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Data Everywhere: Making Data Work - O'Reilly Webcast

913 views · Published 14 May 2014 · 1:34:27 · Indexed 8 October 2026

Channel: O'Reilly · 2014 · Science & Technology

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In this webcast about Data Anthropology, Quantified Self, Machine Data, Human Centered Design, and more, you can learn how the Industrial Internet is optimizing the assets and operations of large industries; how to apply design thinking to your data and identify problems and opportunities; the ways that sleep data gathered from hundreds of thousands of UP wrist bands is pushing the boundaries of understanding our bodies. You'll also get a sneak peak into the future of spreadsheets, still the number one tool for financial analysis, as well as how to express yourself in R.

About Alistair Croll

Alistair is the chair of O'Reilly's Strata conference, Techweb's Cloud Connect, and the International Startup Festival. "Lean Analytics" is his fourth book on analytics, technology, and entrepreneurship. He lives in Montreal, Canada and tries to mitigate chronic ADD by writing about far too many things at "Solve For Interesting". 

Spreadsheets: The Dark Matter of Big Data. By: Felienne Hermans of Delft University.

In this talk Felienne will summarize her recently completed PhD research on the topic of spreadsheet structure visualization, spreadsheet smells and clone detection, as well as presenting a sneak peek into the future of spreadsheet research as Delft University.

Expressing Yourself in. By: Hadley Wickham

There are three main time sinks in any data science task:

1. Figuring out what you want to do. 
2. Turning a vague goal into a precise set of tasks (i.e. programming). 
3. Actually crunching the numbers.

Data visualisation and manipulation are key parts of the data science process. ggvis makes it easy to declaratively describe interactive web graphics. It combines a declarative syntax based on ggplot2 with shiny's reactive programming model and vega's declarative JS rendering system. dplyr implements the most important verbs of data manipulation in a datastore-agnostic fashion, so you can think about and compute with your data in the same way regarldess of whether you're working with a local in-memory data frame or a remote on-disk database. Hadley Wickham is Chief Scientist at RStudio.

Big Industrial Internet Data: Connecting and Optimizing at New Scales. By: Steven Gustafson

The Industrial Internet is all about optimizing an industry's assets and operations. I will introduce the concepts and technologies behind the Industrial Internet, describe how Big Data is a critical component, survey how industry is approaching Big Data, and describe several of our existing efforts to research and develop technologies for the Industrial Internet utilizing Big Data.  Dr. Gustafson leads the Knowledge Discovery Lab at the General Electric Global Research Center in Niskayuna, New York. 

Design Thinking for Dummies (Data Scientists). By: Dean Malmgren

Being "data-driven" is about more than just storing lots of data and generating reports. As with many other types of projects, the most crucial part of any data-oriented project is choosing an appropriate problem or opportunity on which to focus in the first place. In this tutorial, you will learn how to apply design thinking to identify problems and opportunities where data can be used as part of a solution. Dean Malmgren is co-founder and managing partner of Datascope Analytics.

Bedtime Stories: Learning from Sleep Data. By: Monica Rogati

We optimize ads, but not our mood. We know more about our tweets than our own bodies. As wearables transform the "quantified self" from a niche to a mainstream market, they are generating vast amounts of data about our health, habits, and lifestyles. With sleep data gathered from hundreds of thousands of UP wrist bands, we can push the boundaries of understanding our bodies beyond what was possible with traditional studies alone. We can now understand not only whether men sleep longer than women, but also how that changes with age.  Monica is a data scientist. As the VP of Data for Jawbone. 

Soylent Mean: Data Science is Made of People. By: Cameran Hetrick & Kimberly Stedman

Combine your best algorithms and smar data architecture, and what do you get? Without humans, you have an expensive, high tech brick. Humans generate data, which is used by and for humans to achieve human goals. If you want your data department to earn its keep by showing real value, you must build your social systems as meticulously as you build your pipeline. 
Cameran is the Director of Analytics for VMware.

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