What is the difference between data science, artificial intelligence and machine learning?

Data Science involves skills and concepts from several different areas, including statistics, machine learning, and visualization. A data scientist uses data to answer questions. For a really good description of data science.

Artificial Intelligence is a collection of problems and methods related to making computers behave intelligently and solve complex problems. There is a wide variety of problems to solve within AI. For example, teaching a computer to play poker, automatically translate natural language, or select a security strategy are all AI problems. Some problems are more abstract or theoretical, and some are focused on specific applications.

Machine Learning is a discipline within artificial intelligence that is focused on using data (or interactive experience) to build intelligent systems. Machine learning technologies are now used widely in many other fields, too, including cybersecurity, bioinformatics, natural language processing, computer vision, robotics, and more. One of the most basic machine learning tasks is building a classifier to automatically label objects. For example, a machine learning algorithm can find patterns in emails that differentiate spam from non-spam and use those patterns to predict if a new email is spam or not. "Deep learning" and "deep networks" have received a lot of attention lately, due to their big successes on image data, but the field is much broader than that.

In terms of industry jobs, data science is probably the most flexible and widely used set of skills. There's lots of data out there and lots of need to analyze it. Machine learning is a close second, and overlaps a lot with data science, but it tends to focus more on building sophisticated and accurate systems and less on exploring data. Other areas within artificial intelligence are also important, but they're not used as widely in industry.