Explore big data and how it works. Learn about big data's relationship to AI, data science, social media, and the Internet of Things (IoT).
- [Barton] 10 years ago, we were deep in the big data revolution when the volume, the velocity, and the variety of data completely overwhelmed the systems used to store, manipulate, and analyze that data. Now we're in the midst of an artificial intelligence revolution, but it's important to remember that big data hasn't gone away or become irrelevant. Rather, big data has become the new normal. It's everywhere and in fact, it's big data that makes artificial intelligence possible. I'm Barton Poulson and in this course we'll explore the ways that big data has developed in parallel with social media and the internet of things. We'll see how it's added unexpected value to enterprising analytics by improving business strategy and customer interactions. We'll introduce some of the common approaches to analyzing big data and evolving methods for implementing its insights. We'll see how big data relates to data science and machine learning and artificial intelligence. And we'll discuss important issues surrounding data governance and privacy, but this is not a technical discussion. We're not going to demonstrate code or walk through specific algorithms. Rather, this discussion focuses on the concepts that enliven big data. So, if you want to know how you can thrive in the world of big data, data science, machine learning, and artificial intelligence, then regardless of your technical background this course can give you a road map and a better understanding of how to draw on data to do the things that are important to you and to do them more effectively and more efficiently. And so with that in mind, let's get started with Big Data in the Age of AI.
- Identify the components that make up big data.
- Examine how big data has grown over the last few years.
- Explain the importance of using big data in business organizations.
- Distinguish between knowledge requirements for using big data and for understanding data science.
- Justify the need for training on big data within an organization.
- Analyze the factors that go into utilizing big data on a project.
- Differentiate outcomes that are derived from big data from outcomes that are derived from observing behaviors.