Learn R, data analysis, visualization, inference, and regression through real-world statistical practice.
What you'll learn
- R Programming & Data Wrangling
- R programming for data analysis
- Writing clean reproducible R code
- Tidyverse data manipulation skills
- Data wrangling with dplyr and tidyr
- Visualizing data with ggplot2
- Handling messy, real-world datasets
- Creating clear, professional plots
- Organizing projects for reproducibility
- GitHub code-along scripts included
- Core Statistical Concepts
- Understanding sampling variability
- Exploring statistical distributions
- Central limit theorem in practice
- Standard error and confidence intervals
- Logic of hypothesis testing
- Null vs alternative hypotheses
- P-values and significance testing
- Comparing statistical tests effectively
- Building analytic intuition hands-on
- Inferential Statistics & Modeling
- Conducting t-tests in R
- ANOVA and group comparisons
- Chi-square test for categorical data
- Linear regression modeling in R
- Understanding assumptions of tests
- Interpreting effect sizes in R
- Practical Data Analysis
- Realistic messy data scenarios
- Iterative analysis and refinement
- Making decisions with uncertainty
- Interpreting results like a researcher
- Guided exercises for practice
- Step-by-step code demonstrations
- Building confidence as a data analyst
- Applying statistics to real projects