Data Analysis R

Course Outline:

1. Introduction to Data Analysis and R
2. Data Manipulation with dplyr and tidyr
3. Data Visualization with ggplot2
4. Descriptive Statistics and Exploratory Data Analysis (EDA)
5. Probability Distributions and Sampling
6. Hypothesis Testing and Confidence Intervals
7. Correlation and Regression Analysis
8. Multivariate Analysis
9. Machine Learning for Statistical Analysis with caret
10. Case Studies and Real-world Applications

Skills You Will Learn:

• Understanding of statistical concepts and methods
• Proficiency in data manipulation using dplyr and tidyr
• Ability to create insightful visualizations with ggplot2
• Knowledge of probability theory and its applications in R
• Expertise in hypothesis testing and interpreting results
• Familiarity with advanced statistical techniques and machine learning algorithms using caret

Requirements:

• Basic understanding of statistics
• Familiarity with programming concepts (R programming experience is a plus but not required)
• Access to a computer with R and RStudio installed
• Eagerness to learn and apply statistical techniques to real-world datasets
• Strong analytical and problem-solving skills