article thumbnail

Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

article thumbnail

An agile approach to Data Science

OpenText Information Management

A notable exception is OpenText™ Magellan™ and our Data Science projects. In these cases, customers can expect an approach which simply adds refinement iterations to the build phase or … The post An agile approach to Data Science appeared first on OpenText Blogs.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

article thumbnail

An agile approach to Data Science

OpenText Information Management

Taking an agile approach to data science helps deal with rapidly changing environments, uncertainty, complex solutions, emerging technologies, and ambiguous requirements inherent in these projects.

article thumbnail

MLOps and the evolution of data science

IBM Big Data Hub

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

article thumbnail

Artificial Intelligence: 6 Step Solution Decomposition Process

Bill Schmarzo - Dell EMC

The conversation is simple because the objective is simple: How do I become more effective at leveraging (big) data and analytics (artificial intelligence) to power my business? Artificial Intelligence Solution Decomposition Process. Figure 1: The Evolution of AI, ML and DL (Source: Nvidia ).

article thumbnail

The Consumerization of Artificial Intelligence

Bill Schmarzo - Dell EMC

Whether we know it or not, we have all become “Citizens of Data Science,” and the world will never be the same. Apple Core ML in the iPhone is an example of how industry leaders are seamlessly embedding powerful machine learning, deep learning, and artificial intelligence frameworks into their development and operating platforms.