Data Science with R: Sampling & Linear Regression

Are you looking to understand the statistical backbone of data science and build your analytical skills? This course introduces you to two fundamental pillars: sampling theory and linear regression, using the powerful R programming language. By the end of this course, you will be able to confidently apply various sampling techniques, perform exploratory data analysis, and construct, interpret, and evaluate linear regression models, enabling you to extract meaningful insights from data. What you'll learn: Understand the principles of different sampling methods and their importance in data collection. Apply R to implement sampling techniques and prepare data for statistical analysis. Master the theory and assumptions behind simple and multiple linear regression models. Build and interpret linear regression models in R, including coefficient interpretation and model diagnostics. Practice evaluating model performance and understanding its limitations. Utilize modern R packages like dplyr and ggplot2 for data manipulation and visualization. Create reproducible analysis reports using RMarkdown for clear communication of results. The course begins with foundational statistical concepts and sampling methodologies, then transitions into the practical application and interpretation of linear regression models. You will progress from understanding theoretical principles to hands-on data analysis using R. This course is designed for absolute beginners in data science and statistics who want to learn how to analyze data using R. No prior experience with R programming or advanced statistics is required. Begin your journey into data analysis and predictive modeling today.