"LISA is where professionals share what's hot in designing, building, and maintaining critical systems."
Tom Limoncelli, author, speaker, and system administrator
"LISA is the place where industry best practices and cutting-edge research come together to advance system administration."
Nicole Forsgren Velasquez, Utah State University
"LISA is where I find direction for evolving the my core professional skills."
LISA '13 Attendee
"I use LISA to benchmark the SA activities in my company."
LISA '13 Attendee
"I keep coming back for the technical content and the personal networking opportunities. I attend for career development."
LISA '13 Attendee
"LISA is the conference that I send my system administrators to so they can bring the latest tools and techniques back to the rest of the team. Much of our current environment can be traced directly back to LISA."
Cory Lueninghoener, Deputy Group Leader of Production High Performance Computing at Los Alamos National Laboratory
"Information from LISA helps us push the envelope on automation and scaling, allowing a team of four to manage over 3000 Firefox build and test systems running 15 different operating systems."
Amy Rich, Manager of Release Engineering Operations at Mozilla
"LISA is the conference that I send my system administrators to so they can bring the latest tools and techniques back to the rest of the team. Much of our current environment can be traced directly back to LISA."
Cory Lueninghoener, Deputy Group Leader of Production High Performance Computing at Los Alamos National Laboratory
"LISA is where professionals share what's hot in designing, building, and maintaining critical systems."
Tom Limoncelli, author, speaker, and system administrator
LISA: Where systems engineering and operations professionals share real-world knowledge about designing, building, and maintaining the critical systems of our interconnected world.
The LISA conference has long served as the annual vendor-neutral meeting place for the wider system administration community. The LISA14 program recognized the overlap and differences between traditional and modern IT operations and engineering, and developed a highly-curated program around 5 key topics: Systems Engineering, Security, Culture, DevOps, and Monitoring/Metrics. The program included 22 half- and full-day training sessions; 10 workshops; and a conference program consisting of 50 invited talks, panels, refereed paper presentations, and mini-tutorials.
This tutorial is a course in statistics with a specific focus on system administrators and the types of data they face. We assume little prior knowledge of statistics and cover the most common concepts in descriptive statistics and apply them to data taken from real-life examples. Our aim is to provide insight into what methods provide good interpretation of data such as distributions, probability and formulating basic statements about the properties of observed data.
The tutorial instructors will be available in a Lab Space following the tutorial in order to answer questions and offer personal feedback on cases the attendees wish to investigate with their own data.
Who should attend:
Sysadmins who are faced with data overload and wish they had some knowledge of how statistics can be used to make more sense of it. We assume little prior knowledge of statistics, but a basic mathematical proficiency is recommended.
Take back to work:
A fundamental understanding of how descriptive statistics can help provide additional insight on the data in the sysadmin world and that will allow for further self-study on statistics.
A basic set of statistical approaches that can be used to identify fundamental properties of the data they see in their own environments, and identify patterns in that data.
Learn how to make accurate and clear statements about metrics that are valuable to their organization.
Topics include:
Descriptive statistics for single datasets, including: mean, median, mode, range, and distributions
Basic analysis of distributions and probabilities using percentiles typically seen in ops
Interpretation of analyses to include team and business implications
Regression analysis to suggest predictive relationships, with an emphasis on interpretation and implications
Correlation analysis and broad pattern detection (if time allows)
