![]() ![]() You might be an applied machine learning/AI engineer if your response to “I bet you couldn’t build a model that passes testing at 99.99999% accuracy” is “Watch me.” With the coding chops to build both prototypes and production systems that work and the stubborn resilience to fail every hour for several years if that’s what it takes, machine learning specialists know that they won’t find the perfect solution in a textbook. Excellence in machine learning: performance In other words, they use data to minimize the chance that you’ll come to an unwise conclusion. The result? A perspective that helps leaders make important decisions in a risk-controlled manner. They care deeply about whether the methods applied are right for the problem and they agonize over which inferences are valid from the information at hand. To them, inferring something sloppily is a greater sin than leaving your mind a blank slate, so expect a good statistician to put the brakes on your exuberance. Statisticians are specialists in coming to conclusions beyond your data safely - they are your best protection against fooling yourself in an uncertain world. Doing so will help explain why analysts are valuable, and how organizations should use them. So, let’s examine what it means to be truly excellent in each of the data science disciplines, what value they bring, and which personality traits are required to survive each job. In data science, excellence in one area beats mediocrity in two. Instead of asking an analyst to develop their statistics or machine learning skills, consider encouraging them to seek the heights of their own discipline first. It’s dangerous to have them quit on you, but that’s exactly what they’ll do if you under-appreciate them. Far from being a lesser version of the other data science breeds, good analysts are a prerequisite for effectiveness in your data endeavors. They may use some of the same methods and equations, but that’s where the similarity ends. If your primary skill is analytics (or data-mining or business intelligence), chances are that your self-confidence has taken a beating as machine learning and statistics have become prized within companies, the job market, and the media.īut what the uninitiated rarely grasp is that the three professions under the data science umbrella are completely different from one another. What about analysts? Analytics as a second-class citizen Alternative challengers for the alpha spot come from statistics, thanks to a century-long reputation for rigor and mathematical superiority. Today’s fashion in data science favors flashy sophistication with a dash of sci-fi, making AI and machine learning the darlings of the job market. When teams can’t get their hands on a three-in-one polymath, they set their sights on luring the most impressive prize among the single-origin specialists. The top trophy hire in data science is elusive, and it’s no surprise: a “full-stack” data scientist has mastery of machine learning, statistics, and analytics.
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