Python data science essentials : a practitioner's guide covering essential data science principles, tools, and techniques / Alberto Boschetti, Luca Massaron.

Format:

Book

Author/Creator:

Boschetti, Alberto, author.

Massaron, Luca, author.

Language:

English

Subjects (All):

Python (Computer program language).

Database management.

Information visualization.

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

Third edition.

Place of Publication:

Birmingham, UK : Packt Publishing, 2018.

System Details:

text file

Summary:

Gain useful insights from your data using popular data science tools Key Features A one-stop guide to Python libraries such as pandas and NumPy Comprehensive coverage of data science operations such as data cleaning and data manipulation Choose scalable learning algorithms for your data science tasks Book Description 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 What you will learn Set up your data science toolbox on Windows, Mac, and Linux Use the core machine learning methods offered by the scikit-learn library Manipulate, fix, and explore data to solve data science problems Learn advanced explorative and manipulative techniques to solve data operations Optimize your machine learning models for optimized performance Explore and cluster graphs, taking advantage of interconnections and links in your data Who this book is for If you're a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support an...

Contents:

Cover

Title Page

Copyright and Credits

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: First Steps

Introducing data science and Python

Installing Python

Python 2 or Python 3?

Step-by-step installation

Installing the necessary packages

Package upgrades

Scientific distributions

Anaconda

Leveraging conda to install packages

Enthought Canopy

WinPython

Explaining virtual environments

Conda for managing environments

A glance at the essential packages

NumPy

SciPy

pandas

pandas-profiling

Scikit-learn

Jupyter

JupyterLab

Matplotlib

Seaborn

Statsmodels

Beautiful Soup

NetworkX

NLTK

Gensim

PyPy

XGBoost

LightGBM

CatBoost

TensorFlow

Keras

Introducing Jupyter

Fast installation and first test usage

Jupyter magic commands

Installing packages directly from Jupyter Notebooks

Checking the new JupyterLab environment

How Jupyter Notebooks can help data scientists

Alternatives to Jupyter

Datasets and code used in this book

Scikit-learn toy datasets

The MLdata.org and other public repositories for open source data

LIBSVM data examples

Loading data directly from CSV or text files

Scikit-learn sample generators

Summary

Chapter 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

Putting data together

Data preprocessing

Data selection

Working with categorical and textual data

A special type of data - text

Scraping the web with Beautiful Soup

Data processing with NumPy

NumPy's n-dimensional array

The basics of NumPy ndarray objects

Creating NumPy arrays

From lists to unidimensional arrays.

Controlling 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

Working with sparse arrays

Chapter 3: The Data Pipeline

Introducing EDA

Building new features

Dimensionality reduction

The covariance matrix

Principal component analysis

PCA for big data - RandomizedPCA

Latent factor analysis

Linear discriminant analysis

Latent semantical analysis

Independent component analysis

Kernel PCA

T-SNE

Restricted Boltzmann Machine

The detection and treatment of outliers

Univariate outlier detection

EllipticEnvelope

OneClassSVM

Validation metrics

Multilabel classification

Binary classification

Regression

Testing and validating

Cross-validation

Using cross-validation iterators

Sampling and bootstrapping

Hyperparameter optimization

Building custom scoring functions

Reducing the grid search runtime

Feature selection

Selection based on feature variance

Univariate selection

Recursive elimination

Stability and L1-based selection

Wrapping everything in a pipeline

Combining features together and chaining transformations

Building custom transformation functions

Chapter 4: Machine Learning

Preparing tools and datasets

Linear and logistic regression

Naive Bayes

K-Nearest Neighbors

Nonlinear algorithms

SVM for classification

SVM for regression

Tuning SVM

Ensemble strategies

Pasting by random samples

Bagging with weak classifiers

Random Subspaces and Random Patches

Random Forests and Extra-Trees.

Estimating probabilities from an ensemble

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

An 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

K-means

DBSCAN - a density-based clustering technique

Latent Dirichlet Allocation (LDA)

Chapter 5: Visualization, Insights, and Results

Introducing the basics of matplotlib

Trying curve plotting

Using panels for clearer representations

Plotting scatterplots for relationships in data

Histograms

Bar graphs

Image visualization

Selected graphical examples with pandas

Working with boxplots and histograms

Plotting scatterplots

Discovering patterns by parallel coordinates

Wrapping up matplotlib's commands

Introducing Seaborn

Enhancing your EDA capabilities

Advanced data learning representation

Learning curves

Validation curves

Feature importance for RandomForests

Gradient Boosting Trees partial dependence plotting

Creating a prediction server with machine-learning-as-a-service

Chapter 6: Social Network Analysis

Introduction to graph theory

Graph algorithms

Types of node centrality

Partitioning a network

Graph loading, dumping, and sampling

Chapter 7: Deep Learning Beyond the Basics

Approaching deep learning

Classifying images with CNN

Using pre-trained models

Working with temporal sequences

Chapter 8: Spark for Big Data.

From a standalone machine to a bunch of nodes

Making sense of why we need a distributed framework

The Hadoop ecosystem

Hadoop architecture

Hadoop Distributed File System

MapReduce

Introducing Apache Spark

PySpark

Starting with PySpark

Setting up your local Spark instance

Experimenting with Resilient Distributed Datasets

Sharing variables across cluster nodes

Read-only broadcast variables

Write-only accumulator variables

Broadcast and accumulator variables together-an example

Data preprocessing in Spark

CSV files and Spark DataFrames

Dealing with missing data

Grouping and creating tables in-memory

Writing the preprocessed DataFrame or RDD to disk

Working with Spark DataFrames

Machine learning with Spark

Spark on the KDD99 dataset

Reading the dataset

Feature engineering

Training a learner

Evaluating a learner's performance

The power of the machine learning pipeline

Manual tuning

Final cleanup

Appendix: Strengthen Your Python Foundations

Your learning list

Lists

Dictionaries

Defining functions

Classes, objects, and object-oriented programming

Exceptions

Iterators and generators

Conditionals

Comprehensions for lists and dictionaries

Learn by watching, reading, and doing

Massive open online courses (MOOCs)

PyCon and PyData

Interactive Jupyter

Don't be shy, take a real challenge

Other Books You May Enjoy

Index.

Notes:

Description based on online resource; title from title page (viewed November 5, 2018).

ISBN:

1-78953-189-6

OCLC:

1061288904