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Data science: Python
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1. 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
  • Python version
  • JSON
  • Python JSON input
  • Area of a trapezoid
  • Area using trapezoidal method
  • Pipes and redirection
  • The Python command line
  • Loops
  • Python integers
  • Python list processing
  • Python: Literals
  • Python: Variables
  • Python: Expressions
  • Python: Statements
  • 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
  • Customer matching
  • Euclidean distances
  • Map distances
  • K-Means clustering
  • Python: Clustering
  • Python: Zero
  • Entropy function
  • Lorem Ipsum text
  • 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

  • 2. End of page

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