Class Calendar Length
Year Long
Grade Levels
11, 12
Prerequisites
Algebra II
Seats Available
10-12
Class Schedule
Mon through Fri, 10 to 10:47 am
Course Summary
This rigorous, college-level mathematics course prepares students for the AP exam through an “Experience First, Formalize Later” approach to data analysis. Students will go beyond simple numbers to discover patterns, trends, and relationships in real-world data sets, mastering the methods used by modern researchers and data scientists. The curriculum covers probability distributions, statistical simulations, and the reasoning required to justify complex claims. By the end of the term, students will possess the hands-on technology skills and analytical depth needed to draw sound, evidence-based conclusions from any complex data set.
Key Outcomes
- Execute advanced data analysis and interpretation of quantitative variables.
- Master the use of statistical technology to perform simulations and inferences.
- Construct sound statistical arguments based on random processes and surveys.
Mass. State Standards
- S-ID.A–C: Data Interpretation (Summarizing categorical and quantitative data)
- S-IC.A–B: Statistical Inference (Evaluating random processes and conclusions)
- S-CP.A–B: Probability (Independence and conditional probability)
- S-MD.A–B: Decision Making (Expected values and evaluating outcomes)
About the Instructor
Kelsey Felder is a veteran mathematics educator at Codman Academy, where she has spent the last eight years teaching a wide range of subjects, including Algebra, Precalculus, and her personal favorites, AP Statistics and AP Calculus. Kelsey holds a BA in History and English from Texas A&M University-Kingsville and a Master’s in Russian, East European, and Eurasian Studies. She is currently pursuing a PhD in Russian History at Brandeis University, bringing a unique interdisciplinary perspective to her math classroom. Beyond her teaching, Kelsey is an avid marathon runner and is passionate about creating a student-centered environment where every learner feels empowered to master the language of data.