LNGT 1230

DataScience Across Disciplines

Data Science Across Disciplines
In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Geography, Linguistics, Political Science, or Writing & Rhetoric. This course will use the R programming language. No prior experience with R is necessary.

GEOG: Students will apply data science tools to explore the geography human-environment relationships around protected areas. We will use household survey and land cover data from locations across the humid tropics where the Wildlife Conservation Society has been tracking human wellbeing and forest resource use in high-priority conservation landscapes. Projects and visualizations will be presented back to WCS to inform their ongoing monitoring and management in these sites.

LNGT: In this section, we will learn how to collect and analyze Twitter data in R. We will focus on social metrics and geographical locations to examine language variation in online communities across the United States. While the emphasis will be placed on linguistics, the statistical and analytical tools will help you work with other types of Twitter corpora in the future.

PSCI: Students will use cross-national data to explore relationships between conflict events and political, social, and economic factors in each nation. What factors contribute to conflict and violence? Our focus will be to find patterns in the data using the tools in R and discuss what those patterns suggest for addressing rising conflict and resolving ones that have already experienced violence.

WRPR: Students will learn to conduct writing studies research through working with "big data” from a multiyear survey of first-year college students about their academic confidences, attitudes, and perceptions. We will explore how educational access, identity, and language background impacts survey responses. Using statistical analysis and data visualizations, as well as writing, we will report our findings.
Requirements Fulfilled:
Equivalent Courses:
ENVS 1230 *
MATH 0118
ECON 0210
JAPN 1230 *
MATH 0216
SOCI 1230 *
FMMC 1230 *
MATH 0201
MATH 0116
MATH 1230
BIOL 1230 *
WRPR 1230 *
GEOG 1230 *
STAT 0118 *
NSCI 1230 *
ECON 1230 *
ECON 0111 *
HARC 1230 *
STAT 0201
PSCI 1230 *

Sections in Spring 2007