✅ Prerequisites for Learners

💻 1. Basic Computer Skills

• Ability to install software and navigate folders
• Familiarity with working on a Windows, macOS, or Linux system

📊 2. Basic Understanding of Data

• Conceptual knowledge of:
• Rows and columns (tabular data)
• Variables, values, and types of data (e.g., numeric, categorical)
• Basic idea of what data analysis or statistical thinking involves

📈 3. Math or Analytical Thinking

• High school-level mathematics or statistics is helpful
• Comfort with concepts like mean, median, variance, and basic probability

No prior programming experience is required for an introductory R course.

🧾 Course Title: R Programming – Foundations of Data Analysis and Statistical Computing

Course Description:

This R Programming course provides a hands-on, practical introduction to data analysis and statistical computing using the R language—one of the most widely used tools in data science, research, and analytics. Whether you're a beginner or transitioning from another programming language, this course equips you with the essential skills to manipulate, analyze, and visualize data effectively.

Starting with the basics of R syntax and environment setup, the course guides learners through working with data frames, performing statistical analysis, writing custom functions, and generating clear and compelling visualizations using libraries such as ggplot2, dplyr, and tidyr. The course also introduces real-world case studies from domains like healthcare, marketing, economics, and academia.

By the end of this course, learners will be confident in using R for data cleaning, exploration, statistical testing, modeling, and communicating insights through data visualization.

🎯 Key Learning Objectives:

• Understand R programming fundamentals and how to work within RStudio
• Perform data importing, cleaning, transformation, and manipulation
• Conduct descriptive and inferential statistical analysis
• Create compelling charts and graphs using ggplot2
• Apply packages like dplyr, readr, tidyr, and lubridate for efficient workflows
• Write user-defined functions and use control structures
• Build linear models and apply basic statistical tests (t-tests, chi-square, ANOVA)
• Manage and analyze real-world datasets using tidyverse principles

👨‍🏫 Who Should Enroll:

• Students and researchers in statistics, economics, biology, or social sciences
• Aspiring data analysts, data scientists, or biostatisticians
• Professionals working in research, healthcare, marketing, or finance
• Beginners in programming or those looking to shift from tools like Excel/SPSS to R