"data science" entries

2014 Data Science Salary Survey

Salary insights from more than 800 data professionals reveal a correlation to skills and tools.

Data is growing: Whether in terms of data-driven applications, the diversity of tools or the actual quantities of data we collect and process, the data space is characterized by expansion. The excitement around data has been tempered in some circles — the first two query completion suggestions for a Google search of “Is data science” are “dead” and “a fad” — but from a practitioner’s perspective, things are looking quite rosy.

In the results of this year’s O’Reilly Media Data Science Salary Survey, we found a median total salary of $98k ($144k for US respondents only). The 816 data professionals in the survey included engineers, analysts, entrepreneurs, and managers (although almost everyone had some technical component in their role).

Why the high salaries? While the demand for data applications has increased rapidly, the number of people who set up the systems and perform advanced analytics has increased much more slowly. Newer tools such as Hadoop and Spark should have even fewer expert users, and correspondingly we found that users of these tools have particularly high salaries. Read more…

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Fast data calls for new ways to manage its flow

Examples of multi-layer, three-tier data-processing architecture.

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Like CPU caches, which tend to be arranged in multiple levels, modern organizations direct their data into different data stores under the principle that a small amount is needed for real-time decisions and the rest for long-range business decisions. This article looks at options for data storage, focusing on one that’s particularly appropriate for the “fast data” scenario described in a recent O’Reilly report.

Many organizations deal with data on at least three levels:

  1. They need data at their fingertips, rather like a reference book you leave on your desk. Organizations use such data for things like determining which ad to display on a web page, what kind of deal to offer a visitor to their website, or what email message to suppress as spam. They store such data in memory, often in key/value stores that allow fast lookups. Flash is a second layer (slower than memory, but much cheaper), as I described in a recent article. John Piekos, vice president of engineering at VoltDB, which makes an in-memory database, says that this type of data storage is used in situations where delays of just 20 or 30 milliseconds mean lost business.
  2. For business intelligence, theses organizations use a traditional relational database or a more modern “big data” tool such as Hadoop or Spark. Although the use of a relational database for background processing is generally called online analytic processing (OLAP), it is nowhere near as online as the previous data used over a period of just milliseconds for real-time decisions.
  3. Some data is archived with no immediate use in mind. It can be compressed and perhaps even stored on magnetic tape.

For the new fast data tier, where performance is critical, techniques such as materialized views further improve responsiveness. According to Piekos, materialized views bypass a certain amount of database processing to cut milliseconds off of queries. Read more…

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Signals from Strata + Hadoop World in Barcelona 2014

From the Internet of Things to data-driven fashion, here are key insights from Strata + Hadoop World in Barcelona 2014.

Experts from across the big data world came together for Strata + Hadoop World in Barcelona 2014. We’ve gathered insights from the event below.

#IoTH: The Internet of Things and Humans

“If we could start over with these capabilities we have now, how would we do it differently?” Tim O’Reilly continues to explore data and the Internet of Things through the lens of human empowerment and the ability to “use technology to give people superpowers.”

Read more…

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The science of moving dots: the O’Reilly Data Show Podcast

Rajiv Maheswaran talks about the tools and techniques required to analyze new kinds of sports data.

Many data scientists are comfortable working with structured operational data and unstructured text. Newer techniques like deep learning have opened up data types like images, video, and audio.

Other common data sources are garnering attention. With the rise of mobile phones equipped with GPS, I’m meeting many more data scientists at start-ups and large companies who specialize in spatio-temporal pattern recognition. Analyzing “moving dots” requires specialized tools and techniques.

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A few months ago, I sat down with Rajiv Maheswaran founder and CEO of Second Spectrum, a company that applies analytics to sports tracking data. Maheswaran talked about this new kind of data and the challenge of finding patterns:

“It’s interesting because it’s a new type of data problem. Everybody knows that big data machine learning has done a lot of stuff in structured data, in photos, in translation for language, but moving dots is a very new kind of data where you haven’t figured out the right feature set to be able to find patterns from. There’s no language of moving dots, at least not that computers understand. People understand it very well, but there’s no computational language of moving dots that are interacting. We wanted to build that up, mostly because data about moving dots is very, very new. It’s only in the last five years, between phones and GPS and new tracking technologies, that moving data has actually emerged.”

Read more…

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The big data sweet spot: policy that balances benefits and risks

Deciding what data to collect is hard when consequences are unpredictable.

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A big reason why discussions of “big data” get complicated — and policy-makers resort to vague hand-waving, as in the well-known White House executive office report — is that its ripple effects travel fast and far. Your purchase, when recorded by a data broker, affects not only the the ads and deals they offer you in the future, but the ones they offer innumerable people around the country that share some demographic with you.

