Read more about The Crystal Ball Instruction Manual - version 1.1 Volume One: Introduction to Data Science

The Crystal Ball Instruction Manual - version 1.1 Volume One: Introduction to Data Science

(2 reviews)

Stephen Davies, University of Mary Washington

Copyright Year: 2020

ISBN 13: 9781715320041

Publisher: University of Mary Washington

Language: English

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Reviewed by Thomas Blamey, Math Faculty, University of Hawaii Maui College on 2/8/22

I found the text to be complete and sufficient for an introduction to data science with python. This was remarkable as there are OER textbook for python, but few on data science "using" python. read more

Reviewed by Patrick Earl, Instructor, Kutztown University on 12/31/20

The book provides a great introduction to the world of data science, using Python as the main driver. Python is a good choice as it has become the de facto programming language used in the field with its many libraries that fit the bill. read more

Table of Contents

  • 1 Introduction 
  • 2 A trip to Jupyter 
  • 3 Three kinds of atomic data 
  • 4 Memory pictures 
  • 5 Calculations 
  • 6 Scales of measure 
  • 7 Three kinds of aggregate data
  • 8 Arrays in Python (1 of 2) 
  • 9 Arrays in Python (2 of 2) 
  • 10 Interpreting Data 
  • 11 Assoc. arrays in Python (1 of 3) 
  • 12 Assoc. arrays in Python (2 of 3) 
  • 13 Assoc. arrays in Python (3 of 3) 
  • 14 Loops 
  • 15 EDA: univariate 
  • 16 Tables in Python (1 of 3) 
  • 17 Tables in Python (2 of 3) 
  • 18 Tables in Python (3 of 3) 
  • 19 EDA: bivariate (1 of 2) 
  • 20 EDA: bivariate (2 of 2) 
  • 21 Branching 
  • 22 Functions (1 of 2) 
  • 23 Functions (2 of 2) 
  • 24 Recoding and transforming 
  • 25 Machine Learning: concepts 
  • 26 Classification: concepts 
  • 27 Decision trees (1 of 2) 
  • 28 Decision trees (2 of 2) 
  • 29 Evaluating a classifier 

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  • About the Book

    A perfect introduction to the exploding field of Data Science for the curious, first-time student. The author brings his trademark conversational tone to the important pillars of the discipline: exploratory data analysis, choices for structuring data, causality, machine learning principles, and introductory Python programming using open-source Jupyter Notebooks. This engaging read will allow any dedicated learner to build the skills necessary to contribute to the Data Science revolution, regardless of background.

    About the Contributors


    Stephen Davies, Associate Professor of Computer Science, earned a Ph.D. (2005) in Computer Science from the University of Colorado, Boulder, after having received an M.S. (1995) in Electrical Engineering from Colorado and a B.S. (1992) in Electrical Engineering from Rice University. He joined the UMW faculty in 2006, and has taught courses in database schema theory, Web application development, computational science, data mining, and object-oriented analysis & design, among other topics.

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