One way to think about data science is as a wide and wonderful world, with myriad sub-genres encompassed within the wide umbrella it spreads. Another way to understand data science is as the meeting point at which machine learning tools, predictive analytics, and other forms of analysis converge to draw critical insights from vast oceans of raw data. Whichever way you look at it, you can see that data scientists work with many different processes to gain the insights and information they need to make all that data collection worthwhile. After all, just collecting data is useless unless you have the tools necessary to separate the wheat from the chaff and use that grain to nourish youryouryour decision-making as a business leader.
One of the processes that data scientists use to glean important insights and useful information from within the raw data at their disposal is machine learning. In other words, although data science uses machine learning, they differ. Machine learning is part of data science; it is just one aspect that allows data scientists to do their job effectively and well. Data science is the umbrella under which machine learning resides.
It’s an exciting time for data science. According to the , advancements in data analytics, machine learning, and business intelligence allow business users and vendors access to insight-driven power they didn’t believe possible just a few short years ago. So, with that in mind, what is machine learning, and how can it be used to drive your business to success?
Machine learning is what it sounds like—machines that can learn. Not in the way we may think about learning—a child learning their ABCs and much—more in the vein of learning from mistakes. For example, an algorithm can be trained to recognize certain patterns, images, and other information. The algorithms learn by being fed large amounts of data and undergoing training to learn from their mistakes. If the algorithms guess that an image of a dog is an image of a cat, it undergoes a correction until the level of precision is extremely high and reliable.
This is, of course, a very rudimentary example. Advanced algorithms can do a whole lot more than telling cats from dogs. These days, powerful data analytics solutions and business intelligence platforms can use real-time streaming data and immersive imaging to extract key insights from within the seas of data a company amasses daily. Based on historical data, advanced machine learning processes can use predictive analytics and statistical analysis to very nearly tell the future of a company’s outlook with some accuracy. Knowing what tomorrow may bring is a business users using machine learning as it works within business intelligence platforms.
As suggested above, machine learning and data science provide great power to any technology users who may choose to use them. That being said, even regular people who aren’t CEOs of major corporations already use machine learning daily, whether we know it or not. When Google Maps suggests a location you usually visit at a certain time, the algorithm learns your habits. When Netflix recommends a movie you may like, the machine is learning your tastes, and when Facebook recommends someone, you may know their algorithm is learning about your social circles.
Machine learning and data science are used in other ways—to recognize dangerous or copyrighted content being spread around the web, to help anticipate natural disasters so that governments can prepare ahead of time, and know completely.. With the whole wild and wonderful world of data, it’s exciting to think about what tomorrow may bring. Even with advanced predictive analytics, that’s one thing we can’t