1. Installing Python and VS Code.
2. Arithmetic in Python
3. Application:interpolation
4. Lists and the for loop
5. if-else statements
6. while loops
7. Tuples and list comprehensions
8. Functions
9. Review of loops and functions
10. Root finding revisited
11. Introduction to files
12. Sorting lists
13. Processing lists of tuples
14. Introduction to classes
15. Plotting with pyplot
16. Introduction to Jupyter and numpy
17. More numpy examples
18. Introduction to pandas
19. Cycling regression model
20. JSON data format
21. Introduction to databases
22. Python time package
23. More on sorting
24. More on recursion
25. Introduction to sympy
26. Introduction to flask
27. Additional Python features
28. Example problem

Syllabus

The syllabus for this course is here.

The midterm exam is coming up on Friday, Feb. 5. Here are some materials to help you prepare.

Here is the midterm exam. Your work on the exam is due by 3:10 on Feb. 6.

Here is information on the final exam, including practice problems.

Here is the final exam. Your work on the final is due by 7 PM on March 17.

1. Linear interpolation: due Jan. 11.
2. Printing a table: due Jan. 13.
3. Computing a root: due Jan. 15.
4. Working with tuples and list comprehensions: due Jan. 20.
5. Newton's method: due Jan. 22.
6. Finding outliers: due Jan. 25.
7. Working with files: due Jan. 27.
8. Classifying bank notes: due Jan. 29.
9. Rational number class: due Feb. 3.
10. Differential equations: due Feb. 8.
11. Working with vectors in numpy: due Feb. 10
12. Cycling regression model: due Feb. 19
13. GDP and population growth: due Feb. 22
14. SQL GDP: due Feb. 26
15. Better QuickSort: due March 3
16. Interpolation: due March 5