STAREAST 2017 Tutorial: Data Analytics and Machine Learning

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Tuesday, May 1, 2018 - 1:00pm to 4:30pm

Data Analytics and Machine Learning

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Do you have access to lots and lots of test, development, app, and service data—really big data—from client and cloud service log files, test execution results, and more? Then, you have a great opportunity to begin using data analytics and machine learning (ML) to gain new product quality insights. Bring your laptops and your sense of discovery as Ken Johnston and Eun Change introduce analysis techniques and ML tools to help you develop new and potentially groundbreaking insights. First, they present a fast-paced statistics primer for those with no prior data exploration experience and others looking for a refresher in descriptive statistics. With that foundation, Ken and Eun introduce machine learning and walk you through foundational ML algorithms. They demonstrate several open source ML tools that you can start using right away. Leave with an understanding of the what, why and how of data analytics—and a jumpstart on your machine learning journey.

Note: Participants are encouraged—but not required—to bring a laptop computer to this tutorial.

Ken Johnston
Microsoft

A principal manager on the Microsoft core data science team, Ken Johnston and his team focus their research on Windows post sales monetization and device usage within the commercial and education segments. Since joining Microsoft in 1998, Ken has held roles of group program manager for Bing Data Quality and Measurements, group manager for Bing Shopping and Data Operations, test lead and test manager on MSN, and director of test excellence. Ken coauthored How We Test Software at Microsoft and is a contributing author to Experiences of Test Automation: Case Studies of Software Test Automation. Contact Ken on Twitter.

Eun Chang
Microsoft

Eun Chang is a lifelong learner, an avid consumer of new technologies, and a data scientist at Microsoft specializing in machine learning. In her career at Microsoft, Eun has driven a number of projects focusing on Windows post sales monetization and enterprise device usage. Her research uses a broad spectrum of techniques ranging from statistical inference to deep learning. Eun is thrilled when a seemingly simple problem results in a novel, mathematically intricate solution. Her current work involves incorporating neural networks into areas such as feedback classification and community detection. Contact Eun via email or on LinkedIn.