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Course information

  • Course details
  • Grading
  • Use of AI tools
  • Content licenses

Lesson 1

  • Lesson 1 overview
  • Course environment
  • A brief introduction to NumPy
  • Basic statistical terms and concepts
  • Uncertainty
  • Reporting measurements
  • The normal distribution
  • Exercise 1

Lesson 2

  • Lesson 2 overview
  • Least squares regressions
  • Linear correlation
  • Goodness-of-fit calculations
  • Exercise 2

Lesson 3

  • Lesson 3 overview
  • The diffusion equation
  • Basics of thermochronology
  • Python concepts
  • Exercise 3

Lesson 4

  • Lesson 4 overview
  • Geological advection
  • Solving the advection-diffusion equation
  • Advection and heat transfer
  • Exercise 4

Lesson 5

  • Lesson 5 overview
  • Rocks and ice as viscous materials
  • Viscous flow down an incline
  • Theory for Exercise 5
  • Exercise 5

Lesson 6

  • Lesson 6 overview
  • Low-temperature thermochronology
  • Intro to Exercises 6 and 7
  • Exercise 6

Lesson 7

  • Lesson 7 overview
  • Exercise 7

Final report

  • Final paper information
  • Scientific articles for the final paper
  • Final paper template

Resources

  • Glossary
  • Installing Python

Index

A | N | P | R | S

A

  • accurate measurement

N

  • normal distribution

P

  • population
  • precise measurement

R

  • random error
  • representative sample

S

  • sample
  • systematic error

© Copyright 2016-2025, D. Whipp and A.-K. Maier, Department of Geosciences and Geography, University of Helsinki..

Last updated on Dec 10, 2025.