COMPUTING IN DATA SCIENCE AND STATISTICS
A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.
Prerequisites & course information
Requisites: Declared in Statistics MS or Statistics VISP (undergraduate) Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Understand and apply the basics of the Python programming language and basic programming patterns in both the object-oriented and functional programming frameworks Audience: Graduate 2. Collect and clean data from a variety of data sources including markup languages from the web, databases, and by using APIs Audience: Graduate 3. Understand the MapReduce framework and apply it to large-scale data sets in a distributed environment using modern cloud computing platforms Audience: Graduate 4. Use numerical and scientific computing libraries to build and fit statistical models on large datasets Audience: Graduate Learning Outcomes: 1. Understand and apply the basics of the Python programming language and basic programming patterns in both the object-oriented and functional programming frameworks Audience: Graduate 2. Collect and clean data from a variety of data sources including markup languages from the web, databases, and by using APIs Audience: Graduate 3. Understand the MapReduce framework and apply it to large-scale data sets in a distributed environment using modern cloud computing platforms Audience: Graduate 4. Use numerical and scientific computing libraries to build and fit statistical models on large datasets Audience: Graduate
Sources
- Course catalogue ↗2026-2027