Presented By O'Reilly and Cloudera
Make Data Work
March 28–29, 2016: Training
March 29–31, 2016: Conference
San Jose, CA

Data 101 conference sessions

Tuesday, March 29

9:05am–9:30am Tuesday, 03/29/2016
Location: LL20B
Edd Wilder-James (Google)
Average rating: ****.
(4.00, 4 ratings)
Spark is white-hot at the moment, but why does it matter? Developers are usually the first to understand why some technologies cause more excitement than others. Edd Dumbill relates this insider knowledge, providing a tour through the hottest emerging data technologies of 2016 to explain why they’re exciting in terms of both new capabilities and the new economies they bring. Read more.
9:30am–10:00am Tuesday, 03/29/2016
Location: LL20B
Tim Berglund (Confluent)
Average rating: ****.
(4.25, 4 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, Tim Berglund offers several examples of distributed-systems functions, exploring topics like distributed storage, computation, timing, messaging, and consensus. Read more.
10:00am–10:30am Tuesday, 03/29/2016
Location: LL20B
Yael Garten (LinkedIn)
Average rating: ***..
(3.50, 2 ratings)
You’ve decided you need data scientists. You know who to hire. Now, what do you do with them? Yael Garten offers examples of how companies like LinkedIn use data to make business and product decisions. Yael reviews the spectrum of data science, and discusses the culture, process and tools needed to transform companies into data-driven organizations. Read more.
11:00am–11:40am Tuesday, 03/29/2016
Location: LL20B
Tags: education
Matthew Gee (Impact Lab/University of Chicago )
Average rating: ****.
(4.00, 4 ratings)
Machine-learning algorithms are the workhorses of the data economy but often seem like one part math and two parts magic. Matthew Gee demystifies the core concepts of machine learning, gives practical examples of applications, and walks attendees 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.
11:40am–12:05pm Tuesday, 03/29/2016
Location: LL20B
Ben Sharma (Zaloni)
Average rating: ***..
(3.00, 2 ratings)
Ben Sharma uses popular cloud-based use cases to explore how to effectively and safely leverage big data in the cloud to achieve business goals. Now is the time to get the jump on this trend before your competition gets the upper hand! Read more.
12:05pm–12:30pm Tuesday, 03/29/2016
Location: LL20B
Julia Rodriguez (Eagle Investment Systems)
Average rating: *****
(5.00, 2 ratings)
Designing data visualizations presents unique and interesting challenges: how to tell a compelling story, how to deliver important information in a forthright, clear format, and how to make visualizations beautiful and engaging. Julie Rodriguez shares a few disruptive designs and connects them back to Vizipedia, her compiled data visualization library. Read more.