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You know your data is big - you found Hadoop. What implications must you consider when working at this scale? This lecture addresses common challenges and general best practices for scaling with your data.

NodeXL is a template for Excel 2007 and 2010 that lets you enter a network edge list, click a button, and see the network graph, all in the Excel window. You can easily customize the graph's appearance; zoom, scale and pan the graph; dynamically filter vertices and edges; alter the graph's layout; find clusters of related vertices; and calculate a set of graph metrics. Networks can be imported from and exported to a variety of data formats, and built-in connections for getting networks from Twitter, Flickr, YouTube, and your local email are provided.

Today, we measure the size of the Web in exabytes and are uploading to it 15 times more data than we were 3 years ago. Technologies for sensing, storing, and sharing information are driving innovation in the tools available to help us understand our world in greater detail and accuracy than ever before. The implications of analyzing data on a massive scale transcend the tech industry, impacting the environmental sector, social justice issues, health and science research, and more. When coupled with astute technical insight, data is dynamic, accessible, and ultimately, creative. Marissa Mayer will speak to the power of data and the role it plays in Google's innovation. She will present on the technology trends that are changing our relationship with data, discuss fresh Google products that creatively put data to work, and offer her vision for the future of data in driving the Web forward.

As the integration of community-centred teaching practices intensifies, an understanding of the types of relationships that manifest in this network and the associated impact on student learning is required. This paper explores the relationship between a student's position in a classroom social network and their reported level of sense of community. Quantitative methods, such as Rovai's (2002b) Classroom Community Scale and social network centrality measures, were incorporated to evaluate an individual's level of sense of community and their position within the classroom social network. Qualitative methods such as discussion forum content analysis and student interviews were adopted to clarify and further inform this relationship. The results demonstrate that the centrality measures of  closeness and  degrees are positive predictors of an individual's reported sense of community whereas,  betweenness indicates a negative correlation. Qualitative analyses indicate that an individual's pre-existing external social network influences the type of support and information exchanges an individual requires and therefore, the degree of sense of community ultimately experienced. The paper concludes by discussing future recommendations for teaching practices incorporating computer-mediated communications. 

Data alone isn't the answer. In fact, from a business perspective, the data is still part of problem. Insight is the answer, which is derived from the data.