Book Image

Python Data Science Essentials - Third Edition

By : Alberto Boschetti, Luca Massaron

Book Image

Python Data Science Essentials - Third Edition

By: Alberto Boschetti, Luca Massaron

Overview of this book

Fully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn. The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost. By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

First Steps

Introducing data science and Python

Installing Python

Introducing Jupyter

Datasets and code used in this book

Data Munging

The data science process

Data loading and preprocessing with pandas

Working with categorical and textual data

Data processing with NumPy

Creating NumPy arrays

NumPy fast operation and computations

The Data Pipeline

The Data Pipeline

Introducing EDA

Building new features

Dimensionality reduction

The detection and treatment of outliers

Validation metrics

Testing and validating

Cross-validation

Hyperparameter optimization

Feature selection

Wrapping everything in a pipeline

Machine Learning

Machine Learning

Preparing tools and datasets

Linear and logistic regression

K-Nearest Neighbors

Nonlinear algorithms

Ensemble strategies

Dealing with big data

A peek into natural language processing (NLP)

An overview of unsupervised learning

Visualization, Insights, and Results

Visualization, Insights, and Results

Introducing the basics of matplotlib

Wrapping up matplotlib's commands

Advanced data learning representation

Social Network Analysis

Social Network Analysis

Introduction to graph theory

Graph algorithms

Graph loading, dumping, and sampling

Deep Learning Beyond the Basics

Deep Learning Beyond the Basics

Approaching deep learning

Classifying images with CNN

Using pre-trained models

Working with temporal sequences

Spark for Big Data

Spark for Big Data

From a standalone machine to a bunch of nodes

Starting with PySpark

Sharing variables across cluster nodes

Data preprocessing in Spark

Machine learning with Spark

Strengthen Your Python Foundations

Strengthen Your Python Foundations

Your learning list

Learn by watching, reading, and doing

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Introduction to graph theory

Basically, a graph is a data structure that's able to represent relations in a collection of objects. Under this paradigm, the objects are the graph's nodes and the relations are the graph's links (or edges). The graph is directed if the links have an orientation (conceptually, they're like the one-way streets of a city); otherwise, the graph is undirected. In the following table, examples of well-known graphs are provided:

Graph example	Type	Nodes	Edges
World Wide Web	Directed	Web pages	Links
Facebook	Undirected	People	Friendship
Twitter	Directed	People	Follower
IP network	Undirected	Hosts	Wires/Connections
Navigation systems	Directed	Places/Addresses	Streets
Wikipedia	Directed	Pages	Anchor links
Scientific literature	Directed	Papers	Citations
Markov...