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Python Data Science Essentials

Python Data Science Essentials

By : Alberto Boschetti
4.2 (6)
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Python Data Science Essentials

Python Data Science Essentials

4.2 (6)
By: Alberto Boschetti
Buy this Book

Overview of this book

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Table of Contents (8 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. First Steps
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1. First Steps
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Introducing data science and Python
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Installing Python
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Scientific distributions
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Introducing IPython
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Summary
2
2. Data Munging
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2. Data Munging
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The data science process
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Data loading and preprocessing with pandas
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Working with categorical and textual data
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Data processing with NumPy
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Creating NumPy arrays
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NumPy fast operation and computations
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Summary
3
3. The Data Science Pipeline
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3. The Data Science Pipeline
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Introducing EDA
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Feature creation
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Dimensionality reduction
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The detection and treatment of outliers
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Scoring functions
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Testing and validating
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Cross-validation
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Hyper-parameters' optimization
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Feature selection
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Summary
4
4. Machine Learning
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4. Machine Learning
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Linear and logistic regression
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Naive Bayes
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The k-Nearest Neighbors
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Advanced nonlinear algorithms
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Ensemble strategies
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A peek into Natural Language Processing (NLP)
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An overview of unsupervised learning
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Summary
5
5. Social Network Analysis
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5. Social Network Analysis
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Introduction to graph theory
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Graph algorithms
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Graph loading, dumping, and sampling
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Summary
6
6. Visualization
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6. Visualization
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Introducing the basics of matplotlib
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Selected graphical examples with pandas
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Advanced data learning representation
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Summary
7
Index
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Index

Testing and validating

After loading our data, preprocessing it, creating new useful features, checking for outliers and other inconsistent data points, and choosing the right metric, we are finally ready to apply some machine learning algorithm that, by observing a series of examples and pairing them with their outcome, is able to extract a series of rules that can be successfully generalized to new examples by correctly guessing their resulting outcome. This is the supervised learning approach where a series of specialized algorithms that are fundamental to data science is used. How can we correctly apply the learning process in order to achieve the best generalizable model for prediction?

There are some best practices to be followed. Let's proceed step by step, by first loading the dataset that we will be working on in the following example:

In: from sklearn.datasets import load_digits
digits = load_digits()
print digits.DESCR
X = digits.data
y = digits.target

The digit dataset contains...

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Tech Concepts
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Programming languages
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Python Data Science Essentials
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