Using Python and Jupyter

In this class, programming will be an important tool, both for acquiring data from the various sensors we will be working with and for analyzing that data. We will be relying on Python for our work, but it is useful to approach learning to program with the attitude that what you are learning is the semantics of programming - in other words how to convey a particular meaning, or set of instructions, to the computer using programming languages. Although the interpretation of meaning does rely on the specific syntax of the language in use, in this case, Python, abstractly what you are trying to accomplish and the basic steps you should take does not. It is a near-certainty that if you continue in an engineering or science field, you will be expected to become familiar with several programming languages. Though it is true that some languages have deep and fundamental implementation differences, being comfortable with the more universal semantics of computer programming will make the transition from one language to another much more seamless.

To build familiarity with Python and programming more generally, we will spend some time working through several exercises, as well as follow the Programming with Python and Plotting and Programming in Python lessons from Software Carpentry. Specifically, we will work through or assign as homework these lessons:

1. Variables and Assignment
2. Data Types and Type Conversion
3. Built-in Functions and Help
4. Repeating Actions with Loops
5. Storing Multiple Values in Lists
6. Libraries
7. Analyzing Data
8. Analyzing Data from Multiple Files
9. Conditionals
10. Variable Scope
11. Creating Functions
12. Reading Tabular Data into DataFrames
13. Pandas DataFrames
14. Lists Review
15. For Loops Review
16. Plotting Review
17. Multiple Data Sets Review
18. Making Functions Review
19. Conditionals Review

Advanced Concepts:

1. Errors and Exceptions
2. Defensive Programming
3. Debugging
4. Command-line Programs
5. Programming Style

Jupyter notebooks will offer a useful tool for writing, testing, and executing code as you work through the lessons listed below and our class and lab activities. The ability intersperse Markdown comments and blocks of code to be executed, and embed plots makes Jupyter a useful way of documenting and presenting your work. Jupyter cells can also be converted directly to slide decks for your class presentations related to code development.