Presented By O'Reilly and Cloudera
Make Data Work
Sept 29–Oct 1, 2015 • New York, NY

Data 101 conference sessions

Tuesday, September 29

9:00am–9:20am Tuesday, 09/29/2015
Location: 3D 05 / 08
Paco Nathan (derwen.ai)
Average rating: ****.
(4.60, 10 ratings)
Whether starting a data science program, reaching the breaking point with your current data technology, or figuring out what the competition is up to, these sessions will give you a bird’s-eye view of data technologies, techniques, and data-driven organizations. Read more.
9:20am–9:55am Tuesday, 09/29/2015
Location: 3D 05 / 08 Level: Intermediate
Tim Berglund (Confluent)
Average rating: ***..
(3.80, 10 ratings)
Normally simple tasks like running a program or storing and retrieving data become much more complicated when we start to do them on collections of computers, rather than single machines. Using the analogy of a coffee shop, we'll look at several examples of distributed systems functions. Read more.
9:55am–10:30am Tuesday, 09/29/2015
Location: 3D 05 / 08 Level: Non-technical
Edd Wilder-James (Google)
Average rating: ***..
(3.88, 17 ratings)
Spark is white-hot, but why does it matter? Some technologies cause more excitement than others, and at first the only people who understand why are the developers who use them. This talk provides a tour through the hottest emerging data technologies of 2015 and explains why they’re exciting, in the context of the new capabilities and economies they bring. Read more.
11:00am–11:30am Tuesday, 09/29/2015
Location: 3D 05 / 08 Level: Non-technical
Matthew Gee (Impact Lab/University of Chicago )
Average rating: ***..
(3.56, 18 ratings)
Machine learning algorithms are the workhorses of the data economy, but often seem like one part math and two parts magic. In this session, we'll demystify core concepts in machine learning, give practical examples of applications, and walk through some basic rules for deciding if your organization’s key questions and data sources are a good fit for a machine learning solution. Read more.
11:30am–12:00pm Tuesday, 09/29/2015
Location: 3D 05 / 08 Level: Intermediate
Katie Kent (Galvanize)
Average rating: ***..
(3.80, 15 ratings)
It's hard to hire data scientists. Companies like Facebook, Twitter, Airbnb, etc. have built competitive advantages by building great teams. This talk draws from original research into team setup and hiring practices of 200 technology employers. Listeners will learn when to hire a data scientist, which kind to hire, how to interview reliably, and specific recommendations about how to hunt them. Read more.
12:00pm–12:30pm Tuesday, 09/29/2015
Location: 3D 05 / 08 Level: Non-technical
Yael Garten (LinkedIn)
Average rating: ***..
(3.33, 15 ratings)
You’ve decided you need data scientists. You know who to hire. Now, what do you do with them? We’ll discuss examples of how companies like LinkedIn make business decisions from data. We’ll review the spectrum of data science, data quality and platforms, and how data scientists drive the art, science, and politics of defining KPIs to transform into a data-driven org. Read more.