Complex Systems: Data Science I - Experience

Data harvesting, cleaning, and summarizing; working with non-traditional, non-numeric data (social network, natural language textual data, etc.); scientific visualization; advanced data pipelines with a practical focus on real datasets and developing good habits for rigorous and reproducible computational science; Project-based. Credit not awarded for both CSYS 5870 and CS 3870. Prerequisites: Knowledge of CS 1210 and either STAT 1410 or STAT 2430 required; knowledge of CS 2100 and MATH 2522 or MATH 2544 recommended; Graduate student or Instructor permission. Cross-listed with: STAT 5870, CS 5870.

Prereqs: Graduate student or instructor permission; Knowledge of CS 1210 and either STAT 1410 or STAT 2430 assumed; Knowledge of CS 2100 and MATH 2522 or MATH 2544 strongly recommended; Cross-listed with CS / STAT 5870; Total combined enrollment: 30 Open to degree and PACE students

Extracting meaning from data remains one of the most important tasks of science and industry. The Internet and modern computers have given us vast amounts of data, so it is more important than ever to understand how to collect, process, and analyze these data. A picture is worth a thousand words, so visualizations, from scientific plots and infographics to interactive data explorers, are crucial to summarize and communicate new discoveries.

Learning objectives In this course students will learn: 1. basic data harvesting and storage with automated computer programs, 2. data “munging” or cleaning to process data, 3. analyzing data with existing methods such as descriptive statistics and visualizations, 4. developing new, problem-specific measures to explore trends and features in data, and 5. communicating data-driven results. Particular emphasis will be placed on nontraditional (non-numeric) data such as networks, text corpora, etc. and on developing good habits for rigorous and reproducible computational science. Programming This is a programming-intensive course taught using Python, and homework and projects will use Python (version 3). Python is very popular in industry and is free, easy to learn, and has many useful third-party packages. While you should have prior programming experience (such as UVM’s CS021), experience with Python is not necessary. Early lectures include reviews of Python programming and how to set up your working environment.

Grades 35% for homework, 15% for quizzes, 15% for each of two midterm projects, 20% for final project and presentation.