It can be difficult to keep up with all the best podcast episodes during the year. That's why we've compiled the Top 10 podcasts of the year from the IBM Big Data & Analytics Hub Insights Podcast feed right here.
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
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
Experts share their on-the-ground impressions of the IBM Chief Data Officer Summit, the perception of the CDO role amongst audience participants, and discuss how data science and the chief data officer role interact.
Augmented reality will change the way we experience data. But, what will this new reality look and feel like, and how can we take advantage of it to make better decisions? Tune in to this episode of Making Data Simple to learn about augmented reality data visualization from Ben Resnick and Alfredo
What will happen to companies who don't embrace data? What do the next five years hold? What's the difference between AI and machine learning? Steve Ardire and Adam Gabriel tackle these questions and more during this special post-event Facebook Live session.
There’s no doubt data science and machine learning are main areas of focus for enterprises to better their business. However, talking about data science and machine learning isn’t the same as making it a reality.
In the connected world of today’s digital economy, apps, IoT devices, vehicles, appliances and servers are generating endless stream of event data. The stream of events describes what is happening over time and offers the opportunity to track and analyze things as they happen.
Smart companies are finding new ways to squeeze more value out of their massive data storehouses. They’re unlocking insights from their data that build new business models, improve customer experiences and outpace competitors. So where do these business-changing insights come from?
Many of today’s top business performers successfully leverage a discipline – data science. Machine learning is one major way to apply data science and with machine learning, the more data we feed in, the better it performs. However, much of the world’s value data cannot be found on the Internet. It