Better Software West 2017 Tutorial - Statistics in Big Data Analysis: Beyond Counting | TechWell

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Tuesday, June 6, 2017 - 1:00pm to 4:30pm

Statistics in Big Data Analysis: Beyond Counting

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Do you have data—lots and lots of really big data? Do you know what it’s telling you? Maybe your organization is stuck in dashboard mode, counting how many of this or that. And since you have big data, perhaps you even calculate an average every once in awhile. Therein lies the problem: Organizations don’t advance their use of big data. They just spend more time and money processing more data so they can count it just a little better. Ken Johnston helps you go beyond counting and into statistical relevance. This statistics primer is designed for those with no prior experience or those looking for a refresher. Ken focuses mostly on the what, why, and when of using different statistical techniques. See how easy it is to find a tool and a specific calculation when you know which approach makes sense. Learn different types of data and distributions, techniques for data exploration, and the best use of descriptive statistics. Discover the value of your big data and start driving real business value.

Ken Johnston

Ken Johnston is a principal data science manager on the Microsoft core data science team where he and his team focus their research on Windows post sales monetization and device usage in the commercial and education segments. Since joining Microsoft in 1998 Ken’s roles have included GPM for Bing data quality and measurements; group manager for Bing shopping and data operations; test lead and test manager on MSN, hosted exchange, subscription and billing platform, and office products. He previously served as the Microsoft director of test excellence. Ken is a frequent presenter, a regular blogger, coauthor of How We Test Software at Microsoft, and contributor to Experiences of Test Automation: Case Studies of Software Test Automation. Contact Ken on Twitter @rkjohnston.

Eun Chang

Eun Chang is a lifelong learner, an avid consumer of new technologies, and a data scientist at Microsoft specializing in machine learning. As part of her career at Microsoft, Eun has driven a number of projects focusing on Windows post sales monetization and enterprise device usage. Her research utilizes a broad spectrum of techniques ranging from statistical inference to deep learning. She is thrilled when a seemingly simple problem results in a novel, mathematically intricate solution—training a supervised learning model without a training set was one such example. Her current work involves incorporating neural networks into areas such as feedback classification and community detection. Eun can be contact via email or on LinkedIn.