Book Image

Python Data Science Essentials

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials

By: Alberto Boschetti, Luca Massaron

Overview of this book

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

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Table of Contents (13 chapters)
Python Data Science Essentials
Python Data Science Essentials
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
First Steps
First Steps
Introducing data science and Python
Installing Python
Scientific distributions
Introducing IPython
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 Science Pipeline
The Data Science Pipeline
Introducing EDA
Feature creation
Dimensionality reduction
The detection and treatment of outliers
Scoring functions
Testing and validating
Cross-validation
Hyper-parameters' optimization
Feature selection
Summary
4
Machine Learning
Machine Learning
Linear and logistic regression
Naive Bayes
The k-Nearest Neighbors
Advanced nonlinear algorithms
Ensemble strategies
A peek into 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
Visualization
Introducing the basics of matplotlib
Selected graphical examples with pandas
Advanced data learning representation
Summary
Index
Index

Summary

In this chapter, we learned what a social network is—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 the previous chapter 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.

Previous Section
End of Chapter 5
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