{"id":465,"date":"2021-07-29T16:04:58","date_gmt":"2021-07-29T16:04:58","guid":{"rendered":"https:\/\/bootstrap-it.com\/blog\/?p=465"},"modified":"2021-07-29T16:04:58","modified_gmt":"2021-07-29T16:04:58","slug":"data-analytics-machine-learning-and-artificial-intelligence-the-differences","status":"publish","type":"post","link":"https:\/\/bootstrap-it.com\/blog\/?p=465","title":{"rendered":"Data Analytics, Machine Learning, and Artificial Intelligence: the Differences"},"content":{"rendered":"
<\/a><\/a><\/a><\/a><\/a><\/div>\n

Because there is some overlap between them – and plenty of confusion – here’s a quick post to show you how data analytics, machine learning, and artificial intelligence fit into the larger scheme of things.<\/p>\n\n\n\n

First, though, let’s define data itself:<\/p>\n\n\n\n

Data is any digital information that is generated by, or used for, your compute operations. That will include log messages produced by a compute device, weather information relayed through remote sensors to a server, digital imaging files (like CT, tomography and ultrasound scans), and the numbers you enter into a spreadsheet. And everything in between.<\/p><\/blockquote>\n\n\n\n

Data analytics<\/strong> is the art (and science) of using programming tools or spreadsheets to parse large volumes of that data so it can be better understood and exploited. Typically, a data analyst will load a data set into an appropriate environment where it can be cleaned of unnecessary “noise”, shaped, visualized, and interpreted.<\/p>\n\n\n\n

I describe this process in all its gory detail in my “Teach Yourself Data Analytics in 30 Days” project<\/a>. But at its core, analytics is about understanding<\/em> the world as the data presents it.<\/p>\n\n\n\n

Machine learning<\/em> (ML), on the other hand, is about using some of that same data – although often in much larger quantities – to predict what the world is going to<\/em> look like. Like data analytics, machine learning looks for patterns in historical data sets. But rather than just trying to understand what happened, ML engineers seek to design (or “train”) software models to extend those historical patterns into the future.<\/p>\n\n\n\n

A successful ML model might accurately predict which products will most interest the customers on your website, for instance. This will allow you to individualize each user’s experience. As a (slightly) less creepy example, think how banks use ML models to flag suspicious transactions that suggest someone’s trying to illegally use your credit card.<\/p>\n\n\n\n

Finally, artificial intelligence<\/em> (AI) is a cluster of tools for combining data – and often ML models – with computers to enable real-time interaction with the physical world. The trick is to get AI to the point where its autonomous machine performance is as good or better than that of humans.<\/p>\n\n\n\n

The countless – and constantly expanding – range of AI applications include self-driving cars, digital assistants (like Alexa or Siri), and complex industrial control systems.<\/p>\n\n\n\n

That’ll hopefully get you started in the right direction. There’s plenty more “executive briefing”-level information like this available in my Keeping Up: Backgrounders to all the big technology trends you can\u2019t afford to ignore book<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Because there is some overlap between them – and plenty of confusion – here’s a quick post to show you how data analytics, machine learning, and artificial intelligence fit into the larger scheme of things. 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