This Harvard course is designed for individuals with a keen interest in data analysis and interpretation. Throughout the program, you will delve into the field of data science and its practical applications. The course begins by providing a comprehensive understanding of the mathematical concept of distance. This knowledge forms the basis for exploring the singular value decomposition (SVD) and its role in dimension reduction, as well as multi-dimensional scaling and its connection to principal component analysis.
One of the most challenging data analytical problems in genomics today is the batch effect. In this course, you will learn about the techniques used to detect and adjust for batch effects. Specifically, the course covers principal component analysis and factor analysis, demonstrating how these concepts are applied to visualize and analyze high-throughput experimental data.
In addition to genomics, this course also introduces you to the exciting field of machine learning. You will gain a fundamental understanding of clustering analysis, focusing on algorithms such as K-means and hierarchical clustering. Furthermore, the course explores prediction algorithms, including k-nearest neighbors, while emphasizing the concepts of training sets, test sets, error rates, and cross-validation.
Upon completion of this course, you will possess a solid foundation in data science principles and techniques. You will be equipped with the necessary skills to confidently analyze and interpret complex datasets, particularly in the context of genomics. Join us on this captivating journey to unlock the potential of data science in various domains.