PIC 16 Python with Applications
You can download the course information here.
Student projects from past courses:
- Winter 2018
- Spring 2018
Fall 2018 Tentative Course Schedule
Week 0
- 09/28 Lecture 1: Course Overview
– Follow instruction in GettingStarted.pdf on CCLE
– Reading material: Python tutorial 1 and 2.1.2
Week 1
- 10/01 Lecture 2: Python Basics - Basic Data Types, commenting, list, dictionary, functions, modules
– Reading material: Python tutorial 3.1.1, 3.1.2, 3.1.4, 5.1, 5.5 - 10/03 Lecture 3: Python Basics - Control Flow, Functions
– Reading material: Python tutorial 4.1 - 4.7 - 10/05 Lecture 4: Python Basics - Data Structures
– Reading material: Python tutorial 5.2 - 5.5
Week 2
- 10/08 Lecture 5: Python Basics - Functional Programming
– Reading material: Python tutorial 6.1, 8.1 - 8.4, 5.7 - 10/10 Lecture 6: Python Basics - Classes and Objects, Magic Methods
– Reading material: tutorialspoint - 10/12 Lecture 7: Regular Expressions I
– Reading material: tutorial
– Cheat sheet
– To test your regular expressions: pythex
** Homework 1 due Wednesday by 5pm
Week 3
- 10/15 Lecture 8: Regular Expressions II
- 10/17 Lecture 9: Visualization I: Turtle, Matplotlib and pyplot
– Recursion
– Matplotlib tutorial - 10/19 Lecture 10: Visualization II: Pandas, Matplotlib and Numpy
– Pandas Tutorial
– Pandas Basics Cheat Sheet (on CCLE)
** Homework 2 due Wednesday by 5pm
Week 4
- 10/22 Lecture 11: Visualization III: More on plotting
– Numpy tutorial - 10/24 Lecture 12: GUI I: Tkinter
– An Introduction To Tkinter - 10/26 Lecture 13: GUI II: Tkinter bouncing ball example
** Homework 3 due Wednesday by 5pm
Week 5
- 10/29 Lecture 14: GUI III: Inheritance
– Executing Modules as Scripts
– Class Inheritance and Overloading Methods - 10/31 Lecture 15: GUI IV: Qt Designer and PyQt
– Qt Designer Documentation
– PyQt5 Tutorial - 11/02 Lecture 16: Page-Rank algorithm
– NetworkX Tutorial
– Read from “Creating a graph” to “Directed graphs”
– PageRank Algorithm
– (Optional) PageRank Algorithm Implementation
** Homework 4 due Wednesday by 5pm
Week 6
- 11/05 Lecture 17: Image Processing I: Grey-scale images and adding noise to an image
– Matplotlib image tutorial - 11/07 Lecture 18: Image Processing II: Uniform Blur and Gaussian Blur
– Blurring for Beginners - 11/09 Lecture 19: Image Processing ctd (edge detection) and NLTK
– Read the introduction to Chapter 1 of the NLTK Book.
– Follow 1.2, 1.3, 1.4
– Follow Chapter 1, section 3 (all)
– Skim Chapter 1, section 5. This will give you a good overview of the issues in natural language
– Skim Chapter 3 for processing raw text processing.
** Homework 5 due Wednesday by 5pm
Week 7
- 11/12 Veterans Day holiday: No class
- 11/14 Lecture 20: NLTK ctd (Haiku)
- 11/16 Lecture 21: Scrapy
– W3 Schools HTML Intro
– W3 Schools XML Intro
– “What is the DOM” in W3 Schools XML DOM Intro
– XML DOM Nodes
– Scrapy Tutorial
** Homework 6 due Wednesday by 5pm
Week 8
-
11/19 Lecture 22: Symbolic Math I
– Sympy Tutorial -
11/21 Lecture 23: Symbolic Math II, Scipy
– Sympy Tutorial -
11/23 Thanksgiving holiday: No class
Week 9
-
11/26 Lecture 24: Machine Learning I: KNN and K-means clustering
– Scikit-learn: Nearest Neighbors Classification
– K-nearest Neighbors Wikipedia
– Scikit-learn: K-Means Clustering
– Visualizing K-Means clustering -
11/28 Lecture 25: Machine Learning II: SVM
– Machine learning: the problem setting
– Scikit-learn: Support Vector Machines (SVMs) -
11/30 Lecture 26: Machine Learning III: NMF
– Scikit-learn: Non-negative Matrix Factorization
** Homework 7 due Friday by 5pm
Week 10
-
12/03 Lecture 27: Socket (Networking)
– The first half (16.5 minutes) of Python Advanced Tutorial 6 – Networking
– Socket module documentation
– Python Network Programming
– (Optional) How does ping work? -
12/05 Lecture 28: Catch up
-
12/07 Lecture 29: Final Review
** Homework 8 due Friday by 5pm