Book Image

Python: Real-World Data Science

By : Fabrizio Romano, Dusty Phillips, Phuong Vo.T.H, Martin Czygan, Robert Layton, Sebastian Raschka
Book Image

Python: Real-World Data Science

By: Fabrizio Romano, Dusty Phillips, Phuong Vo.T.H, Martin Czygan, Robert Layton, Sebastian Raschka

Overview of this book

The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you’ll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it’s time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls.
Table of Contents (12 chapters)
Free Chapter
Table of Contents
Python: Real-World Data Science
Meet Your Course Guide
What's so cool about Data Science?
Course Structure
Course Journey
The Course Roadmap and Timeline

Chapter 7. Discovering Accounts to Follow Using Graph Mining

Lots of things can be represented as graphs. This is particularly true in this day of Big Data, online social networks, and the Internet of Things. In particular, online social networks are big business, with sites such as Facebook that have over 500 million active users (50 percent of them log in each day). These sites often monetize themselves by targeted advertising. However, for users to be engaged with a website, they often need to follow interesting people or pages.

In this chapter, we will look at the concept of similarity and how we can create graphs based on it. We will also see how to split this graph up into meaningful subgraphs using connected components. This simple algorithm introduces the concept of cluster analysis—splitting a dataset into subsets based on similarity. We will investigate cluster analysis in more depth in Chapter 10, Clustering News Articles.

The topics covered in this chapter include...