STAT606: Computing for Data Science and Statistics, Spring 2025

This course provides a survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Our focus will be on complementing the tools that students are already familiar with from their previous courses on R. The course will begin with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. We will then cover some of the scientific computing platforms available in Python, including numpy, scipy and scikit-learn, as well as visualization using matplotlib. We will then turn to discussing collecting data from the web both by scraping and using APIs. The course will conclude with a brief survey of distributed computing, focusing on Hadoop and Google Cloud Platform.

  Instructor: Keith Levin, kdlevin | at | wisc | dot | edu
Lectures: TuTh 11:00AM-12:15PM in 1175 Grainger Hall
Office Hours: W 1:30-2:30 in MSC 6170, or by appointment
Textbook: There is no required textbook. See below for weekly readings.
Syllabus: Available here.
Prerequisites: there are no formal prerequisites for this course. Previous experience with programming in R, the UNIX/Linux command line, text editing in vim/emacs, regular expressions and distributed computing (equivalent to STAT605) is assumed.

Date Topics Readings Notes and Resources
Week 0: Jan 21-24
  • Course introduction and Administrivia
  • Installing and running Python and Jupyter
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  • Jupyter notebook documentation (required)
    		•  HW00
    Week 1: Jan 27 - Jan 31
  • Basic Python: types, variables and functions
  • Basic Python: conditionals and iteration
    		•
  • Either A. B. Downey, Chapters 1 through 3 or Severance, Chapters 1, 2 and 4 (required)
  • Either A. B. Downey, Chapter 5 or Severance, Chapters 3 and 5
  • Types, variables and functions: Slides; Demo code
  • Conditionals and iteration: Slides; Demo code
    		•
    Week 2: Feb 3-7
  • Sequence data: strings, lists and tuples
  • List comprehensions
  • Python dictionaries and hashing
    		•
  • Either A. B. Downey, Chapters 8 and 10 or Severance, Chapters 6 and 8 (required); A. B. Downey, Chapter 9 (recommended)
  • Python documentation on lists (recommended); Python documentation on sequences (recommended)
  • Either A. B. Downey, Chapters 11 and 12 or Severance, Chapters 9 and 10 (required)
  • Python documentation on dictionaries (recommended)
  • Python documentation on tuples (recommended)
  • Python documentation on sets (recommended)
  • A. B. Downey, Section B.4 (recommended); A. B. Downey, Chapter 13 (recommended)
    		•
  • Playlist: strings, lists and sequence data; Slides; Demo code
  • Playlist: dictionaries and tuples; Slides; Demo code
    • Week 3: Feb 10-14
  • Files and I/O
  • Python on the Command Line
    		•
  • A. B. Downey, Chapter 14 or Severance, Chapter 7 (required)
  • Python File I/O Documentation (required)
  • Handling Errors and Exceptions (required)
  • Python pickle module (recommended)
  • Overview of the Python interpreter (recommended)
  • Calling Python from the command line (recommended)
  • Python sys module (recommended)
    		•
  • Playlist: Files and I/O; Slides; Demo code
  • Playlist: Python on the Command Line; Slides; Demo code
    • Week 4: Feb 17-21
  • Basics of object-oriented programming
  • Classes and instances
  • Methods and attributes
    		•
  • A. B. Downey, Chapters 15 and 16 or Severance Chapter 14 (required)
  • Python documentation on classes (only through section 9.3) (required)
  • D. Phillips (2015). Python 3 Object-oriented Programming, Second Edition. Packt Publishing. (recommended)
  • M. Weisfeld (2009). The Object-Oriented Thought Process, Third Edition. Addison-Wesley. (recommended)
    		•
  • Playlist: Objects and Classes; Slides; Demo code
    		•
    Week 5: Feb 24-28
  • Basic concepts in functional programming
  • Map, reduce and filter
    		•
  • Python itertools documentation (required)
  • Python functools documentation (required)
  • A. M. Kuchling. Functional Programming HOWTO (required)
  • M. R. Cook. A Practical Introduction to Functional Programming (recommended)
  • D. Mertz Functional Programming in Python (recommended)
    		•
  • Playlist: Functional Programming; Slides; Demo code
    		•
    Week 6: Mar 3-7
  • numpy, scipy and matplotlib
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  • Numpy quickstart tutorial (required)
  • SciPy tutorial (recommended)
  • Pyplot tutorial (required)
  • Pyplot API (recommended)
  • E. Tufte (2001). The Visual Display of Quantitative Information. Graphics Press. (recommended)
  • E. Tufte (1997). Visual and Statistical Thinking: Displays of Evidence for Making Decisions. Graphics Press. (recommended)
    		•
  • Playlist: numpy and scipy; Slides; Demo code
  • Playlist: matplotlib; Slides; Demo code
    		•
    Week 7: Mar 10-14
  • Python pandas
    		•
  • pandas quickstart guide (required)
  • Basic data structures (required)
  • Basic functionality of pandas Series and DataFrames (required)
  • pandas group-by operations (required)
  • Reshaping and pivoting (required)
  • pandas cookbook (recommended)
  • Merge, join and concatenation (recommended)
  • Time series functionality (recommended)
    		•
  • Playlist: pandas; Slides; Demo code; CSV file for demo code
    		•
    Week 8: Mar 17-21
  • Markup languages: HTML, XML and JSON
    		•
  • Severance Chapter 12 (HTTP, HTML) and Chapter 13 (XML, JSON) (required)
  • BeautifulSoup documentation (Quick Start up to "CSS sleectors...") (required)
  • BeautifulSoup4 tutorial (recommended)
    		•
  • Playlist: markup languages; Slides; Demo code
    		•
    Mar 24-Mar 28 Spring Break. No lecture.
    Week 9: Mar 31-Apr 4
  • Databases and SQL
  • Retrieving data with APIs
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    Week 10: Apr 7-11
  • Introduction to Hadoop and MapReduce
  • MapReduce using mrjob
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    Week 11: Apr 14-18
  • MapReduce using PySpark
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    Week 12: Apr 21-25
  • Google TensorFlow and Keras
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    Week 13: Apr 28-May 2
  • Google TensorFlow and Keras, cont'd
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