The Crystal Ball - Instruction Manual I: Introduction to Data Science (Davies)
- Page ID
- 39253
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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)