Here are the best projects from BrainStation's Diploma graduates.
We’ve explored the transformative effect data is having on restaurants, sports, and the world more broadly as data seems to grow ever-more crucial in industry and our daily life. The 2.5 quintillion bytes of data created 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.
As the world takes more of an interest in data science, it’s understandable that there might be some confusion over terminology that’s often incorrectly used interchangeably. With that in mind, we took a closer look at the difference between data science and data mining.
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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.
The business and societal impacts of data science are vast, and as data-driven decision making becomes an increasingly urgent priority for smart companies – MIT research shows that companies that lead the way in the use of data-driven decision making were 6 percent more profitable than their competitors – 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.
A burgeoning belief in data science really is consistent across all types of businesses. Dresner’s 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).
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 “teach” computers how to find patterns in data and make predictions.
A Data Scientist’s work is varied, and two days could look very different. These are usually among a Data Scientist’s routine responsibilities:
- Researching your company and the industry more broadly to discover opportunities for growth, improved efficiency, or potential issues
- Defining relevant data sets and extracting data
- Cleaning data and ensuring its accuracy
- Creating algorithms to run automation tools
- Modeling and analyzing data for meaningful patterns
- Creating visualizations that are attractive and accessible for others in the organization
- Communicating findings to other stakeholders
BrainStation’s 2019 Digital Skills Survey found that Data Scientists primarily work on developing new ideas, products, and services, as opposed to other data professionals who focus more time on optimizing existing platforms. And Data Scientists are also unique among big-data professionals in that their most-used tool is Python.
Though data science is a broad field, its ultimate purpose is to use data to make better-informed decisions.
Where data science is a broad field, data mining describes an array of techniques within data science to extract information from a database that was otherwise obscure or unknown. Data mining is a step in the process known as “knowledge discovery in databases” or KDD, and like other forms of mining, it’s all about digging for something valuable.
Since data mining can be viewed as a subset of data science, there’s of course overlap; data mining also includes such steps as data cleaning, statistical analysis, and pattern recognition, as well as data visualization, machine learning, and data transformation.
Where data science, however, is a multidisciplinary area of scientific study, data mining is more concerned with the business process and, unlike machine learning, data mining is not purely concerned with algorithms. Another key difference is that data science deals with all kinds of data, where data mining primarily deals with structured data.
The goal of data mining is largely to take data from any number of sources and make it more usable, where data science has larger aims to build data-centric products and make data-driven business decisions.
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