Visualizing and Analyzing Networks with Python or R (and Javascript)

Abstract: 

Social networks play a powerful role in shaping our lives, affecting our power and influence, how we acquire and think about basic facts, as well as our behaviors. These effects exist in our professional environments as much as they do in our personal lives. Given the importance of these social structures on our personal and professional lives, it’s incredibly valuable to be able to visualize and analyze them because this knowledge enables you to drive positive change.

By completing this tutorial, you will develop an understanding of some basic properties of social networks, including how to calculate network statistics, how to visualize networks, and how to incorporate network characteristics into your statistical models. You will become familiar with how to compute network statistics and perform statistical modeling in Python or R, as well as how to create interactive visualizations of networks in Javascript.

Session Outline
Lesson 1: Network Elements and Characteristics
Familiarize yourself with network elements and characteristics and the Python or R syntax necessary to compute these statistics. At the end of this lesson, you will be able to describe different network types and generate some key network statistics in Python or R.

Lesson 2: Visualizing Networks
Familiarize yourself with common network visualizations and how to generate interactive versions of them in Javascript. At the end of this lesson, you will be able to describe the strengths and weaknesses of different network visualizations and generate interactive network visualizations in Javascript.

Lesson 3: Modeling Network Characteristics and Outcomes
Let’s answer some intriguing business questions by using our network data! We’ll put our modeling skills to the test to analyze the relationship between our network characteristics and outcomes of interest. At the end of this lesson, you will be able to generate statistical models that incorporate network data to predict outcomes.

Background Knowledge
Prior familiarity with the following will be helpful: Jupyter notebooks, Observable notebooks, Python, NetworkX, PyMC3, R, igraph, rstanarm or brms, Javascript, D3

Bio: 

Clinton Brownley, Ph.D., is a data scientist at Facebook, where he’s responsible for a variety of analytics projects designed to empower employees to do their best work.

Prior to this role, he was a data scientist at WhatsApp, working to improve messaging and VoIP calling performance and reliability. Before WhatsApp, he worked on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions at Facebook.

As an avid student and teacher of modern analytics techniques, Clinton is the author of two books, "Foundations for Analytics with Python" and "Multi-objective Decision Analysis," and also teaches Python programming and interactive data visualization courses at Facebook and in the Bay Area.

Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Open Data Science

 

 

 

Open Data Science
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Cambridge, MA 02142
info@odsc.com

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