Practical Data Science in R: Regression, Forecasting, and Text Mining

Raw data is only valuable if you know how to extract its hidden stories. This course provides a clear, text-based pathway to mastering data analysis, statistical modeling, and predictive analytics using the R programming language. By working through structured written explanations and practical code examples, you will progress from understanding basic statistical foundations to building sophisticated predictive models and analyzing unstructured text. You will gain the confidence to manipulate data, forecast future trends, and uncover meaningful patterns in any dataset. What you'll learn: - Understand foundational statistics, probability distributions, and data visualization principles using modern R packages. - Build and evaluate linear and logistic regression models to identify relationships and make data-driven predictions. - Apply clustering techniques like K-Means to segment data and discover hidden patterns within complex datasets. - Perform text mining and sentiment analysis using modern tidy data workflows to extract insights from unstructured text. - Forecast future trends using time-series models, including autoregressive and moving average approaches. - Implement clean, reproducible data workflows using modern tidyverse conventions and best practices. The course begins with core statistical concepts and data preparation basics before moving into advanced regression, clustering, text analysis, and forecasting techniques. You will learn by reading structured explanations and studying real-world code snippets designed for immediate application. This course is designed for beginners, aspiring data analysts, and professionals looking to add R to their toolkit, with no prior programming or advanced statistical background required. Start your journey into data science and unlock the predictive power of R today.