Getting the most from a data science agenda requires more than data scientists. At Think, you’ll learn to view data science as a team sport, involving multiple roles and appropriate tools that help organizations tap into the benefits data science can bring wherever the business opportunity is.
Technology trends and growth areas can vary in different parts of the world. In this week's podcast, Keichii Okada, vice president of IBM Tokyo Software & Systems Development Lab, discusses how natural language technology is helping to advance business strategy and healthcare in Japan. He also
Many companies are expected to pursue data management, advanced analytics and cognitive computing to stay competitive and drive revenue. Except for a handful of leaders such as LinkedIn, Netflix, Nordstrom, Target and Verizon, most companies are still struggling to close the gap between data
The search function is a very powerful tool, assuming you have concrete keywords or concepts to find in your data. And that does not even take into account the size of the information you might be searching.
Readers of the IBM Big Data & Analytics Hub were hungry for knowledge this year. They voraciously read blog posts about incorporating machine learning, choosing the best possible data model, determining how to make the most of data science skills, working with open source frameworks and more.
How do you choose and learn a new coding language? In this episode of Making Data Simple, we are joined by YouTuber and IBM Social Strategist, Caleb Curry. With over 5M view of his tutorials on YouTube, Caleb has learned a thing or two about how to teach and learn to program successfully. Discover
Many large organizations still have a large amounts of data on-premise, but also need data from a public cloud. Regardless of where the data resides, organizations can build a trusted data source from which they can drive key business insights and derive significant sustained advantages. Here's how.
Organizations everywhere, from massive governments to the smallest start-ups, are in a race for the best-possible data expertise and tools. To help your team understand the data science journey, IBM created the Data Science for All webcast.
Information analytics has never been a “one size fits all” proposition. That applies to the hardware and software technologies organizations employ, the information being parsed and the goals of specific projects.
What are your major concerns before flying on a trip? Would you ever give up your seat due to overbooking? How do airlines predict weather patterns and take proactive action to minimize delays? In this episode of Making Data Simple, Yianni Gamvros, Global Data Science Enablement Leader for IBM
Machine learning concerns in Silicon Valley tend to be different from those elsewhere in the U.S. — and outside of the U.S. So, here are five tips for those hearing about machine learning efforts in Silicon Valley, but who work elsewhere. These suggestions consider where machine learning and data