Python for Data Science For Dummies
- 6h 23m
- John Paul Mueller, Luca Massaron
- John Wiley & Sons (US)
- 2015
Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide.
- Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models
- Explains objects, functions, modules, and libraries and their role in data analysis
- Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib
Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.
About the Authors
John Paul Mueller, consultant, application developer, writer, and technical editor, has written over 600 articles and 97 books. His topics range from programming to home security.
Luca Massaron is a data scientist and a research director specializing in multivariate statistical analysis, machine learning, and customer insight. He is a pioneer of Web audience analysis in Italy and was named one of the top ten data scientists at competitions by kaggle.com.
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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Reviewing Basic Python
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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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Understanding the Tools
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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 Science Resource Collections
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Ten Data Challenges You Should Take
