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Python Data Science Essentials - Third Edition

By : Alberto Boschetti, Luca Massaron, Pietro Marinelli, Matteo Malosetti

5 (2)

Buy this Book

Python Data Science Essentials

5 (2)

By: Alberto Boschetti, Luca Massaron, Pietro Marinelli, Matteo Malosetti

Buy this Book

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

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Free Chapter

First Steps

Introducing data science and Python

Installing Python

Introducing Jupyter

Datasets and code used in this book

Summary

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

Summary

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

Summary

Machine Learning

Preparing tools and datasets

Linear and logistic regression

Naive Bayes

K-Nearest Neighbors

Nonlinear algorithms

Ensemble strategies

Dealing with big data

A peek into natural language processing (NLP)

An overview of unsupervised learning

Summary

Visualization, Insights, and Results

Introducing the basics of matplotlib

Wrapping up matplotlib's commands

Advanced data learning representation

Summary

Social Network Analysis

Introduction to graph theory

Graph algorithms

Graph loading, dumping, and sampling

Summary

Deep Learning Beyond the Basics

Approaching deep learning

Classifying images with CNN

Using pre-trained models

Working with temporal sequences

Summary

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

Summary

Strengthen Your Python Foundations

Your learning list

Learn by watching, reading, and doing

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Summary

This chapter provided an overview of essential data science by providing examples of both basic and advanced graphical representations of data, machine learning processes, and results. We explored the pylab module from matplotlib, which gives the easiest and fastest access to the graphical capabilities of the package. We used pandas for EDA, and tested the graphical utilities provided by scikit-learn. All examples were like building blocks, and they are all easily customizable in order to provide you with a fast template for visualization.

In the next chapter, you'll be introduced to graphs, which are an interesting deviation from the predictors/target flat matrices. They are quite a hot topic in data science now. Expect to delve into very complex and intricate networks.