ENTRIES TAGGED "data science"

Financial analytics as a service

Analytic services are tailoring their solutions for specific problems and domains

In relatively short order Amazon’s internal computing services has become the world’s most successful cloud computing platform. Conceived in 2003 and launched in 2006, AWS grew quickly and is now the largest web hosting company in the world. With the recent addition of Kinesis (for stream processing), AWS continues to add services and features that make it an attractive platform for many enterprises.

A few other companies have followed a similar playbook: technology investments that benefit a firm’s core business, is leased out to other companies, some of whom may operate in the same industry. An important (but not well-known) example comes from finance. A widely used service provides users with clean, curated data sets and sophisticated algorithms with which to analyze them. It turns out that the world’s largest asset manager makes its investment and risk management systems available to over 150 pension funds, banks, and other institutions. In addition to the $4 trillion managed by BlackRock, the company’s Aladdin Investment Management system is used to manage1 $11 trillion in additional assets from external managers.

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Expanding options for mining streaming data

New tools make it easier for companies to process and mine streaming data sources

Stream processing was in the minds of a few people that I ran into over the past week. A combination of new systems, deployment tools, and enhancements to existing frameworks, are behind the recent chatter. Through a combination of simpler deployment tools, programming interfaces, and libraries, recently released tools make it easier for companies to process and mine streaming data sources.

Of the distributed stream processing systems that are part of the Hadoop ecosystem0, Storm is by far the most widely used (more on Storm below). I’ve written about Samza, a new framework from the team that developed Kafka (an extremely popular messaging system). Many companies who use Spark express interest in using Spark Streaming (many have already done so). Spark Streaming is distributed, fault-tolerant, stateful, and boosts programmer productivity (the same code used for batch processing can, with minor tweaks, be used for realtime computations). But it targets applications that are in the “second-scale latencies”. Both Spark Streaming and Samza have their share of adherents and I expect that they’ll both start gaining deployments in 2014.

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Reproducing Data Projects

Popular approaches for reproducing, managing, and deploying complex data projects

As I talk to people and companies building the next generation of tools for data scientists, collaboration and reproducibility keep popping up. Collaboration is baked into many of the newer tools I’ve seen (including ones that have yet to be released). Reproducibility is a different story. Many data science projects involve a series of interdependent steps, making auditing or reproducing1 them a challenge. How data scientists and engineers reproduce long data workflows depends on the mix of tools they use.

The default approach is to create a set of well-documented programs and scripts. Documentation is particularly important if several tools and programming languages are involved in a data science project. It’s worth pointing out that the generation of scripts need not be limited to programmers: some tools that rely on users executing tasks through a GUI also generate scripts for recreating data analysis and processing steps. A recent example is the DataWrangler project, but this goes back to Excel users recording VBA macros.

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Data Scientists and Data Engineers like Python and Scala

Python and Scala are popular among members of several well-attended SF Bay Area Meetups

In exchange for getting personalized recommendations many Meetup members declare1 topics that they’re interested in. I recently looked at the topics listed by members of a few local, data Meetups that I’ve frequented. These Meetups vary in size from 600 to 2,000 total (and 400 to 1,100 active2) members.

I was particularly interested in the programming languages members expressed interest in. What I found3 confirmed trends that we’ve noticed in other data sets (online job postings): Python has surpassed R among data scientists and data engineers, Scala is second to Java among JVM languages, and many folks are interested in Javascript. As pydata tools mature, I’ve encountered people who have shifted more of their data workflow from R over to Python.

Popular programming languages for select Meetups

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Data Wrangling gets a fresh look

We are in the early days of productivity technology in data science

Data analysts have long lamented the amount of time they spend on data wrangling. Rightfully so, as some estimates suggest they spend a majority of their time on it. The problem is compounded by the fact that these days, data scientists are encouraged to cast their nets wide, and investigate alternative (unstructured) data sources. The general perception is that data wrangling is the province of programmers and data scientists. Spend time around Excel users and you’ll learn that they do quite a bit of data wrangling too!

