W. Trevor King: I dropped everything from the original
e19d4dd except
for the lessons/thw-testing/tutorial.md modification.
Conflicts:
lessons/thw-git/local.md
lessons/thw-git/remote.md
lessons/thw-matplotlib/tutorial.md
lessons/thw-numpy/tutorial.md
lessons/thw-python/data-structures/data_structures.ipynb
lessons/thw-python/data-structures/tutorial.md
lessons/thw-python/flow-control/python_flow_control.ipynb
lessons/thw-shell/tutorial.md
*Uncertainty Quantification* is the process of asking, "Given that our
algorithm may not be deterministic, was our execution within acceptable
error bounds?" This is particularly important for anything which uses
-random numbers, eg Monte Carlo methods.
+random numbers, for example Monte Carlo methods.
# Where are tests?
# Quality Assurance Exercise
-Can you think of other tests to make for the fibonacci function? I promise there
+Can you think of other tests to make for the Fibonacci function? I promise there
are at least two.
Implement one new test in test_fib.py, run nosetests, and if it fails, implement