Policy-making might be simple if data collectors or governments could say, “We’ll collect certain kinds of data for certain purposes and no others” — but the impacts of data collection are rarely predictable. And if one did restrict big data that way, its value would be seriously reduced.

Follow my steps: big data privacy vs collection

Data collection will explode as we learn how to connect you to different places you’ve been by the way you walk or to recognize certain kinds of diseases by your breath.

When such data exhaust is being collected, you can’t evade consequences by paying cash and otherwise living off the grid. In fact, trying to do so may disadvantage you even more: people who lack access to sophisticated technologies leave fewer tracks and therefore may not be served by corporations or governments. Read more…

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The problem of managing schemas

Schemas inevitably will change — Apache Avro offers an elegant solution.

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When a team first starts to consider using Hadoop for data storage and processing, one of the first questions that comes up is: which file format should we use?

This is a reasonable question. HDFS, Hadoop’s data storage, is different from relational databases in that it does not impose any data format or schema. You can write any type of file to HDFS, and it’s up to you to process it later.

The usual first choice of file formats is either comma delimited text files, since these are easy to dump from many databases, or JSON format, often used for event data or data arriving from a REST API.

There are many benefits to this approach — text files are readable by humans and therefore easy to debug and troubleshoot. In addition, it is very easy to generate them from existing data sources and all applications in the Hadoop ecosystem will be able to process them. Read more…

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Big data’s big ideas

From cognitive augmentation to artificial intelligence, here's a look at the major forces shaping the data world.

Big data’s big ideas

Looking back at the evolution of our Strata events, and the data space in general, we marvel at the impressive data applications and tools now being employed by companies in many industries. Data is having an impact on business models and profitability. It’s hard to find a non-trivial application that doesn’t use data in a significant manner. Companies who use data and analytics to drive decision-making continue to outperform their peers.

Up until recently, access to big data tools and techniques required significant expertise. But tools have improved and communities have formed to share best practices. We’re particularly excited about solutions that target new data sets and data types. In an era when the requisite data skill sets cut across traditional disciplines, companies have also started to emphasize the importance of processes, culture, and people. Read more…

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Four short links: 22 October 2014

Four short links: 22 October 2014

Docker Patterns, Better Research, Streaming Framework, and Data Science Textbook

  1. Eight Docker Development Patterns (Vidar Hokstad) — patterns for creating repeatable builds that result in as-static-as-possible server environments.
  2. How to Make More Published Research True (PLOSmedicine) — overview of efforts, and research on those efforts, to raise the proportion of published research which is true.
  3. Gearpump — Intel’s “actor-driven streaming framework”, initial benchmarks shows that we can process 2 million messages/second (100 bytes per message) with latency around 30ms on a cluster of 4 nodes.
  4. Foundations of Data Science (PDF) — These notes are a first draft of a book being written by Hopcroft and Kannan [of Microsoft Research] and in many places are incomplete. However, the notes are in good enough shape to prepare lectures for a modern theoretical course in computer science.
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Streamlining feature engineering

Researchers and startups are building tools that enable feature discovery.

Why do data scientists spend so much time on data wrangling and data preparation? In many cases it’s because they want access to the best variables with which to build their models. These variables are known as features in machine-learning parlance. For many0 data applications, feature engineering and feature selection are just as (if not more important) than choice of algorithm:

Good features allow a simple model to beat a complex model.
(to paraphrase Alon Halevy, Peter Norvig, and Fernando Pereira)

The terminology can be a bit confusing, but to put things in context one can simplify the data science pipeline to highlight the importance of features:

Feature engineering and discovery pipeline

Feature Engineering or the Creation of New Features
A simple example to keep in mind is text mining. One starts with raw text (documents) and extracted features could be individual words or phrases. In this setting, a feature could indicate the frequency of a specific word or phrase. Features1 are then used to classify and cluster documents, or extract topics associated with the raw text. The process usually involves the creation2 of new features (feature engineering) and identifying the most essential ones (feature selection).

Read more…

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Four short links: 14 May 2014

Four short links: 14 May 2014

Problem Solving, Fashion Mining, Surprising Recommendations, and Migrating Engines

  1. Data Jujitsu — new O’Reilly Radar report by the wonderful DJ Patil about the exploration and problem solving part of data science. Me gusta.
  2. Style Finder: Fine-Grained Clothing Style Recognition and Retrieval (PDF) — eBay labs machine learning, featuring the wonderful phrase “Women’s Fashion: Coat dataset”.
  3. Amazon’s Drug Dealer Shopping List — reinforcing recommendations surface unexpected patterns …
  4. Migrating Virtual Machines from Amazon EC2 to Google Compute Engine — if you want the big players fighting for your business, you should ensure you have portability.
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