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The Crystal Ball Instruction Manual Stephen Davies Volume One: Introduction to Data Science

Av: Medverkande: Materialtyp: TextSerie: Open textbook libraryDistributör: Minneapolis, MN Open Textbook LibraryUtgivningsuppgift: [Place of publication not identified] University of Mary Washington [2020]Datum för upphovsrätt: ©2020Utgåva: version 1.1Beskrivning: 1 online resourceInnehållstyp:
  • text
Medietyp:
  • computer
Bärartyp:
  • online resource
ISBN:
  • 9781715320041
Ämnen: Library of Congress (LC) klassifikationskod:
  • QA76
Onlineresurser:
Innehåll:
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
Ämne: 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.
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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

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.

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In English.

Description based on print resource

Freely available e-book