This review discusses the importance of dimensionality reduction in analyzing large-scale neural recordings. It highlights three key motivations for population studies: single-trial statistical power, population response structure, and exploratory data analysis. Dimensionality reduction methods help uncover features of neural activity that are not apparent at the individual neuron level. These methods produce low-dimensional representations of high-dimensional data, preserving or highlighting specific features of interest. They have been used to study various phenomena, including decision-making in the prefrontal cortex, movement preparation in the motor system, and odor discrimination in the olfactory system. Dimensionality reduction is also useful for exploratory data analysis, helping to generate hypotheses about neural activity patterns. The review provides an overview of common dimensionality reduction methods, such as principal component analysis (PCA) and factor analysis (FA), and discusses their applications in neuroscience. It also addresses the selection of appropriate methods, the interpretation of results, and potential pitfalls. The review emphasizes the importance of dimensionality reduction in systems neuroscience, as it allows researchers to analyze large-scale neural data and gain insights into the underlying mechanisms of neural activity.This review discusses the importance of dimensionality reduction in analyzing large-scale neural recordings. It highlights three key motivations for population studies: single-trial statistical power, population response structure, and exploratory data analysis. Dimensionality reduction methods help uncover features of neural activity that are not apparent at the individual neuron level. These methods produce low-dimensional representations of high-dimensional data, preserving or highlighting specific features of interest. They have been used to study various phenomena, including decision-making in the prefrontal cortex, movement preparation in the motor system, and odor discrimination in the olfactory system. Dimensionality reduction is also useful for exploratory data analysis, helping to generate hypotheses about neural activity patterns. The review provides an overview of common dimensionality reduction methods, such as principal component analysis (PCA) and factor analysis (FA), and discusses their applications in neuroscience. It also addresses the selection of appropriate methods, the interpretation of results, and potential pitfalls. The review emphasizes the importance of dimensionality reduction in systems neuroscience, as it allows researchers to analyze large-scale neural data and gain insights into the underlying mechanisms of neural activity.