0 or 3 credits
Spring 2026 Laboratory Lecture Upper Division
Students will apply data processes including identifying data needs, acquiring data, assessing data quality, data wrangling, filtering, and visualization. In each of several topic areas (forestry, animal science, agronomy, food science, entomology, engineering, economics), data-driven insights and improved decision making will be the culmination of applied data skills. Students will understand data ethics and practice data management skills including the merging of disparate but related data sets.
Learning Outcomes1Construct a research question that helps address a decision.
2Describe different types of experimental designs and discuss the differences between observational and experimental studies.
3Identify data needed to address various research questions.
4Identify how these data sources are used in data analysis: agronomics, machine data, maps, spreadsheets, sensor data.
5Describe how various data sets are acquired.
6Describe how the following impact data ethics: ownership, storage, access.
7Assess data quality and utility.
8Identify potential limitations of a dataset.
9Describe the following aspects of data wrangling: data formats, data compatibility, mobility.
10Describe the following aspects of data management: storage, curation, metadata, FAIR (findable, accessible, interoperable, reusable).
11List reasons for filtering, cleaning, and pre-processing data.
12Describe tools for data cleaning.
13Integrate disparate data sets.
14Describe uses for the following in data visualization: bar charts, line charts, maps, tables.
15Use the following tools to analyze data: correlations, mean generation, confidence intervals, simple model building, R Python.
16Make decisions based on data outcomes.
Prerequisites
GPA by professor
made by Purdue students.