Mathematics & statistics · Computer science
The Crystal Ball Instruction Manual, Volume Two: Foundations for Data Science
Stephen Davies, Evan Coleman
University of Mary Washington · Version 1.5 (2026) · English · Published 2026
A continuation of the Crystal Ball series covering exploratory data analysis, Python, databases, probability, machine learning and graph-based data.
- Learning stage
- College introduction
- Prerequisites
- Volume One topics, basic Python and introductory statistics.
- PDF pages
- 330 in this file
- Editorial review
- 2026-10-29
Contents
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Contents
Intermission and review
Navigating the Spyder's web
EDA: review and extensions
KDEs and distributions
Random value generation
Synthetic data sets
JSON (1 of 2)
JSON (2 of 2)
LOWESS
Data fusion
Long, wide, and ``tidy'' data
Dates and times
Using logarithms
Accessing databases
Screen scraping (1 of 2)
Screen scraping (2 of 2)
Probabilistic reasoning
Causality
Naïve Bayes (1 of 3)
Naïve Bayes (2 of 3)
Naïve Bayes (3 of 3)
APIs
kNN (1 of 2)
kNN (2 of 2)
Two key ML principles
Feature selection
Association Analysis
``Special'' data types
Sources and permissions
The author and publisher remain the source of this textbook. Permission information applies to the specific files in this edition.
Author, publisher, and discovery sources
Authors: Stephen Davies, Evan Coleman
Publisher: University of Mary Washington
- Discovered through Open Textbook Library: Source record · Checked 2026-10-29
- Discovered through Open Textbook Library: Source record · Checked 2026-10-25
CC BY-SA 4.0
Stephen Davies; Evan Coleman. The Crystal Ball Instruction Manual, Volume Two: Foundations for Data Science. University of Mary Washington. Version 1.5 (2026). CC BY-SA 4.0. Cover art: Elizabeth M. Davies, copyright 2020.
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Checked 2026-10-29