Data Science from A to Z

Join us for the our first 2017 DATA SCIENCE event to kick off the year!

We will have five lightning talks from Coursera Data Scientists covering decision science, data products, career paths, & more..

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Building Discovery Data Product

Airong Cai

I'll introduce a couple of data products built to match learners with the most relevant content to reach their goals. The talk will cover the products' motivations, methodology and tools used, as well as sample results.

Airong Cai is a Data Scientist working on Discovery Data Products. She builds data products to improve learners’ discovery experience including in search and browse.

AB Testing our way to Better Products

Katherine Wong

Learn about the lifecycle of an AB test at Coursera! I'll cover why and how we test new product features, as well as nuances we consider. I’ll take you through a couple examples of how AB testing has influenced our product and touch on cases where we may not want to use AB testing.

Katherine is a Data Scientist working on Growth Analytics. She leads the AB testing efforts that support our Growth Product team in making data-informed decisions on new features and product strategy.

The Journey from Ph.D. to Data Science

Miaomiao Wen

In this talk, I’ll share my experience transitioning from academia to data science. I’ll compare my typical day as a computer science PhD to my typical day as a data scientist. I'll also share some tips on preparing for interviews.

Miaomiao Wen is a data scientist working on Learning Analytics. She graduated from Language Technologies Institute, Carnegie Mellon University in September 2016 with a PhD in computer science. She studied team formation and collaboration in online communities and MOOCs.

Roles, Orgs, and Talent: Building for Impact

Emily Glassberg Sands

Data science is a broad and ever-evolving field with diverse talent and a range of roles and team structures. I'll highlight some of the different roles and organizational structures through the lens of our team’s evolution here at Coursera.

Emily is a data science manager at Coursera. Her team includes data scientists working on decision science and data products to grow reach and improve the learning experience across Coursera’s consumer and enterprise products.

Hearing the User Voice through Data

Ning Liu

For questions that cannot be answered in user behavior data alone, why not ask our users directly? Lucky for us, learners at Coursera have been graciously providing direct feedback -- from course rating and satisfaction to learning progress and beyond. In this talk, I'll cover user voice data collection and analysis, and highlight some of the lessons learned.

Ning is a data scientist working on Learning Analytics. She partners with Learning Experience Product team to provide the best online learning experience.