Book Image

Python Data Science Essentials - Second Edition

By : Luca Massaron, Alberto Boschetti
Book Image

Python Data Science Essentials - Second Edition

By: Luca Massaron, Alberto Boschetti

Overview of this book

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.

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Python Data Science Essentials - Second Edition
Luca Massaron | Alberto Boschetti
Oct, 2016 | 378 pages
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Table of Contents (13 chapters)
Python Data Science Essentials - Second Edition
Python Data Science Essentials - Second Edition
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
First Steps
First Steps
Introducing data science and Python
Installing Python
Introducing Jupyter
Datasets and code used in the book
Summary
2
Data Munging
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
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
Approaching deep learning
A peek at Natural Language Processing (NLP)
An overview of unsupervised learning
Summary
5
Social Network Analysis
Social Network Analysis
Introduction to graph theory
Graph algorithms
Graph loading, dumping, and sampling
Summary
6
Visualization, Insights, and Results
Visualization, Insights, and Results
Introducing the basics of matplotlib
Wrapping up matplotlib's commands
Interactive visualizations with Bokeh
Advanced data-learning representations
Summary
Strengthen Your Python Foundations
Strengthen Your Python Foundations
Your learning list
Learn by watching, reading, and doing

Summary

In this chapter, we learned what a social network is, discussing its creation and modification, representation, and some of the important measures of the social network and its nodes. Finally, we discussed the loading and saving of large graphs and ways to deal with the same.

With this chapter, all the essential data science algorithms have been presented; machine learning techniques were discussed in Chapter 4, Machine Learning, and social network analysis methods in the current one.

In the next chapter, which is the concluding one, we are going to introduce you to the basics of visualization with Matplotlib, how to operate EDA with pandas and achieve beautiful visualizations with Seaborn and Bokeh, and how to set up a web server to provide information on demand.

Previous Section
End of Chapter 5
Next Chapter