Python Data Science Essentials - Second Edition

Become an efficient data science practitioner by understanding Python's key concepts

Python Data Science Essentials - Second Edition

Learning
Alberto Boschetti, Luca Massaron

Become an efficient data science practitioner by understanding Python's key concepts
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Book Details

ISBN 139781786462138
Paperback378 pages

Book Description

Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow.

Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of 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.

Table of Contents

Chapter 1: First Steps
Introducing data science and Python
Installing Python
Introducing Jupyter
Datasets and code used in the book
Summary
Chapter 2: Data Munging
The data science process
Data loading and preprocessing with pandas
Working with categorical and text data
Data processing with NumPy
Creating NumPy arrays
NumPy's fast operations and computations
Summary
Chapter 3: 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
Chapter 4: Machine Learning
Preparing tools and datasets
Linear and logistic regression
Naive Bayes
K-Nearest Neighbors
Nonlinear algorithms
Ensemble strategies
Dealing with big data
Approaching deep learning
A peek at Natural Language Processing (NLP)
An overview of unsupervised learning
Summary
Chapter 5: Social Network Analysis
Introduction to graph theory
Graph algorithms
Graph loading, dumping, and sampling
Summary
Chapter 6: Visualization, Insights, and Results
Introducing the basics of matplotlib
Wrapping up matplotlib's commands
Interactive visualizations with Bokeh
Advanced data-learning representations
Summary

What You Will Learn

  • Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux
  • Get data ready for your data science project
  • Manipulate, fix, and explore data in order to solve data science problems
  • Set up an experimental pipeline to test your data science hypotheses
  • Choose the most effective and scalable learning algorithm for your data science tasks
  • Optimize your machine learning models to get the best performance
  • Explore and cluster graphs, taking advantage of interconnections and links in your data

Authors

Table of Contents

Chapter 1: First Steps
Introducing data science and Python
Installing Python
Introducing Jupyter
Datasets and code used in the book
Summary
Chapter 2: Data Munging
The data science process
Data loading and preprocessing with pandas
Working with categorical and text data
Data processing with NumPy
Creating NumPy arrays
NumPy's fast operations and computations
Summary
Chapter 3: 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
Chapter 4: Machine Learning
Preparing tools and datasets
Linear and logistic regression
Naive Bayes
K-Nearest Neighbors
Nonlinear algorithms
Ensemble strategies
Dealing with big data
Approaching deep learning
A peek at Natural Language Processing (NLP)
An overview of unsupervised learning
Summary
Chapter 5: Social Network Analysis
Introduction to graph theory
Graph algorithms
Graph loading, dumping, and sampling
Summary
Chapter 6: Visualization, Insights, and Results
Introducing the basics of matplotlib
Wrapping up matplotlib's commands
Interactive visualizations with Bokeh
Advanced data-learning representations
Summary

Book Details

ISBN 139781786462138
Paperback378 pages
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