ENTRIES TAGGED "strata"
Who do you trust? You are surrounded by bots.
Preview of upcoming session "Who is Fake?" at the Strata Conference
By Lutz Finger
In the Matrix, the idea of a computer algorithm determining what we think may seemed far-fetched. Really? Far-fetched? Let’s look at some numbers.
About half of all Americans get their news in digital form. This news is written up by journalists, half of whom at least partially source their stories from social media. They use tools to harvest the real time knowledge of 100,000 tweets per second and more.
But what if someone could influence those tools and create messages that look as though they were part of a common consensus? Or create the appearance of trending?
Keep your data science efforts from derailing
Preview of upcoming session at Strata Santa Clara
By Marck Vaisman and Sean Murphy
Is your organization considering embracing data science? If so, we would like to give you some helpful advice on organizational and technical issues to consider before you embark on any initiatives or consider hiring data scientists. Join us, Sean Murphy and Marck Vaisman, two Washington, D.C. based data scientists and founding members of Data Community DC, as we walk you through the trials and tribulations of practicing data scientists at our upcoming talk at Strata.
We will discuss anecdotes and best practices, and finish by presenting the results of a survey we conducted last year to help understand the varieties of people, skills, and experiences that fall under the broad term of “Data Scientist”. We analyzed data from over 250 survey respondents, and are excited to share our findings, which will also be published soon by O’Reilly.
Maps not lists: network graphs for data exploration
Preview of upcoming Strata session on data exploration
Amy Heineike is Director of Mathematics for Quid Inc, where she has been since its inception, prototyping and launching the company’s technology for analyzing document sets. Below is the teaser for her upcoming talk at Strata Santa Clara.
I recently discovered that my favorite map is online. It used to hang on my housemate’s wall in our little house in London back in 2005. At the time I was working to understand how London was evolving and changing, and how different policy or infrastructure changes (a new tube line, land use policy changes) would impact that.
The map was originally published as a center-page pull out from the Guardian, showing the ethnic groups that dominate different neighborhoods across the city. The legend was as long as the image, and the small print labels necessitated standing up close, peering and reading, tracing your finger to discover the Congolese on the West Green Road, our neighbors the Portuguese on the Stockwell Road, or the Tamils in Chessington in the distant south west.
Big data comes to the big screen
Using data science to predict the Oscars
By Michael Gold, Farsite
Sophisticated algorithms are not going to write the perfect script or crawl YouTube to find the next Justin Beiber (that last one I think we can all be thankful for!). But a model can predict the probability of a nominee winning the Oscar, and recently our model has Argo overtaking Lincoln as the likely winner of Best Picture. Every day on FarsiteForecast.com we’ve been describing applications of data science for the media and entertainment industry, illustrating how our models work, and updating the likely winners based on the outcomes of the Awards Season leading up to the Oscars. Just as predictive analytics provides valuable decision-making tools in sectors from retail to healthcare to advocacy, data science can also empower smarter decisions for entertainment executives, which led us to launch the Oscar forecasting project. While the potential for data science to impact any organization is as unique as each company itself, we thought we’d offer a few use cases that have wide application for media and entertainment organizations.
Fruit or mobile device: learning concepts through connections
Preview of insights shared at upcoming session at Strata Santa Clara
Social media gives us the power to share content and engage with a wide range of internet users. As a person or brand, we are often concerned with who we are talking to and how we can better serve our viewers. Traditional demographics such as ‘female’ and ‘25-30’ are no longer sufficient in this arena. For example, Google is having a hard time getting gender and age correct for ad preferences. It is more interesting to observe what content is consumed and how attention changes over time.
Bitly, which is used to shorten and share links, can offer insight into this space. This means the data has an unprecedented view into what people are sharing and has a holistic view of what users are concerned about on the internet.
We use their data to look into how we can define the audience of different content. The simplest example of this is: given a group of users that click on “oreilly.com”, what other websites do they engage with. We now have what bitly calls a co-click graph. Domains are represented as nodes while edges between nodes represent the number of people that have clicked on each domain. A co-click graph can be made to represent any number of attributes, but for now we are going to remain interested in topics and keywords.
