When dealing with high-dimensional datasets, it can be overwhelming and difficult to know where to begin. Traditionally, visual exploration is the first step, but when faced with numerous dimensions, conventional methods may prove inadequate. Fortunately, there are visualization techniques specifically designed for high-dimensional data, which will be covered in this course. Upon exploring the data, it is often discovered that many features provide little valuable information due to lack of variance or duplication. In this course, you will learn how to identify and eliminate these redundant features, allowing you to focus on the informative ones. Subsequently, when building a model using these features, it may become apparent that certain features have no impact on the desired prediction. You will acquire the skills to detect and remove these irrelevant features, thereby reducing dimensionality and simplifying the analysis. Lastly, this course will teach you about feature extraction techniques that can effectively reduce dimensionality. By calculating uncorrelated principal components, these techniques enable you to streamline the dataset and enhance its interpretability.