{"id":9948,"date":"2019-10-29T13:46:56","date_gmt":"2019-10-29T17:46:56","guid":{"rendered":"https:\/\/blog.brainstation.io\/?p=9948"},"modified":"2020-12-11T17:26:22","modified_gmt":"2020-12-11T22:26:22","slug":"data-science-vs-data-mining","status":"publish","type":"post","link":"https:\/\/brainstation.io\/blog\/data-science-vs-data-mining","title":{"rendered":"Data Science vs. Data Mining"},"content":{"rendered":"

We\u2019ve explored the transformative effect data is having on <\/span>restaurants<\/span><\/a>, <\/span>sports<\/span><\/a>, and the <\/span>world more broadly<\/span><\/a> as data seems to grow ever-more crucial in industry and our daily life. The\u00a02.5 quintillion bytes of data\u00a0created daily will only increase, and as that volume grows, companies are wisely investing more and more in data science: Dresner Advisory Services reports that data science adoption in enterprises has increased from 17 percent to 59 percent.<\/span><\/p>\n

As the world takes more of an interest in data science, it\u2019s understandable that there might be some confusion over terminology that\u2019s often incorrectly used interchangeably. With that in mind, we took a closer look at the difference between data science and data mining.<\/span>\u00a0<\/b><\/p>\n

Data Science<\/b><\/h2>\n

Data science is a field that uses math and technology to find otherwise invisible patterns in the massive volumes of raw data that we are increasingly generating. With the goal of making accurate predictions and smart decisions, data science allows us to find otherwise imperceptible insights hiding in plain sight in those troves of data.<\/span><\/p>\n

The business and societal impacts of data science are vast, and as data-driven decision making becomes an increasingly urgent priority for smart companies \u2013 MIT <\/span>research<\/span> shows that companies that lead the way in the use of data-driven decision making were 6 percent more profitable than their competitors \u2013 the field of data science is influencing and changing how we view marketing best practices, consumer behavior, operational issues, supply-chain cycles, corporate communication, and predictive analyses.<\/span><\/p>\n

A burgeoning belief in data science really is consistent across all types of businesses. Dresner\u2019s study found that the industries leading the way for big-data investment include telecommunications (95 percent adoption), insurance (83 percent), advertising (77 percent), financial services (71 percent) and healthcare (64 percent).<\/span><\/p>\n

Data science is a broad field, spanning predictive causal analytics (or forecasting the possibilities of a future event), prescriptive analytics (which looks at a range of actions and the related outcomes) and machine learning, which describes the process of using algorithms to \u201cteach\u201d computers how to find patterns in data and make predictions.<\/span><\/p>\n

A Data Scientist\u2019s work is varied, and two days could look very different. These are usually among a Data Scientist\u2019s routine responsibilities:<\/span><\/p>\n