Finished cleanup pass on Python tutorial; printed to review on paper

tutorials/README.md

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+# Tutorials
+
+## Python tutorial
+
+### Learning objectives 
+
+In this tutorial, I'm going to show you how to ...
+
+- use basic programming concepts:
+  - assigning values to variables
+  - quotes around text (strings)
+  - interpreting error messages
+  - using `help(method)`
+- use the Python built-in functions:
+  - `sum`
+  - `len`
+  - `print`
+  - `range`
+- define and run new functions
+- Specific requirements from DATA chapter:
+  - `None`
+  - 0-based indexing
+  - attributes and methods
+  - object, instance, class, module
+  - accessing columns using square brackets
+- Specific requirements from PROB chapter:
+  - `range` and `sum` 
+  - know how to define function (e.g. `fH` for Example 3 in Section 2.1)
+  - list comprehension (e.g. `[fH(h) for h in range(0,5)]` for Example 3 in Section 2.1)
+- Specific requirements from STATS chapter:
+  - `for` loop
+- Specific requirements from LINEAR MODELS chapter:
+  - ?
+- Specific requirements from BAYESIAN STATS chapter:
+  - `numpy` arrays
+
+
+
+## Pandas tutorial
+
+### Learning objectives 
+
+
+
+## Seaborn tutorial
+
+### Learning objectives 
+
+- know the general pattern for plotting the graph of function `f` using `numpy` arrays:
+  1. `xs = np.linspace  OR  np.arange  OR  list` 
+  2. `ys = f(xs)`  (
+  3. Call `plt.stem(ys)` or `sns.lineplot(x=xs, y=ys)`
+
+
+
+
+
+
+## Out of scope
+
+- understand when we need to use `vectorize(f)`