Big Data Introduction to D3

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Synopsis: D3.js is a Javascript library primarily used to create interactive data visualizations in the browser. Despite its growing popularity and warm community, getting started with D3 can be tricky. This talk covers the basics of D3 and sheds light on some of its main conceptual hurdles. It concludes by discussing some applications of D3 to big data. About the speaker: Sam Selikoff [ http://www.samselikoff.com/ | @samselikoff ] is a self-taught full-stack web developer. Formerly a graduate student of economics and finance, he unexpectedly discovered a passion for programming while doing data work for a consulting firm. He is currently focusing on client-side MVC and data visualization. Thanks to our Sponsors Microsoft [ http://microsoftnewengland.com ] for providing awesome venue for the event. Rovi [ http://rovi.com ] for providing the food/drinks. cognizeus [ http://cognizeus.com ] for hosting the event and providing books to give away as raffle.

Technology Art & Photos

INTRO TO D3
with applications to big data
Feb 2014
@samselikoff
www.samselikoff.com

Apple today announced financial results for its fiscal 2014 first
quarter ended December 28, 2013. The Company posted
record quarterly revenue of $57.6 billion and quarterly net
profit of $13.1 billion, or $14.50 per diluted share. These
results compare to revenue of $54.5 billion and net profit of
$13.1 billion, or $13.81 per diluted share, in the year-ago
quarter. Gross margin was 37.9 percent compared to 38.6
percent in the year-ago quarter. International sales accounted
for 63 percent of the quarter’s revenue.

Hypothetical bars in a document. Lets set their heights:
With JS
var data = [80, 53, 125, 200, 28, 97];
var bars = document.getElementsByTagName("rect");
for (var i = 0; i < bars.length; i++) {
var bar = bars.item(i);
bar.style.setProperty("height", data[i], null);
}
With D3
d3.selectAll('rect')
.attr('height', function(d, i) {return data[i];});

D3 IS NOT:
DOM query lib
Compatibility layer
Charting library
Easy!
Proprietary 3rd-party tech

HOW CAN D3 HELP US?
Less convenient, but more powerful

THE PATH TO LEARN
Examples
Practice
Reading
Repeat
Today, higher-level concepts

Initial document
<html>
<body>
<script src="d3.v3.min.js" charset="utf-8"></script>
<script>
// Our code
</script>
</body>
</html>
Some data
var data = [80, 53, 125, 200, 28, 97];

First, need a parent <svg>
d3.select('body').append('svg');
d3 is global object - think $ from jquery
Lets us select elements - similar to jquery
Can perform operations on these selections
like `append`, or `style`
d3.select('body').style('background-color', 'blue');

.append actually returns a new selection
var svg = d3.select('body').append('svg');
Work with local var svg
just as if we had done d3.select('svg')

Let's make the bars. We could just...
// Recall, var data = [80, 53, 125, 200, 28, 97];
svg.append('rect');
svg.append('rect');
svg.append('rect');
svg.append('rect');
svg.append('rect');
svg.append('rect');
But this falls short

d3.selectAll wraps arrays of elements
var paragraphs = d3.selectAll('p');
So what are selections?
Understanding selections is key to writing d3 code.

Selections enable declarative programming
Imperative
paragraphs.forEach(function(p) {
p.style('background-color', 'green');
});
Declarative
paragraphs.style('background-color', 'green');

We can also select no elements
<svg>
</svg>
var bars = d3.selectAll('rect');
Again, selections are higher level
In this case, `bars` doesn't refer to anything in the DOM
But it does represent an array of <rect> elements

Selections have two pieces
The key to D3's power!

The data join
var nums = [80, 53, 125, 200, 28, 97];
var bars = svg.selectAll('rect')
.data(nums)
Our representation is now explicit

var data = [80, 53, 125, 200, 28, 97];
var bars = svg.selectAll('rect')
.data(data);
But our DOM is empty
This means there are six <rect> in our enter selection
bars.enter()
.append('rect');

Where does the data actually live?
The DOM
This enables selections to be transient
d3.selectAll('rect').data()

Data-driven transformations
Let's finish up the bar chart.

