Today’s businesses need a culture of collaboration that empowers knowledge workers to glean cognitive insights from data that help transform and modernize operations. See how cloud-based platforms and solutions enable data scientists and other experts to exploit artificial intelligence, machine
Some organizations misunderstand the optimized way to use Hadoop and Spark together, primarily because of their complexity. But investing in both technologies enables a broad set of big data analytics and application development use cases. See what Niru Anisetti and Rohan Vaidyanathan have to say
Streaming analytics is giving developers the ability to accelerate the time to value provided by applications designed for the Internet of Things. Discover how IBM Streams 4.2 is connecting developers with the capabilities of advanced data analytics solutions, helping them keep pace with the ever-
Data science may be the en vogue profession descriptor for what many who work with statistics and statistical modeling do, but being a data scientist in this era requires a unique set of skills and experiences. See why data science may not be a crystal ball capable of predicting all events, but how
Data science seems to be experiencing a renaissance when it comes to advanced open source tools. Get a glimpse into creative application development with IPython Notebooks, Jupyter Notebooks, Apache Spark, the PixieDust open source library and more at IBM Insight at World of Watson 2016.
IBM Insight at World of Watson 2016 offers you opportunities to explore solutions to your most challenging problems, connect with data engineers and data scientists from other organizations and find out what’s new in streaming analytics. And in anticipation of the event, check out this overview to
IBM Insight at World of Watson 2016, 24–27 October 2016, at Mandalay Bay in Las Vegas, Nevada, is the only place to be for people who work with data. Take a look at this list of top-ten reasons you wont’ want to miss out on one of the most intriguing and innovative events of the year.
Advances in tools and the capability to work with cloud-based data sets are dramatically changing the nature of data science workloads. Take a look at one data scientist’s quest to learn more about performing data science analysis in the cloud.
A day in the life of data science professionals likely involves navigating the challenges and complexities of sourcing, preparing, modeling, developing and governing data, analytics tools and other assets in collaborative environments. Get a glimpse of the roles that compose data science teams and
Machine learning is finding its way into a variety of applications. Discover an open source machine learning platform that combines the data processing power of Spark with powerful machine learning algorithms courtesy of the H2O platform to tackle challenges technologists face when applying machine
Data science takes collaborate teams of data scientists engaging in productive, open data development initiatives that can ensure strong workflow, governance, security and management. See why open environments are revolutionizing the data science landscape.
An open ecosystem thrives on a mature core platform. It also depends on partnering arrangements that incentivize solution providers to continue developing standards-based interoperability around the shared environment. Take a deeper dive into recent announcements of new open ecosystem milestones
SPSS Modeler was the star of its own ring at IBM Insight 2015. Take a look at what attendees learned about the latest and greatest capabilities of Modeler to see how Modeler can help your organization find its place in the insight economy.