"data science" entries
Stories from women who are making a big impact on the field of big data.
Through a series of 15 interviews with women across the data field, we’ve uncovered stories we think you’ll find and both interesting and inspiring. The interviews explore:
- Interviewees’ views about opportunities for women in the fields of science, technology, engineering, and math (STEM)
- Benefits of the data field as a career choice for women
- The changing attitudes of Millennials toward women working in data
- Remedies for continuing to close the gender gap in tech
Our findings reveal an important consensus among the women we interviewed — the role of female mentors and role models working in STEM is extremely important for opening up the pathway for more women to enter these fields. In fact, the impact that mentors have had on our interviewees has inspired many of them to serve as mentors to other female colleagues, and younger generations of girls, today. Read more…
Practical machine-learning applications and strategies from experts in active learning.
What do you call a practice that most data scientists have heard of, few have tried, and even fewer know how to do well? It turns out, no one is quite certain what to call it. In our latest free report Real-World Active Learning: Applications and Strategies for Human-in-the-Loop Machine Learning, we examine the relatively new field of “active learning” — also referred to as “human computation,” “human-machine hybrid systems,” and “human-in-the-loop machine learning.” Whatever you call it, the field is exploding with practical applications that are proving the efficiency of combining human and machine intelligence.
Learn from the expertsThrough in-depth interviews with experts in the field of active learning and crowdsource management, industry analyst Ted Cuzzillo reveals top tips and strategies for using short-term human intervention to actively improve machine models. As you’ll discover, the point at which a machine model fails is precisely where there’s an opportunity to insert — and benefit from — human judgment.
- When active learning works best
- How to manage crowdsource contributors (including expert-level contributors)
- Basic principles of labeling data
- Best practice methods for assessing labels
- When to skip the crowd and mine your own data
Explore real-world examples
This report gives you a behind-the-scenes look at how human-in-the-loop machine learning has helped improve the accuracy of Google Maps, match business listings at GoDaddy, rank top search results at Yahoo!, refer relevant job postings to people on LinkedIn, identify expert-level contributors using the Quizz recruitment method, and recommend women’s clothing based on customer and product data at Stitch Fix. Read more…
If what we are trying to build is artificial minds, intelligence might be the smaller, easier part.
When we talk about artificial intelligence, we often make an unexamined assumption: that intelligence, understood as rational thought, is the same thing as mind. We use metaphors like “the brain’s operating system” or “thinking machines,” without always noticing their implicit bias.
But if what we are trying to build is artificial minds, we need only look at a map of the brain to see that in the domain we’re tackling, intelligence might be the smaller, easier part.
Maybe that’s why we started with it.
After all, the rational part of our brain is a relatively recent add-on. Setting aside unconscious processes, most of our gray matter is devoted not to thinking, but to feeling.
There was a time when we deprecated this larger part of the mind, as something we should either ignore or, if it got unruly, control.
But now we understand that, as troublesome as they may sometimes be, emotions are essential to being fully conscious. For one thing, as neurologist Antonio Damasio has demonstrated, we need them in order to make decisions. A certain kind of brain damage leaves the intellect unharmed, but removes the emotions. People with this affliction tend to analyze options endlessly, never settling on a final choice. Read more…
Using fast, scalable relational databases to build event-oriented applications.
Modern organizations have started pushing their big data initiatives beyond historical analysis. Fast data creates big data, and applications are being developed that capture value, specifically real-time analytics, the moment fast data arrives. The need for real-time analysis of streaming data for real-time analytics, alerting, customer engagement or other on-the-spot decision-making, is converging on a layered software setup called the Lambda Architecture.
The Lambda Architecture, a collection of both big and fast data software components, is a software paradigm designed to capture value, specifically analytics, from not only historical data, but also from data that is streaming into the system.
In this article, I’ll explain the challenges that this architecture currently presents and explore some of the weaknesses. I’ll also discuss an alternative architecture using an in-memory database that can simplify and extend the capabilities of Lambda. Read more…
Learn how to manipulate data, and construct and evaluate models in Azure ML, using a complete data science example.