In my work I tend to write scripts and small programs to do data wrangling. That usually means some combination1 of SQL, Python, and Spark2. I’ve played with Google Refine (now called OpenRefine) in the past, but I found the UI hard to get used to. Part of the problem may have been that I didn’t use the tool often3 enough to become comfortable.

For most users data wrangling still tends to mean a series of steps that usually involves different tools (e.g., you often need to draw charts to spot outliers and anomalies). As I’ve pointed out in previous posts, workflows that involve many different tools require a lot of context-switching, which in turn affects productivity and impedes reproducability.

We are washing our data at the side of the river on stones. We are really in the early, early ages of productivity technology in data science.
Joe Hellerstein (Strata-NYC 2012), co-founder and CEO of Trifacta

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Day-Long Immersions and Deep Dives at Strata Santa Clara 2014

Tutorials for designers, data scientists, data engineers, and managers

As the Program Development Director for Strata Santa Clara 2014, I am pleased to announce that the tutorial session descriptions are now live. We’re pleased to offer several day-long immersions including the popular Data Driven Business Day and Hardcore Data Science tracks. We curated these topics as we wanted to appeal to a broad range of attendees including business users and managers, designers, data analysts/scientists, and data engineers. In the coming months we’ll have a series of guest posts from many of the instructors and communities behind the tutorials.

Analytics for Business Users
We’re offering a series of data intensive tutorials for non-programmers. John Foreman will use spreadsheets to demonstrate how data science techniques work step-by-step – a topic that should appeal to those tasked with advanced business analysis. Grammar of Graphics author, SYSTAT creator, and noted Statistician Leland Wilkinson, will teach an introductory course on analytics using an innovative expert system he helped build.

Data Science essentials
Scalding – a Scala API for Cascading – is one of the most popular open source projects in the Hadoop ecosystem. Vitaly Gordon will lead a hands-on tutorial on how to use Scalding to put together effective data processing workflows. Data analysts have long lamented the amount of time they spend on data wrangling. But what if you had access to tools and best practices that would make data wrangling less tedious? That’s exactly the tutorial that distinguished Professors and Trifacta co-founders, Joe Hellerstein and Jeff Heer, are offering.

The co-founders of Datascope Analytics are offering a glimpse into how they help clients identify the appropriate problem or opportunity to focus on by using design thinking (see the recent Datascope/IDEO post on Design Thinking and Data Science). We’re also happy to reprise the popular (Strata Santa Clara 2013) d3.js tutorial by Scott Murray.

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How companies are using Spark

The inaugural Spark Summit will feature a wide variety of real-world applications

When an interesting piece of big data technology gets introduced, early1 adopters tend to focus on technical features and capabilities. Applications get built as companies develop confidence that it’s reliable and that it really scales to large data volumes. That seems to be where Spark is today. With over 90 contributors from 25 companies, it has one of the largest developer communities among big data projects (second only to Hadoop MapReduce).

Spark Growth by Numbers

I recently became an advisor to Databricks (a startup commercializing Spark) and a member of the program committee for the inaugural Spark Summit. As I pored over submissions to Spark’s first community gathering, I learned how companies have come to rely on Spark, Shark, and other components of the Berkeley Data Analytics Stack (BDAS). Spark is at that stage where companies are deploying it, and the upcoming Spark Summit in San Francisco will showcase many real-world applications. These applications cut across many domains including advertising, marketing, finance, and academic/scientific research, but can generally be grouped into the following categories:

Data processing workflows: ETL and Data Wrangling
Many companies rely on a wide variety of data sources for their analytic products. That means cleaning, transforming, and fusing (unstructured) external data with internal data sources. Many companies – particularly startups – use Spark for these types of data processing workflows. There are even companies that have created simple user interfaces that open up batch data processing tasks to non-programmers.

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Simplifying interactive, realtime, and advanced analytics

Tools for unlocking big data continue to get simpler

Here are a few observations based on conversations I had during the just concluded Strata NYC conference.

Interactive query analysis on Hadoop remains a hot area
A recent O’Reilly survey confirmed SQL is an important skill for data scientists. A year after the launch of Impala, quite a few attendees I spoke with remained interested in the progress of SQL-on-Hadoop solutions. A trio from Hortonworks gave an update on recent improvements and changes to Hive1. A sign that Impala is gaining traction, Greg Rahn’s talk on Practical Performance Tuning for Impala was one of the best attended sessions in the conference. Ditto for a sponsored session on Kognitio’s latest features.

