The Crystal Ball Instruction Manual Volume One: Introduction to Data Science
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 intr...
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| Main Author: | |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
[Place of publication not identified]
University of Mary Washington
[2020]
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| Edition: | version 1.1 |
| Series: | Open textbook library.
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| Subjects: | |
| Online Access: | Access online version |
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| 245 | 0 | 4 | |a The Crystal Ball Instruction Manual |c Stephen Davies |n Volume One: Introduction to Data Science |
| 250 | |a version 1.1 | ||
| 264 | 2 | |a Minneapolis, MN |b Open Textbook Library | |
| 264 | 1 | |a [Place of publication not identified] |b University of Mary Washington |c [2020] | |
| 264 | 4 | |c ©2020. | |
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| 505 | 0 | |a 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 | |
| 520 | 0 | |a 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. | |
| 542 | 1 | |f Attribution-ShareAlike | |
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| 588 | 0 | |a Description based on print resource | |
| 650 | 0 | |a Computer Science |v Textbooks | |
| 650 | 0 | |a Artificial Intelligence |v Textbooks | |
| 650 | 0 | |a Information technology |v Textbooks | |
| 650 | 0 | |a Databases |v Textbooks | |
| 650 | 0 | |a Programming Languages |v Textbooks | |
| 700 | 1 | |a Davies, Stephen |e author | |
| 710 | 2 | |a Open Textbook Library |e distributor | |
| 856 | 4 | 0 | |u https://open.umn.edu/opentextbooks/textbooks/915 |z Access online version |