That’s it — I’m taking my data and going home
We are simply not good at playing with others when it comes to data
Russia’s railway gauge is different from Western Europe’s. At the border of the former Soviet states, the Russian gauge of 1.524m meets the European & American ‘Standard’ gauge of 1.435m. The reasons for this literal disconnect arise from discussions between the Tsar and his War Minister. When asked the most effective way to prevent Russia’s own rail lines being used against them in times of invasion, the Minister suggested a different gauge to prevent supply trains rolling through the border. The artifact of this decision remains visible today at all rail crossings between Poland and Belarus or Slovakia and Ukraine. The rail cars are jacked up at the border, new wheels inserted underneath, and the car lowered again. It is about a 2-4 hour time burn for each crossing.
Per head, per crossing, over 170 years, is a heck of a lot of resource wasted. But to change it would entail changing the rail stock of the entire country and realigning about 225,000 km (140,000 mi) of track.
Talk about technical debt.
Data suffers from a similar disconnect. It really wasn’t until the advent of XML 15 years ago that we had an agreed (but not entirely satisfactory) mechanism for storing arbitrary data structures outside the application layer. This is as much a commentary on our technical priorities as it is a social indictment. We are simply not good at playing with others when it comes to data.
Facet: The recursive approach to visualization
Sneak peek at my upcoming session at the Strata Conference in Santa Clara
Visualizing data and extracting it from its data store are two activities that go hand in hand. Typically, when you try to use a data visualization toolkit such as Raphael, Protovis or D3 to create a non-trivial visualization, you spend a significant portion of your time writing code to extract the data. The process may involve querying an external database then transforming the resulting data to the correct structure for your visualization.
In his paper introducing plyr, a data manipulation toolkit for R, Hadley Wickham describes a framework, split-apply-combine, for expressing common data operations. The idea is that most data operations can be seen as splitting the data into a series of buckets, applying some aggregation to each bucket to get an aggregate and then combining the results by sorting and limiting. Wickham argues that most data query languages already rely on an equivalent framework whether explicitly or implicitly.
BigData Top 100 Initiative
A Call for Industry-Standard Benchmarks for Big Data Platforms at Strata SC 2013
By Milind Bhandarka, Chaitan Baru, Raghunath Nambiar, Meikel Poess, and Dr. Tilmann Rabl
Big data systems are characterized by their flexibility in processing diverse data genres, such as transaction logs, connection graphs, and natural language text, with algorithms characterized by multiple communication patterns, e.g. scatter-gather, broadcast, multicast, pipelines, and bulk-synchronous. A single benchmark that characterizes a single workload could not be representative of such a multitude of use-cases. However, our systematic study of several use-cases of current big data platforms indicates that most workloads are composed of a common set of stages, which capture the variety of data genres and algorithms commonly used to implement most data-intensive end-to-end workloads. Our upcoming session at Strata SC discusses the BigData Top 100 List, a new community-based initiative for benchmarking big data systems.
Your analytics talent pool is not made up of misanthropes
Tips for interacting with analytics colleagues
To quote Pride and Prejudice, businesses have for many years “labored under the misapprehension” that their analytics talent was made up of misanthropes with neither the will nor the ability to communicate or work with others on strategic or creative business problems. These employees were meant to be kept in the basement out of sight, fed bad pizza, and pumped for spreadsheets to be interpreted in the sunny offices aboveground.
This perception is changing in industry as the big data phenomenon has elevated data science to a C-level priority. Suddenly folks once stereotyped by characters like Milton in Office Space are now “sexy.” The truth is there have always been well-rounded, articulate, friendly analytics professionals (they may just like Battlestar more than you), and now that analytics is an essential business function, personalities of all types are being attracted to practice the discipline.
Communicating data clearly
Preview of Strata Santa Clara 2013 Session
The 2013 Strata Conference in Santa Clara, CA will be my fifth Strata conference. As always, I’m excited to join so many leaders in the data and data viz communities, and I’m honored that I’ll be speaking there.
I will be presenting my tutorial “Communicating Data Clearly” at 9AM on Tuesday, February 26. This talk will cover methods and principles of creating effective graphs, to ensure they are clear, accurate, and make it easier to understand the data. It will also emphasize how to avoid common graphical mistakes. To give you a preview of a few of the topics I will be covering as well as to provide some information to those who cannot attend, I will now link to some of the blog posts I‘ve written for Forbes. I was invited to blog for Forbes at a New York Strata Conference in 2011 so that my relationships with Forbes and Strata are intertwined.
