DATA SCIENCE MODELING I
Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown with applications to a wide variety of data to address open-ended questions.
Topics
- the use of an integrated statistical computing environment
- data wrangling
- the R programming language
- data graphics and visualization
- random variables and concepts of probability including the binomial and normal distributions
- data modeling
- statistical inference in one- and two- sample settings for proportions and means
- simple linear regression
- and report generation using R Markdown with applications to a wide variety of data to address open-ended questions
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: Satisfied Quantitative Reasoning (QR) A Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Comm QR - Quantitative Reasoning B Breadth - Natural Science Level - Intermediate L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. wrangle data: transform data, possibly from multiple sources, into a form convenient for analysis Audience: Undergraduate 2. explore data: visualize and summarize data, generate questions/hypotheses, and address them Audience: Undergraduate 3. program: write R code using the R Studio integrated statistical computing environment to carry out reproducible data analysis Audience: Undergraduate 4. model data: use probability and random variables in statistical computing environment to carry out reproducible data analysis Audience: Undergraduate 5. interpret data: explain what can be inferred from the data analysis and make predictions Audience: Undergraduate 6. communicate: use R Markdown to integrate prose, visualizations, code, interpretation, and results Audience: Undergraduate 7. collaborate: work with other students to solve data challenges Audience: Undergraduate 8. make statistical inferences: employ confidence intervals and hypothesis tests using both computational and standard methods such as binomial and t-tests Audience: Undergraduate Learning Outcomes: 1. wrangle data: transform data, possibly from multiple sources, into a form convenient for analysis Audience: Undergraduate 2. explore data: visualize and summarize data, generate questions/hypotheses, and address them Audience: Undergraduate 3. program: write R code using the R Studio integrated statistical computing environment to carry out reproducible data analysis Audience: Undergraduate 4. model data: use probability and random variables in statistical computing environment to carry out reproducible data analysis Audience: Undergraduate 5. interpret data: explain what can be inferred from the data analysis and make predictions Audience: Undergraduate 6. communicate: use R Markdown to integrate prose, visualizations, code, interpretation, and results Audience: Undergraduate 7. collaborate: work with other students to solve data challenges Audience: Undergraduate 8. make statistical inferences: employ confidence intervals and hypothesis tests using both computational and standard methods such as binomial and t-tests Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027