Data Science for Sustainability Concentration

Students will learn the history and basics of computing as well as the fundamentals of Python programming. General topics include: the history of computing, number systems, Boolean logic, algorithm design and implementation, and modern computer organization. Programming topics include: memory and variables, data types, mathematical operations, basic file I/O, decision-making, repetitions, functions, and list basics.

Students will analyze data using Python programming language to obtain insights from real-world data sets. Students will access data files containing both numerical and categorical data, which they will prepare for analysis by data preprocessing, data wrangling, and data aggregation. Students will conduct statistical analysis, text analysis, and date/time series analysis, summarize their findings in a meaningful manner, and use visualization to illustrate patterns and trends in the data.

Big Data refers to large, diverse data sets that are too complex to manipulate or query with standard tools.. In this course, students will apply a dynamic approach to uncovering patterns, unknown correlations, and other useful insights from diverse, large-scale datasets. Students will use map-reduce and distributed file systems in the Hadoop ecosphere for storing and analyzing data so that it can be used to uncover trends, patterns, and correlations.

Data visualization-representing complex data in a visual format-is a way to communicate and explore patterns, trends, and relationships in a data set. In this course, students will examine a variety of data sets to create data visualizations and use them to explore, interpret, and present information. After transforming data sets into visual graphics, students will then create business reports and presentations designed to drive effective decision-making.

Geospatial communication is a fundamental element of contemporary life. Students will explore the theory, method and application of spatial analysis and Geographic Information Systems (GIS) for various applications, including environmental and social policy, public participation, cultural geography, education and public participation technology, and agent-space interaction. Through laboratory exercises and guided discussions, students will effectively ask and answer spatially-based social, environmental and technological questions to develop applied GIS and spatial analysis skills, critical geographic thinking and communication.