Data science: Python

This page contains links to Python programming language content.

This content was used in a data science course for computer science majors who may have not used Python before.

This content will not be updated again until I need to use it for another course - which might not happen.

For specific content on data science, see Data science: topics .

Introduction to computer science

The Python programming language

Python: A first program

Edit-compile-run in more detail

Python 3 installation

Area using trapezoidal method

Pipes and redirection

The Python command line

Loops

Python integers

Python list processing

Python: Output statements

Python: Input statements

Python: Assignment statements

Here are some topics that are more advanced.

Python: Strings

Python for statement

Python list comprehensions

Fibonacci numbers (write a short Python program to do this before coming to class)

Python: Function arguments

Python: Types

Code and data driven programming

Python: Floating point approximations

Compiler generated code: introduction

CS: code improvement

Code relocatability

Data science and data

Python: Stack traces

Python: Code timing

Python: Comparison operator chaining

Module examples

OOP language examples

Python classes

Set theory for data science

Decision trees

Python find and replace

Regular expressions

Regular expressions: Tester

String rewriting examples

Python: Word extraction

Word clouds using Python

Python: __main__

Python: Short circuit evaluation

Python: Ternary operator

Python simple statistics

Probability estimation

Matplotlib: charts

Regression testing

LCS in Python

Longest common subsequence

Python: Slices

Python: Passing actual parameters

Data categories

Graphics coordinate systems

Matplotlib: DPI tricks and plot class and rsPlot module

Linear equations

Regression and correlation (we will finish this on Thursday)

Matplotlib: Linear regression

Simple linear relationships

Python: Walrus operator

Truth tables: programmed method (as a non-trivial example of the Python walrus operator)

Matplotlib: subplots and image formats

Matplotlib: Chart types

Matplotlib: Color models

Python: With statement

Summarizing data : The M&M Problem

Closeness: arithmetic and geometric progressions

Matplotlib: World population (to be finished next time)

Python: Getters and setters

Math: Exponents and logarithms

Distributions and sampling

Python: Normal and exponential distributions

Prediction from data

Python: Dates and time

Web scraping

XML: Extensible markup language

RSS: Really Simple Syndication

Python: Web scraping

Python: Iterators and generators

Data clustering

Equivalence relations: math

Python: OpenXML docx files

Pseudo random sequence

Raspberry Pi: random number generator

Python: map, filter, reduce

Misleading charts

Auto-complete search terms

Cron jobs

Bar charts: Interesting

Python: Processing lists in reverse order

Python: Random class instances

Bar charts: Interesting (example and setup for later discussion)

Data flow case study (updated 2020-04-15)

Python: Diagrams with GraphViz (Python install and usage)

Graphviz: dot (non-Python install)

GraphViz: expression trees (non-Python usage)

The pitcher-batter problem (decision analysis against adversary, setup for decision trees)

Zip file compression

Matplotlib: 3D plots

Expected value: biased coin flips

Simple decision analysis

Making decisions using information entropy

Note the quiz for today that covers some fundamental programming terms as might be covered in CS 101 but that is also applicable to Python and any programming language.

Python: OpenXML pptx files

Python: Image animation using PIL

Distribution simulation

Python and NLTK: Install

Python: NLTK and bi-chars

As we approach the end of the semester, you should finish and submit any work not submitted (except for programs where the solution was published - A1, A2, A4). The later the submission, the more the late deduction, but some points are better than no points.

Conditional probability

Python: CSV files

Python: OpenXML xlsx files

Python: Canvas drawing and rsCanvas module

Python: decision trees

Location text data conversion

Clustering example using cities

Python and JSON issues

Python: Turtle graphics and rsTurtle module

Pandas