Introduction to Computational Modeling logo Introduction to Computational Modeling

Thinking about learning objectives as a process of modeling

1 - Modeling begins before you ever touch a computer

1.1 For a given situation-to-be-modeled, Students will be able to

2 - Formalizing the model

2.1 When it comes to formalizing models, students will be able to

In Scratch:

In base Python:

In NumPy / Pandas

Across different formalisms of a model:

3 - Interrogating the model

How we think about interrogating the model

3.1 Learning Objectives: For a given model, students will be able to

4 - Refining the model

4.1 - Learning Objectives: Students will be able to

5 - Sharing/publicizing the model (reproducible research/open science)

5.1 - Learning Objectives: Students will be able to

6 - Assessing/Comparing/Debating models

// Some of the really cool intellectual meat. Given the kind of diagnostic information we get (cluster 3) and possibly being able to share our models (cluster 5), how do we make decisions about how well the model is working and whether to refine it (cluster 3). This is where we get into the work of justifying a model we’ve built, exploring whether our asusmptions (cluster 1) held sway, and so on.

// 2015-08-26 - I actually think much of this is covered by the first 5 clusters :-/

7 - Data Science

Understanding data


Fitting a model to data