Engineering_Computations_M1.pdf (1.53 MB)

# Engineering Computations Module 1: Get data off the ground

online resource
posted on 05.12.2017, 23:02
Engineering Computations
—Original material written as Jupyter Notebooks for an undergraduate engineering course, Fall 2017

Module 1: Get data off the ground

This course assumes no coding experience, so the first three lessons are focused on creating a foundation with Python programming constructs using essentially no mathematics. The fourth lesson introduces the basic data structure in scientific computing: arrays. The final lesson is a worked example of linear regression with real data.

Lesson 1: Interacting with Python.

Background: What is Python? Idea of interpreted vs. compiled language. Why use Python? It is a general-purpose and high-productivity language. Getting started: interactive Python (IPython). Using Python as a calculator. New concepts: function, string, variables, assignment, type, special variables (True, False, None). Supported operations, logical operations. Reading error messages.

Lesson 2: Play with data in Jupyter

What is Jupyter? Working with Jupyter. Playing with Python strings: assignment, indexing, slicing. String methods: count, find, index, strip, startswith, split. Play with Python lists: assignment, nested lists, indexing, slicing. String methods: append, index. List membership. Iteration with for-statements. Conditionals.

Lesson 3: Strings and lists in action

A full example using what you learned in lessons 1 and 2: playing with a text file containing the MAE Bulletin (list of courses with their numbers, description, pre-requisites). Reading a data from a file. Cleaning and organizing text data.

Lesson 4: Play with NumPy arrays

Two of the most important libraries for scientific computing with Python: NumPy and Matplotlib. Importing libraries. NumPy functions to create arrays: linspace, ones, zeros, empty, copy. Array operations. Multidimensional arrays. Performance advantage of arrays over lists. Drawing 2D line plots of array data.

Lesson 5: Linear regression with real data

A full worked example using real data of earth temperature over time. Step 1: reading data from a file. Step 2: plotting the data; making beautiful plots. Step 3: least-squares linear regression. Step 4: applying linear regression using NumPy. Split regression.

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Note—If you have suggestions for changes or improvements to this material, please open an issue on the GitHub repository.