Deriving value from machine learning, however, is often impeded by complex technology deployments and long model-development cycles. Fortunately, machine learning and data science are undergoing democratization. Workflow environments make tools for building and evaluating sophisticated machine learning models accessible to a wider range of users. Cloud-based environments provide secure ubiquitous access to data storage and powerful data science tools.
To get you started creating and evaluating your own machine learning models, O’Reilly has commissioned a new report: “Data Science in the Cloud, with Azure Machine Learning and R.” We use an in-depth data science example — predicting bicycle rental demand — to show you how to perform basic data science tasks, including data management, data transformation, machine learning, and model evaluation in the Microsoft Azure Machine Learning cloud environment. Using a free-tier Azure ML account, example R scripts, and the data provided, the report provides hands-on experience with this practical data science example. Read more…
The O'Reilly Data Show Podcast: Carlos Guestrin on the early days of GraphLab and the evolution of GraphLab Create.
Editor’s note: Carlos Guestrin will be part of the team teaching Large-scale Machine Learning Day at Strata + Hadoop World in San Jose. Visit the Strata + Hadoop World website for more information on the program.
I only really started playing around with GraphLab when the companion project GraphChi came onto the scene. By then I’d heard from many avid users and admired how their user conference instantly became a popular San Francisco Bay Area data science event. For this podcast episode, I sat down with Carlos Guestrin, co-founder/CEO of Dato, a start-up launched by the creators of GraphLab. We talked about the early days of GraphLab, the evolution of GraphLab Create, and what’s he’s learned from starting a company.
MATLAB for graphs
Guestrin remains a professor of computer science at the University of Washington, and GraphLab originated when he was still a faculty member at Carnegie Mellon. GraphLab was built by avid MATLAB users who needed to do large scale graphical computations to demonstrate their research results. Guestrin shared some of the backstory:
“I was a professor at Carnegie Mellon for about eight years before I moved to Seattle. A couple of my students, Joey Gonzales and Yucheng Low were working on large scale distributed machine learning algorithms specially with things called graphical models. We tried to implement them to show off the theorems that we had proven. We tried to run those things on top of Hadoop and it was really slow. We ended up writing those algorithms on top of MPI which is a high performance computing library and it was just a pain. It took a long time and it was hard to reproduce the results and the impact it had on us is that writing papers became a pain. We wanted a system for my lab that allowed us to write more papers more quickly. That was the goal. In other words so they could implement this machine learning algorithms more easily, more quickly specifically on graph data which is what we focused on.”
For maximum business value, big data applications have to involve multiple Hadoop ecosystem components.
Data is deluging today’s enterprise organizations from ever-expanding sources and in ever-expanding formats. To gain insight from this valuable resource, organizations have been adopting Apache Hadoop with increasing momentum. Now, the most successful players in big data enterprise are no longer only utilizing Hadoop “core” (i.e., batch processing with MapReduce), but are moving toward analyzing and solving real-world problems using the broader set of tools in an enterprise data hub (often interactively) — including components such as Impala, Apache Spark, Apache Kafka, and Search. With this new focus on workload diversity comes an increased demand for developers who are well-versed in using a variety of components across the Hadoop ecosystem.
Due to the size and variety of the data we’re dealing with today, a single use case or tool — no matter how robust — can camouflage the full, game-changing potential of Hadoop in the enterprise. Rather, developing end-to-end applications that incorporate multiple tools from the Hadoop ecosystem, not just the Hadoop core, is the first step toward activating the disparate use cases and analytic capabilities of which an enterprise data hub is capable. Whereas MapReduce code primarily leverages Java skills, developers who want to work on full-scale big data engineering projects need to be able to work with multiple tools, often simultaneously. An authentic big data applications developer can ingest and transform data using Kite SDK, write SQL queries with Impala and Hive, and create an application GUI with Hue. Read more…