MATH 326 - Introduction to Data Science

Our class will discuss data analysis within the context of Data Science. The class consists of three small Data Science projects and a presentation: (i) Dimensionality reduction and classification implementing PCA and t-SNE; (ii) unsupervised learning implementing K-means, GMM, and DBSCAN; (iii) recommendation systems and matrix estimation by implementing clustering, collaborative filtering, and SVD; and (iv) a final presentation of one of the projects. You will learn how to analyze data using Jupyter Notebook and how to summarize findings with key insights from your analysis. Our class will emphasize data pre-processing, analysis, and method implementation: For this reason, you will have access to all necessary Python code and perform only a cursory exploration of the methodsemployed.