Python Data Science Essentials by Boschetti, Alberto -- Read -- Imperial Library of Trantor

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Python Data Science Essentials

Table of Contents Python Data Science Essentials Credits About the Authors About the Reviewers www.PacktPub.com

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Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Errata Piracy Questions

1. First Steps

Introducing data science and Python Installing Python

Python 2 or Python 3? Step-by-step installation A glance at the essential Python packages

NumPy SciPy pandas Scikit-learn IPython Matplotlib Statsmodels Beautiful Soup NetworkX NLTK Gensim PyPy

The installation of packages Package upgrades

Scientific distributions

Anaconda Enthought Canopy PythonXY WinPython

Introducing IPython

The IPython Notebook Datasets and code used in the book

Scikit-learn toy datasets The MLdata.org public repository LIBSVM data examples Loading data directly from CSV or text files Scikit-learn sample generators

Summary

2. Data Munging

The data science process Data loading and preprocessing with pandas

Fast and easy data loading Dealing with problematic data Dealing with big datasets Accessing other data formats Data preprocessing Data selection

Working with categorical and textual data

A special type of data – text

Data processing with NumPy

NumPy's n-dimensional array The basics of NumPy ndarray objects

Creating NumPy arrays

From lists to unidimensional arrays Controlling the memory size Heterogeneous lists From lists to multidimensional arrays Resizing arrays Arrays derived from NumPy functions Getting an array directly from a file Extracting data from pandas

NumPy fast operation and computations

Matrix operations Slicing and indexing with NumPy arrays Stacking NumPy arrays

Summary

3. The Data Science Pipeline

Introducing EDA Feature creation Dimensionality reduction

The covariance matrix Principal Component Analysis (PCA) A variation of PCA for big data – RandomizedPCA Latent Factor Analysis (LFA) Linear Discriminant Analysis (LDA) Latent Semantical Analysis (LSA) Independent Component Analysis (ICA) Kernel PCA Restricted Boltzmann Machine (RBM)

The detection and treatment of outliers

Univariate outlier detection EllipticEnvelope OneClassSVM

Scoring functions

Multilabel classification Binary classification Regression

Testing and validating Cross-validation

Using cross-validation iterators Sampling and bootstrapping

Hyper-parameters' optimization

Building custom scoring functions Reducing the grid search runtime

Feature selection

Univariate selection Recursive elimination Stability and L1-based selection

Summary

4. Machine Learning

Linear and logistic regression Naive Bayes The k-Nearest Neighbors Advanced nonlinear algorithms

SVM for classification SVM for regression Tuning SVM

Ensemble strategies

Pasting by random samples Bagging with weak ensembles Random Subspaces and Random Patches Sequences of models – AdaBoost Gradient tree boosting (GTB) Dealing with big data

Creating some big datasets as examples Scalability with volume Keeping up with velocity Dealing with variety A quick overview of Stochastic Gradient Descent (SGD)

A peek into Natural Language Processing (NLP)

Word tokenization Stemming Word Tagging Named Entity Recognition (NER) Stopwords A complete data science example – text classification

An overview of unsupervised learning Summary

5. Social Network Analysis

Introduction to graph theory Graph algorithms Graph loading, dumping, and sampling Summary

6. Visualization

Introducing the basics of matplotlib

Curve plotting Using panels Scatterplots Histograms Bar graphs Image visualization

Selected graphical examples with pandas

Boxplots and histograms Scatterplots Parallel coordinates

Advanced data learning representation

Learning curves Validation curves Feature importance GBT partial dependence plot

Summary

Index

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