Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

George Washington University

Get Data Off the Ground with Python

George Washington University via Independent


This first module in Engineering Computations (EngComp1) creates a foundation with Python programming, for complete beginners. You learn to handle core data types (strings, lists) and also n-dimensional arrays. The first three lessons have essentially no mathematics, to focus on the programming patterns. The fourth lesson deals with creating and operating on arrays. The final lesson is a worked example of linear regression with real data.

The target audience is first-year engineering students, but the course would be equally useful to students in any science or technology field.


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.

Taught by

Lorena A. Barba


Start your review of Get Data Off the Ground with Python

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.