The Crystal Ball - Instruction Manual I: Introduction to Data Science (Davies)
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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.
Front Matter
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: Associative Arrays in Python (1 of 3)
12: Associative Arrays in Python (2 of 3)
13: Associative Arrays in Python (3 of 3)
14: Loops
15: Exploratory Data Analysis- univariate
16: Tables in Python (1 of 3)
17: Tables in Python (2 of 3)
18: Tables in Python (3 of 3)
19: Exploratory Data Analysis: bivariate (1 of 2)
20: Exploratory Data Analysis: 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
Back Matter
Thumbnail: Visualized data (Unsplash License; Uriel SC via Unsplash)