To ensure data science success, you need to provide data scientists with an environment that is open, engaging, and fosters collaboration. To explore how your data scientists can access all the open functionality and expertise they’ll need for critical projects, join the new Data Science Experience
In this video, listen as IBM data science evangelist James Kobielus talks with Dean Wampler, a fast data product architect with the office of the CTO at Lightbend, about how data scientists can access the open functionality and expertise that are central to their work.
Are you a big data and analytics subject-matter expert? Do you enjoy writing? Would you like to be published? Check out IBM Press and the great opportunity to be a big data and analytics author. Share your expertise with readers from customer and partner organizations, colleagues and the greater
Deriving actionable insight from data and analytics is shifting to unified, cloud-based platforms that can be used by a variety of analysis personas. Take a look at a national retail chain scenario demonstrating how a comprehensive portfolio of end-to-end analytics in the cloud can provide the
Reimagine the data science experience as an open experience with this IDE, which aims to facilitate a full range of development tasks, from data acquisition and data mining to prototyping and programming. When you do, discover how you can use Apache Spark and R to pursue open analytics by building
A world that grows increasingly complex calls for disruptive innovation in an open, collaborative environment. See how open data science provides an ecosystem of expertise, skill sets and advanced open source data science tools that fuels collaborative creativity in the development and deployment
Use open-source tools to supercharge the data science lifecycle, giving data science teams a boost as they work to provide compelling results in the complex team environments that mark modern corporations. Learn how you can make open data science an ongoing part of your business environment when
Whether organizations want to extract customer data beyond names and addresses from unstructured data sources; pull specific dates, times or monetary amounts; predict trends from sentiment data; or engage in many other uses, text analytics is the way to go. Learn the details of text analytics, and
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.
As Spark continues to mature into mainstream adoption in the data science community, the open data analytics stack and open source tools grow more robust, giving data scientists rich core workbenches to develop evermore innovative applications.
With BigInsights having established itself as a leader and with IBM focused on a Cloud First Strategy, we saw the opportunity to help customers reduce these capital and management costs, to enable them to focus on running the analytics for business advantage while providing BigInsights on a dynamic
A growing number of businesses and industries are finding innovative ways to apply graph analytics to a variety of use-case scenarios because it affords a unique perspective on the analysis of networked entities and their relationships. Gain an understanding of how four different types of graph