STAT606: Computing for Data Science and Statistics, Spring 2024

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: MW 9:30AM-10:45AM in 120 Ingraham Hall
Office Hours: TBA 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 23-26
  • Course introduction and Administrivia
  • Installing and running Python and Jupyter
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  • Jupyter notebook documentation (required)
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  • HW00
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    Week 1: Jan 29 - Feb 2
  • Basic Python: types, variables and functions
  • Basic Python: conditionals and iteration
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  • 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
  • HW01
  • Playlist: types, variables and functions; Slides; Demo code
  • Playlist: conditionals and iteration; Slides; Demo code
    		•
    Week 2: Feb 5-9
  • Sequence data: strings, lists and tuples
  • List comprehensions
  • Python dictionaries and hashing
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  • 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)
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  • HW02
  • Playlist: strings, lists and sequence data; Slides; Demo code
  • Playlist: dictionaries and tuples; Slides; Demo code
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    Week 3: Feb 12-16
  • Files and I/O
  • Python on the Command Line
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  • 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)
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  • HW03
  • Playlist: Files and I/O; Slides; Demo code
  • Playlist: Python on the Command Line; Slides; Demo code
    • Week 4: Feb 19-23
  • Basics of object-oriented programming
  • Classes and instances
  • Methods and attributes
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  • 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)
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  • HW04
  • Playlist: Objects and Classes; Slides; Demo code
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    Week 5: Feb 26-Mar 1
  • Basic concepts in functional programming
  • Map, reduce and filter
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  • 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)
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  • HW05
  • Playlist: Functional Programming; Slides; Demo code
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    Week 6: Mar 4-8
  • 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)
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  • HW06
  • Playlist: numpy and scipy; Slides; Demo code
  • Playlist: matplotlib; Slides; Demo code
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    Mar 11-15
  • Python pandas
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  • 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)
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  • HW07
  • Playlist: pandas; Slides; Demo code; CSV file for demo code
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    Week 7: Mar 18-22
  • Markup languages: HTML, XML and JSON
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  • Severance Chapter 12 (HTTP, HTML) and Chapter 13 (XML, JSON) (required)
  • BeautifulSoup documentation (Quick Start up to "CSS sleectors...") (required)
  • BeautifulSoup4 tutorial (recommended)
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  • HW08
  • Playlist: markup languages; Slides; Demo code
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    Week 8: Mar 25-Mar 29 Spring Break. No lecture.
    Week 9: Apr 1-5
  • Databases and SQL
  • Retrieving data with APIs
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  • Oracle relational databases overview (and only the overview!) (required)
  • First section of Python sqlite3 documentation (required)
  • w3schools SQL tutorial (recommended)
  • Wikipedia: Web APIs (recommended)
  • Overview of HTTP Request Methods (recommended)
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  • HW09
  • Playlist: Databases and SQL; Slides; Demo code
  • Playlist: Web APIs; Slides; Demo code
    • Week 10: Apr 8-12
  • Introduction to Hadoop and MapReduce
  • MapReduce using mrjob
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  • J. Dean and S. Ghemawat MapReduce: Simplified Data Processing on Large Clusters in Proceedings of the Sixth Symposium on Operating System Design and Implementation, 2004 (required)
  • HDFS Architecture Guide (recommended)
  • mrjob Fundamentals and Concepts (required)
  • Hadoop wiki: How MapReduce operations are actually carried out (required)
  • HW10
  • Playlist: MapReduce; Slides
  • Playlist: mrjob; Slides; Demo code
    		•
    Week 11: Apr 15-19 MapReduce using PySpark
  • PySpark DataFrame quickstart (required)
  • PySpark SparkConnect quickstart (required)
  • RDD programming guide (required)
  • Spark MLlib, a Spark machine learning library (recommended)
  • Spark GraphX, a Spark library for processing graph data (recommended)
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  • HW11
  • Playlist: PySpark; Slides; Demo code
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    Week 12: Apr 22-26 Google TensorFlow and Keras
  • Guide: TensorFlow Basics (required)
  • TensorFlow Estimators API (recommended)
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  • HW12
  • Playlist: TensorFlow; Slides; Demo code
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    Week 13: Apr 29-May 3 Google TensorFlow and Keras, cont'd
  • Modules, layers and models in TensorFlow (required)
  • Training loops in TensorFlow (required)
  • Specifying models with Keras (recommended)
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  • Playlist: Building and Training Models in TensorFlow; Slides; Demo code
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