Matthew Renze introduces the R programming language as well as demonstrates how R can be used to create data visualizations to complete day-to-day developer tasks.
Scott W. Ambler explores disciplined agile strategies to avoid and remove existing technical debt, how to fund the removal of technical debt, and related industry data.
Lyndon Maher, Paul McManus discuss data driven development, how to collect data, getting feedback, tools to use, and how to integrate a data-driven mentality into the team.
Larry Maccherone presents his top 10 tips for using data to influence others toward better decisions.
The authors show how statistical debugging can be used for diagnosing performance problems, lowing the overhead of run-time performance diagnosis without extending the diagnosis latency.
Garrett Eardley explores how Riot Games is using Riak for their stats system, discussing why they chose Riak, the data model and indexes, and strategies for working with eventually consistent data.
Ronny Kohavi shares lessons learned, cultural and scaling challenges conducting hundreds of concurrent online controlled experiments at Bing.
Jonathan Seidman and Ramesh Venkataramaiah present how they run R on Hadoop in order to perform distributed analysis on large data sets, including some alternatives to their solution.
Nathan Marz explain Storm, a distributed fault-tolerant and real-time computational system currently used by Twitter to keep statistics on user clicks for every URL and domain.
Hilary Mason presents the history of machine learning covering the most significant developments in the area, and showing how bit.ly uses it to discover various statistical information about users.