Prereq: Any introductory course in computer programming, such as CS 115, CS 221 or EGR 102; or any introductory course in statistics, such as STA 381; or instructor permission.
This course introduced the analytical skills necessary to work with large data sets, focusing on applications in the supply chain and in transportation. For the purpose of this course, Big Data is defined as "anything that doesn't fit in an Excel spreadsheet". This course is positioned at the intersection of coding skills, applied statistics and substantive expertise, teaching the practical skills needed to work with increasingly large data sets. Main topics to be covered include: fundamentals of programming and data wrangling in Python, data visualization, applied statistical modeling and interpretation, and ethical issues in data analysis, including matters of intellectual honesty.
This course introduced the analytical skills necessary to work with large data sets, focusing on applications in the supply chain and in transportation. For the purpose of this course, Big Data is defined as "anything that doesn't fit in an Excel spreadsheet". This course is positioned at the intersection of coding skills, applied statistics and substantive expertise, teaching the practical skills needed to work with increasingly large data sets. Main topics to be covered include: fundamentals of programming and data wrangling in Python, data visualization, applied statistical modeling and interpretation, and ethical issues in data analysis, including matters of intellectual honesty.