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

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Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
First Steps
First Steps
Introducing data science and Python
Installing Python
Introducing Jupyter
Datasets and code used in this book
Summary
2
Data Munging
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
3
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
Summary
4
Machine Learning
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
5
Visualization, Insights, and Results
Visualization, Insights, and Results
Introducing the basics of matplotlib
Wrapping up matplotlib's commands
Advanced data learning representation
Summary
6
Social Network Analysis
Social Network Analysis
Introduction to graph theory
Graph algorithms
Graph loading, dumping, and sampling
Summary
7
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
Summary
8
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
Summary
9
Strengthen Your Python Foundations
Strengthen Your Python Foundations
Your learning list
Learn by watching, reading, and doing
10
Other Books You May Enjoy
Other Books You May Enjoy
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Introducing EDA

Exploratory data analysis (EDA), or data exploration, is the first step in the data science process. John Tukey coined this term in 1977 when he first wrote his, book Exploratory Data Analysis, emphasizing the importance of EDA. EDA is required to understand the dataset better, check its features and its shape, validate some first hypothesis that you have in mind, and get a preliminary idea about the next step that you want to pursue in subsequent subsequent data science tasks.

In this section, you will work on the Iris dataset, which was already used in the previous chapter. First, let's load the dataset:

In: import pandas as pd
    iris_filename = 'datasets-uci-iris.csv'
    iris = pd.read_csv(iris_filename, header=None, 
            names= ['sepal_length', 'sepal_width', 
            'petal_length', 'petal_width...
Previous Section
End of Section 2
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