There will be a one day crash course on all things “big data” at the upcoming San Francisco Predictive Analytics World conference on Monday, March 30th, 2015. Get the Big Data big picture with a day of introduction to the major concepts, methods, challenges, and best practices related to leveraging large volumes of information.
There will be a session on social media network analysis using NodeXL at the conference as well.
Networks are collections of connections — they are everywhere once you start to look. Learn how to collect, analyze, visualize, and publish insights into connected populations. Using the free and open NodeXL addin for Excel, anyone who can make a pie chart can now make a network chart. Create insights into social media, collaboration, organizations, markets, and other connected structures with just a few clicks. Easily publish reports with visualizations and content analysis. Apply social network analysis to your own brands, email, discussions or web sites.
The talk will focus on the easy to follow steps needed to create social media network maps and reports automatically from services like Twitter, Facebook, YouTube, Flickr, email, blogs, wikis, and the WWW. Here is a sample network map of the term #bigdataprivacy:
The graph represents a network of 248 Twitter users whose recent tweets contained “#bigdataprivacy”, or who were replied to or mentioned in those tweets. The tweets in the network were tweeted over the 6-day, 10-hour, 29-minute period from Tuesday, 25 February 2014 at 14:36 UTC to Tuesday, 04 March 2014 at 01:06 UTC. There is an edge for each “replies-to” relationship in a tweet. There is an edge for each “mentions” relationship in a tweet. There is a self-loop edge for each tweet that is not a “replies-to” or “mentions”.
The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
The edge colors are based on edge weight values. The edge widths are based on edge weight values. The edge opacities are based on edge weight values. The vertex sizes are based on followers values. The vertex opacities are based on followers values.
Top 10 Vertices, Ranked by Betweenness Centrality:
@whitehouseostp, @mit, @mit_csail, @steve_lockstep, @aureliepols, @dbarthjones, @digiphile, @stannenb, @djweitzner, @mikaelf
Networks are everywhere but collecting, analyzing, visualizing, and gaining insights into connected structures can require advanced technical skills. This session presents a free, easy-to-use tool for network analysis that builds on the familiar Excel spreadsheet called NodeXL. If you can make a pie chart, you can get insights into networks. The tool makes it easy to collect data from a range of social media (Twitter, Facebook, YouTube, etc.). Quickly create visualizations and reports on the shape of connected groups. Identify the key people, groups and topics in a community. Network analysis can reveal the hidden structures in streams of interactions.
Date and Time: Sunday, March 16, 2014, Full day: 9:00Am – 4:30pm
Intended Audience: Managers, decision makers, practitioners, and professionals interested in a broad overview and introduction
Knowledge Level: All levels
Attendees will receive an electronic copy of the course notes and materials.
“Big Data” is everywhere. The topic is impacting every industry and institution. Big excitement about big data comes from the intersection of dramatic increases in computing power and data storage with growing streams of data coming from almost every person and process on Earth. The pressing question is, how do we best make value of all this data – what should we do with it?
Working with big data effectively depends on understanding the sources of data and the issues in storing and analyzing it:
Where does big data come from?
How do you manage, store, and compute on big data?
What qualifies as “big”?
This one day workshop reviews major big data success stories that have transformed businesses and created new markets.
Dr. Smith will cover these revealing stories in order to illustrate the key concepts, tools, and value-proven applications driving the big data revolution.
“Big data” is a open buzzword – it could be defined as any amount of data you can’t afford to handle – but the big, newfound value achieved by computing at scale is no fad.
What you will learn:
Where does big data come from: Common sources of big data.
What makes data big: Velocity, Variety, and Volume!
How can we leverage it: Open tools and platforms for storing and analyzing big data.
The new paradigm: Today’s shift from hypothesis testing to a broad exploration for correlations is a revolutionary change in the way data is explored.
Best practices for analyzing big data: Key methods in data science, predictive analytics, and text analytics to analytically learn from data.
Social Data: Finding key connections in webs of people and events.
Applications of big data insights to business.
Future directions in big data: bigger, bolder, and better.
Workshop starts at 9:00am
First AM Break from 10:00 – 10:15am
Second AM Break from 11:15 – 11:30am Lunch from 12:30 – 1:15pm
First PM Break: 2:00 – 2:15pm
Second PM Break: 3:15 – 3:30pm Workshops ends at 4:30pm
Mapping Twitter Topic Networks:
From Polarized Crowds to Community Clusters
The paper documents the distinct patterns of connection that emerge when people talk to one another using social media services like Twitter. The paper includes six network visualizations that clearly demonstrate the diverse ways people connect to people when using online tools.