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Fall 2025 CCNY EAS 42000/A4200

  • Fall 2025, CCNY EAS 42000/A4200: Statistical Methods in Earth and Atmospheric Science

Course Information

  • Syllabus
  • Schedule
  • Lecture notes and slides
  • Links to datasets
  • Resources

Assignments

  • HW00: Getting python and your Github account up and running
  • HW01: Writing key quantities from descriptive statistics in Python yourself
  • HW02: Generating core data visualizations on the Central Park weather dataset in Python
  • HW03: Key probability theory concepts and empirical probabilities computed for Central Park
  • HW04: Fitting normal distributions for each calendar month and fitting GEV to annual maxima
  • HW05: linear regressions
  • HW06: hypothesis tests

Labs

  • Lab 03: computing empirical probabilities, empirical CDFs, and empirical PDFs
  • Lab 04: fitting probability distributions to your data using scipy.stats
  • Special lab: Generative AI tools: using—and not using—them to optimize your learning
  • Lab 05: linear regressions and pandas

Exams

  • Midterm 1

Final Project

  • Final project requirements
  • Repository
  • Open issue

Index

C | E | I | K | M | N | P | Q | R | S | T | U | V

C

  • coefficient of variation

E

  • event
  • expectation

I

  • IQ

K

  • kurtosis

M

  • mean
  • median
  • metadata
  • mode
  • mutually exclusive

N

  • numeracy

P

  • percentile
  • population

Q

  • quantile

R

  • range

S

  • sample
  • sample space
  • skewness
  • standard deviation

T

  • tail

U

  • uncertainty quantification

V

  • variance

By Spencer A. Hill

© Copyright 2025.