Tag: A/B Testing
- Why A/B Testers Have The Best Jobs In Tech - Mar 22, 2017.
Learning about what these people do made it clear that when you are deeply involved in A/B testing at scale, there is a tremendous rush from doing so many different things that matter.
- 17 More Must-Know Data Science Interview Questions and Answers, Part 3 - Mar 15, 2017.
The third and final part of 17 new must-know Data Science interview questions and answers covers A/B testing, data visualization, Twitter influence evaluation, and Big Data quality.
- Doing Data Science at Twitter - Sep 16, 2015.
Data scientist career exciting, fulfilling and multidimensional career path. Understand through the journey of a data scientist of twitter about data scientists roles, responsibilities and skills required to perform them.
- Interview: Ramkumar Ravichandran, Visa on Actionable Insights – Easier Said Than Done - Jul 14, 2015.
We discuss Analytics at Visa, adapting to the Big Data world, gaps between expectations and delivery from Analytics, delivering Actionable Insights, and tools/technologies used.
- Predictive Analytics Innovation Summit, San Diego: Day 2 Highlights - Apr 8, 2015.
Highlights from the presentations by Predictive Analytics leaders from eBay, LinkedIn and Facebook on day 2 of Predictive Analytics Innovation Summit 2015 in San Diego.
- Interview: Vince Darley, King.com on the Serious Analytics behind Casual Gaming - Mar 18, 2015.
We discuss key characteristics of social gaming data, ML use cases at King, infrastructure challenges, major problems with A-B testing and recommendations to resolve them.
- Interview: Cliff Lyon, Stubhub on Mastering the Art of Recommendation and Personalization Analytics - Jul 18, 2014.
We discuss challenges in designing recommendation and personalization systems, how to select the right metrics, and learning regarding presentation of recommendation on different channels.
- KDnuggets Interview: Juan Miguel Lavista, Microsoft Data Science Team - Apr 30, 2014.
We discuss Randomized Controlled Experiments, common errors during A/B testing, Correlation vs. Causality, Big Data Myths and setting up realistic expectations from Big Data and more...