Course Summary:

This hands-on course, custom-designed by Magna Skills, equips professionals with the practical skills to perform data analysis using R—one of the most powerful open-source programming languages for statistical computing. Participants will learn to import, clean, manipulate, visualize, and analyze data, turning raw data into actionable insights. The course bridges the gap between theory and practice, focusing on real-world applications in research, policy-making, monitoring & evaluation, and reporting.

Whether you're in government, NGO, academia, or private sector, this course will enable you to confidently handle data-driven projects using R.

Course Objectives:

1. By the end of this course, participants will be able to:

   1. Understand the fundamentals of R programming for data analysis.

   2. Import, clean, and manipulate datasets efficiently using R packages like dplyr and tidyr.

   3. Perform exploratory data analysis (EDA) and generate insights using data visualization techniques with ggplot2.

   4. Conduct statistical tests and basic predictive modeling with R.

   5. Apply data analysis skills to real-world projects in sectors such as health, education, finance, or governance.

Course Outline

1. Introduction to R and RStudio

   • Installing R & RStudio

   • Navigating the RStudio interface

   • Writing and running R scripts

2. Working with Data in R

   • Importing CSV, Excel, and web-based data

   • Data types and structures

   • Data frames, lists, and vectors

3. Data Cleaning and Manipulation

   • Handling missing values

   • Filtering, selecting, and arranging data with dplyr

   • Merging and reshaping datasets with tidyr

4. Data Visualization with ggplot2

   • Creating bar charts, histograms, boxplots, and scatter plots

   • Customizing themes and labels

   • Creating dashboards and reports

5. Descriptive Statistics

   • Measures of central tendency and dispersion

   • Frequency tables and cross-tabulations

   • Summary statistics by group

6. Inferential Statistics

   • Hypothesis testing: t-tests, chi-square, ANOVA

   • Confidence intervals

   • Interpreting p-values and statistical significance

7. Regression and Correlation Analysis

   • Linear regression modeling

   • Correlation matrices

   • Residual analysis and model diagnostics

8. Time Series and Forecasting (Optional Module)

   • Time series decomposition

   • Forecasting with ARIMA

   • Visualization of trends and seasonality

9. R Markdown and Reporting

   • Creating dynamic reports

   • Exporting to PDF, HTML, and Word

   • Automating data analysis reports

10. Capstone Project: Real-World Data Analysis

• Individual or group project using actual datasets

• Presentation of findings

• Feedback and course wrap-up

Course Format

• Duration: 1 or 2 Weeks (Intensive face-to-face or live online sessions)

• Tools Needed: Laptop with R & RStudio installed

• Assessment: Quizzes, Assignments, Capstone Project

• Certificate: Issued by Magna Skills upon successful completion