As big data becomes a household name in problem solving, data mining and data science are also growing. Knowing the latest tips, tricks, and skills that make a successful data miner can offer a competitive edge in data analysis, whether through improving internal operations, adding value to ongoing projects, or simply gaining a deeper understanding of worldly trends.
Identifying the skills and tools for data mining
While you don’t need a post-professional degree to carry out data mining and analysis, you will certainly need a strong mathematical background to handle the statistical tools involved in the process. Over the last decade, the academic and professional worlds have honed their offerings in terms of data mining. Now world class institutions like MIT and Stanford offer classes, both free and premium, that offer cutting-edge insights into large data statistics, big data, and even specific data mining practices.
A strong background in statistics supplemented with insights, certificates, or classes that specialize in data mining is important. You’ll learn important practices that explain how to effectively find trends, the common industry-based software, and tools such as IBM’s SPSS Modeler or RapidMinder, and gain a general sense of the philosophies and approaches for sourcing data sets and carrying out efficient mining.
Above all, no amount of classroom experience can replace the value of critical thinking and industry knowledge. Searching for data patterns is useless without knowing how to identify the right goals and opportunities as you work with large swaths of data.
Realizing that data is everywhere
An important tenet of the modern data miner is the interest in tapping every potentially viable source of information, and figuring out how to exploit every available source of data within the limits of a given task. Understanding this is part of the general best practices that data mining teaches, and requires the patience to not become overburdened by the amount of available information.
This task of filtration is chiefly governed by the goals of your mining operation. It ultimately depends on the amount of time you or your team needs to devote to mining, the resources available in terms of computing power and software, and whether the goal is to answer a big data-esque question about general operations, to get specific insights for a project, or to carry out a request or need on behalf of a client or other project team.
Success depends on core project management values. Think of measurable, attainable, and capable opportunities, and use every available asset. Think creatively about how to find new, data sets that may add value to the information at hand and the patterns in your toolbox. As governments are becoming aware of the value in big data, many agencies are opening up databases or general spreadsheets with fresh, recent information on broad national statistics that can improve data finding.
The future of data mining
Of course, each data miner approaches each project differently, depending on their educational backgrounds, preferred tools, and interests. Trends such as automated mining are also influencing the ways that people source information, and will continue to affect the industry as they become cheaper and more accessible to small enterprises.
Ultimately, data mining depends on a strong core understanding of statistics, powerful tools, and useful data sets, offering valuable insights for your projects and goals.