📘 Course Overview

This course introduces learners to R programming and RStudio as powerful tools for data analytics, visualization, and statistical modeling. Beginning with the basics of R syntax and data structures, students will progress through data wrangling, exploratory data analysis, statistical foundations, and predictive modeling techniques. The course emphasizes hands-on practice with real-world datasets and projects drawn from business, social sciences, public health, and sports analytics.

By the end of the course, learners will be able to confidently analyze, visualize, and interpret data using R, preparing them for roles in data analysis, research, and business intelligence.

🎯 Why Learn This Course?

• Industry Demand: R is widely used in research, academia, and industries like finance, healthcare, and marketing analytics.
• Practical Skills: Gain end-to-end skills, from cleaning raw data to building predictive models and communicating results with impactful visualizations.
• Career Growth: Opens pathways to careers such as Data Analyst, Research Analyst, Statistician, and Business Intelligence Specialist.
• Research & Reporting: Essential for professionals and students conducting academic or applied research who need tools for advanced statistical analysis.

👥 Who Should Enroll?

This course is designed for:

• University students in statistics, economics, computer science, or social sciences.
• Professionals in business, finance, healthcare, education, and NGOs looking to enhance their data analysis skills.
• Researchers and academics who want to use R for data-driven insights and publishing.
• Beginners in analytics who want a structured pathway into data science.

📌 What Will Be Learned?

Learners will:

• Set up R and RStudio, manage packages, and write scripts.
• Work with data structures (vectors, lists, factors, data frames).
• Perform data wrangling, cleaning, and transformation using dplyr, tidyr, and related packages.
• Conduct exploratory data analysis (EDA) with summary statistics, visualizations, and grouped insights.
• Understand statistical foundations (distributions, hypothesis testing, confidence intervals).
• Build and interpret linear and logistic regression models.
• Apply regression diagnostics and model selection techniques.
• Work on real-world projects in multiple domains.
• Create impactful data visualizations with ggplot2 and export results for professional reporting.

🔑 Pre-requisites

• No prior R knowledge required – the course starts from the basics.
• Basic computer literacy (installing software, navigating interfaces).
• Foundational math and statistics knowledge (mean, variance, probability basics) is recommended.
• Familiarity with spreadsheets (Excel or Google Sheets) is a plus, but not mandatory.