In this hands-on course, learners will acquire the skills and knowledge needed to perform data analysis using R, a popular programming language and environment for statistical computing and graphics. Through a combination of lectures, tutorials, and practical exercises, learners will learn how to import, manipulate, visualize, and model data using R
Course Modules
Module 1: Introduction to R programming language
- Downloading and installing R programming
- Downloading and installing RStudio
- What is R?, Why R?, What is RStudio?
- Difference between R and RStudio
- R and RStudio workspace, Update R and R studio
- R and its associated packages
- Importing and exporting data in R
- Data manipulation
- Data visualization in R
- Merging datasets in R
Instructor: Dr. Hellen Namawejje
Module 2: Exploratory Data Analysis (EDA)
- What is EDA?, Importance of EDA?
- Overview of methods of EDA
- Summarizing quantitative and qualitative variables
- Description of population distribution- measure of spread and dispersion
- Exploring relationships between two variables
- Non-parametric tests
Instructor: Prof. Susan Balaba Tumwebaze and Dr. Hellen Namawejje
Module 3: Regression analysis
- Scatter plots and Correlation with R
- Correlation matrix
- Linear regression
- Ordinary least squares regression
- Analysis of variance table
- Assumptions under linear regression
- Diagnostics and validation
- Model building and selection
- Multiple linear regression
- Indicator variables
- Polynomial regression
Instructor: Dr. Odongo Thomas and Prof. Susan Balaba Tumwebaze
Module 4: Generalized Linear Models
- Categorical data analysis
- Chi-square
- Logistic regression
- Log-linear regression
- Ordinal regression
- Poisson Regression