Existing SQL-on-Hadoop solutions require that users define a schema – an additional step given that a lot of data is increasingly in key-value or JSON format. In his talk Hadapt co-founder Daniel Abadi highlighted a solution2 that lets users query complex data types (Hadapt reserializes complex data types to speed up joins). I expect other SQL-on-Hadoop solutions to also offer query support for complex data types in the near future.

Empowering business users
With its launch at the conference, ClearStory joins Platfora and Datameer in the business analytics space. Each company builds tools that lets business users wade through large amounts of data, while emphasizing different areas. Platfora is for interactive visual analysis of massive data sets, while Datameer connects to many data sources (not just Hadoop), has started offering analytics, and can run on a laptop or cluster. Built primarily on the Berkeley stack (BDAS), ClearStory’s interesting platform encourages collaboration and simplifies data harmonization (fusing disparate data sources is a common bottleneck for business users). For organizations willing to tag and describe their data sets, Microsoft unveiled a tool that lets users query data using natural language (UK startup NeutrinoBI uses a similar “search interface”).

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The emergence of Crowdsourcing specialists

As companies continue to use crowdsourcing, demand for people who know how to manage projects remains steady

A little over four years ago, I attended the first Crowdsourcing meetup at the offices of Crowdflower (then called Dolores Labs). The crowdsourcing community has grown explosively since that initial gathering, and there are now conference tracks and conferences devoted to this important industry. At the recent CrowdConf1, I found a community of professionals who specialize in managing a wide array of crowdsourcing projects.

Data scientists were early users of crowdsourcing services. I personally am most familiar with a common use case – the use of crowdsourcing to create labeled data sets for training machine-learning models. But as straightforward as it sounds, using crowdsourcing to generate training sets can be tricky – fortunately there are excellent papers and talks on this topic. At the most basic level, before embarking on a crowdsourcing project you should go through a simple checklist (among other things, make sure you have enough scale to justify engaging with a provider).

Beyond building training sets for machine-learning, more recently crowdsourcing is being used to enhance the results of machine-learning models: in active learning, humans2 take care of uncertain cases, models handle the routine ones. The use of ReCAPTCHA to digitize books is an example of this approach. On the flip side, analytics are being used to predict the outcome of crowd-based initiatives: researchers developed models to predict the success of Kickstarter campaigns 4 hours after their launch.

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Design Thinking and Data Science

By Dean Malmgren and Jon Wettersten


Photo Courtesy of Jon Wettersten

There’s a lot of hype around “Big Data” these days. Don’t believe us? None other than the venerable Harvard Business Review named “data scientist” the “Sexiest Job of the 21st Century” only 13 years into it. Seriously. Some of these accolades are deserved. It’s decidedly cheaper to store data now than it is to analyze it, which is considerably different than 10 or 20 years ago. Other aspects, however, are less deserved. In isolation, big data and data scientists don’t hold some magic formula that’s going to save the world, radically transform businesses, or eliminate poverty. The act of solving problems is decidedly different than amassing a data set the size of 200 trillion Moby Dicks or setting a team of nerds loose on the data. Problem solving not only requires a high-level conceptual understanding of the challenge, but also a deep understanding of the nuances of a challenge, how those nuances affect businesses, governments, and societies, and—don’t forget—the creativity to address these challenges.


Photo Courtesy of Dean Malmgren

In our experience, solving problems with data necessitates a diversity of thought and an approach that balances number crunching with thoughtful design to solve targeted problems. Ironically, we don’t believe this means that it’s important to have an army of PhDs with deep knowledge on every topic under the sun. Rather, we find it’s important to have multi-disciplinary teams of curious, thoughtful, and motivated learners with a broad range of interests who aren’t afraid to immerse themselves in a totally ambiguous topic. With this common vision, IDEO and Datascope Analytics decided to embark on an experiment and integrate our teams to collaborate on a few big data projects over the last year. We thought we’d share a few things here we’ve learned along the way.

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