What next?
Scales, axes, events, transitions...
https://github.com/mbostock/d3/wiki/Gallery
https://github.com/mbostock/d3/wiki/Tutorials
StackOverflow
d3 mailing list (google group) + IRC
Practice, inspect, examples

Can something so low-level be useful for big data?
: filter 250,000 data points
: hundreds of metrics updating in real-time
analytics
analytics
financial tools
Open-source tools binding D3 to R, Python
Much, much more...
Crossfilter
Cubism
Netflix
Square
Addepar

THANKS!
@samselikoff
www.samselikoff.com

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On June 11 Thomas Dinsmore gave a nice outline on tools and technologies that are out there handling analytics in Hadoop. It is a must watch for anyone looking for what advance analytics Hadoop could deliver. Please find video and slides below. Synopsis What is the state of play for advanced analytics in Hadoop? A year ago, options included "roll your own" and little else; today there are a number of serious open source and commercial options available, with new capabilities announced daily. In this presentation, we begin with a brief overview of use cases for advanced analytics and a discussion of what types of analytics must run in Hadoop. We continue with an overview of available architectures. The presentation concludes with a hype-free survey of available open source and commercial software for advanced analytics in Hadoop. Bio Thomas W. Dinsmore is Director of Product Management for Revolution Analytics, a company that provides commercial support and services for open source R. In this role, Mr. Dinsmore closely tracks the market for commercial and open source software on all platforms, including Hadoop. Prior to joining Revolution Analytics, Mr. Dinsmore served as an Analytics Solution Architect for IBM Big Data, and as a Principal Consultant for Razorfish and SAS. Mr. Dinsmore has hands-on experience with leading commercial and open source tools for advanced analytics, including SAS, SPSS, R, Oracle Data Mining across a range of platforms, including Hadoop, Netezza, Teradata and Oracle. He is certified in SAS 9. In his career, Mr. Dinsmore has worked with more than 500 enterprises in the United States, Canada, Mexico, Venezuela, Chile, Brazil, the United Kingdom, Belgium, Italy, Turkey, Israel, Malaysia and Singapore.

Rethinking classical approaches to analysis and predictive modeling

AnalyticsWeek

Synopsis: The speaker will address the need to rethink classical approaches to analysis and predictive modeling. He will examine "iterative analytics" and extremely fine grained segmentation down to a single customer -- ultimately building one model per customer or millions of predictive models delivering on the promise of "segment of one" . The speaker will also address the speed at which all this has to work to maintain a competitive advantage for innovative businesses. Speaker: Afshin Goodarzi, Chief Analyst 1010data A veteran of analytics, Goodarzi has led several teams in designing, building and delivering predictive analytics and business analytical products to a diverse set of industries. Prior to joining 1010data, Goodarzi was the Managing Director of Mortgage at Equifax, responsible for the creation of new data products and supporting analytics to the financial industry. Previously, he led the development of various classes of predictive models aimed at the mortgage industry during his tenure at Loan Performance (Core Logic). Earlier on he had worked at BlackRock, the research center for NYNEX (present day Verizon) and Norkom Technologies. Goodarzi's publications span the fields of data mining, data visualization, optimization and artificial intelligence. Sponsor: 1010Data [ http://1010data.com ] Microsoft NERD [ http://microsoftnewengland.com ] Cognizeus [ http://cognizeus.com ]

Using Topological Data Analysis on your BigData

AnalyticsWeek

Synopsis: Topological Data Analysis (TDA) is a framework for data analysis and machine learning and represents a breakthrough in how to effectively use geometric and topological information to solve 'Big Data' problems. TDA provides meaningful summaries (in a technical sense to be described) and insights into complex data problems. In this talk, Anthony will begin with an overview of TDA and describe the core algorithm that is utilized. This talk will include both the theory and real world problems that have been solved using TDA. After this talk, attendees will understand how the underlying TDA algorithm works and how it improves on existing “classical” data analysis techniques as well as how it provides a framework for many machine learning algorithms and tasks. Speaker: Anthony Bak, Senior Data Scientist, Ayasdi Prior to coming to Ayasdi, Anthony was at Stanford University where he did a postdoc with Ayasdi co-founder Gunnar Carlsson, working on new methods and applications of Topological Data Analysis. He completed his Ph.D. work in algebraic geometry with applications to string theory at the University of Pennsylvania and ,along the way, he worked at the Max Planck Institute in Germany, Mount Holyoke College in Germany, and the American Institute of Mathematics in California.

More from AnalyticsWeek (8)

Understanding Customer Buying Journey with Big Data

Data-As-A-Service to enable compliance reporting

Making sense of unstructured data by turning strings into things

Reimagining the role of data in government

The History and Use of R

Advanced Analytics in Hadoop

Rethinking classical approaches to analysis and predictive modeling

Using Topological Data Analysis on your BigData

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