Course Description
Introduction to the life cycle of data starting from data collection to cleaning, management, storage, sorting, provenance, visualization, and analysis. A rigorous overview of methods for text mining, image processing, linear models, and scientific computing. Core concepts in supervised and unsupervised analytics, dimensionality reduction, and data visualization will be explored in depth.
Athena Title
Foundations Data Science
Prerequisite
Permission of department
Semester Course Offered
Offered fall
Grading System
A - F (Traditional)
Course Objectives
The goal of this course is for students to learn in-depth knowledge of data science with the R and Python languages. Students will learn about the life cycle of data, starting from data collection to inference. In the process students will learn about data management, storing, privacy, and security, as well as data visualization techniques and the theoretical justifications for statistical techniques in linear models, classification, clustering, and dimensionality reduction, learn how to implement them within the R and Python framework, and apply them to extracting knowledge from scientific datasets. Upon completion of this course, students will gain a basic understanding of the broad class of statistical models. They will be able to analyze data by designing an algorithmic pipeline, leverage existing Python packages and R libraries and incorporate their own to implement the pipeline, and visualize the results of their computations, for drawing valid conclusions from data.
Topical Outline
Introduction to data science, data collection, data cleaning, data management, data storing, data privacy and security, data ethics, data querying and provenance, data handling in R, scientific programming with Python, handling missing data, basic linear regression, hypothesis testing, model building, traditional and modern variable selection techniques, model diagnostics, dimensionality reduction, logistic regression, supervised classification, unsupervised clustering, convex optimization, data visualization, introduction to "big data."
Syllabus
Public CV