The combination of Jupyter Notebooks, Apache Hadoop and Apache Spark has become a killer app for data practitioners. It unlocks the ability to explore, visualize and experiment with both structured and unstructured data sets with great ease and efficiency. We spoke recently with Chris Snow at IBM
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
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.
The concluding week of September 2016 offered much excitement in New York City, the backdrop for Strata + Hadoop World 2016 and several key IBM announcements, including the launch of a cloud-based, self-service environment for data science teams. Enjoy some key highlights captured from this
Many marketing concerns have seen the light when it comes to the application of big data analysis as a means of outthinking the competition. Discover three best practices for implementing big data analytics for good data science in marketing initiatives.
IBM Insight at World of Watson 2016 has oodles of opportunities for data engineers to enrich their skill sets with a bevy of best practices, peers to network with, pointers and tips to discover, sessions to attend and more. Consider five key reasons to get the green light from your organization to
Open data science initiatives can be a revolutionary force for innovation that spans diverse industries. And that force comes from the people in different roles and with various skill sets who use open source data science tools to develop and deploy new designs for working and living. Discover why
The productivity of data science teams—often challenged by access and formatting minutiae—can be enhanced by automating many of the manual tasks these teams need to process. Take a peek inside the mind of a data scientist, and see how acceleration of the data science development pipeline can boost
The importance of data science expertise, techniques and tools in a world rapidly employing advanced cognitive systems cannot be understated. Learn more about how business analysts, data scientists, data engineers, application developers and other professionals with analytical skills sets are using
As a working data scientist, you must deliver on your projects while at the same time staying up to speed on changes in your chosen field. That’s a tough balance, considering how stretched you already on the job and how quickly the world of data science is evolving. That’s where IBM World of Watson
Many organizations can capitalize on big data solutions and technologies to make use of expanded volumes of data for enhancing the critical decisions that drive successful business outcomes. And yet, a number of these enterprises can be inhibited from moving big data initiatives forward for a
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
Now introducing the “Insight Ops” model. This new model will embrace and enable an agile environment for discovery and exploration and manage the transition necessary to deploy the insight to make it actionable.