Python for Data Science for Dummies, 2nd Edition
- 7h 11m
- John Paul Mueller, Luca Massaron
- John Wiley & Sons (US)
- 2019
The fast and easy way to learn Python programming and statistics
Python is a general-purpose programming language created in the late 1980s—and named after Monty Python—that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library.
Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud.
- Get started with data science and Python
- Visualize information
- Wrangle data
- Learn from data
The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction.
About the Authors
John Paul Mueller is a tech editor and the author of over 100 books on topics from networking and home security to database management and heads-down programming.
Luca Massaron is a data scientist who specializes in organizing and interpreting big data and transforming it into smart data. He is a Google Developer Expert (GDE) in machine learning.
In this Book
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Introduction
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Discovering the Match between Data Science and Python
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Introducing Python's Capabilities and Wonders
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Setting up Python for Data Science
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Working with Google Colab
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Understanding the Tools
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Working with Real Data
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Conditioning Your Data
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Shaping Data
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Putting What You Know in Action
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Getting a Crash Course in MatPlotLib
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Visualizing the Data
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Stretching Python's Capabilities
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Exploring Data Analysis
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Reducing Dimensionality
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Clustering
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Detecting Outliers in Data
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Exploring Four Simple and Effective Algorithms
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Performing Cross-Validation, Selection, and Optimization
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Increasing Complexity with Linear and Nonlinear Tricks
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Understanding the Power of the Many
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Ten Essential Data Resources
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Ten Data Challenges You Should Take
