How to become a Data Mining Specialist – A complete career guide
A data mining specialist finds the hidden information in vast stores of data, decides the value and meaning of this information, and understands how it relates to the organization. Data mining specialists use statistical software in order to analyze data and develop business solutions. Thus, data mining specialists must both have a mastery of technological skills, especially programming software, and business intelligence.
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|School Name||Level||Program||More Info|
|Georgetown University||Master||Master of Science in Business Analytics||website|
|Johns Hopkins University||Master||Online MS in Data Analytics and Policy||website|
|Utica College||Master||Online MS in Data Science||website|
|Husson University||Bachelor||B.S. in Data Analytics||website|
|Capella University||Bachelor||B.S. in Information Technology||website|
|Southern New Hampshire University||Bachelor||B.S. in Data Analytics||website|
|George Mason University||Master||Online MS in Data Analytics Engineering||website|
|Drake University||Master||Master of Science in Business Analytics||website|
|Saint Joseph’s University||Master||Master of Science in Business Intelligence and Analytics||website|
Four steps to launching a successful data mining specialist Career:
Step 1: Earn your undergraduate degree
Data mining specialists need a strong background in data science, as well as business administration. Relevant undergraduate degrees include computer science, data science, information systems, statistics, and business administration, or any related fields. You’ll need to understand how to use statistical methods to analyze data, and you’ll want to be able to develop predictive models. Data mining specialists must be able to apply data analysis to real-world business issues, and thus coursework in developing business intelligence is excellent preparation.
Step 2: Gain employment as a data analyst
The world of data science offers many avenues into more advanced positions. If possible, you’ll want to get an entry-level job while still in college, especially in IT. Once out of college, look for positions as a data analyst. This will let you hone your technical skills further and develop an all-round understanding of the process of data extraction, transformation, and loading. You’ll want to have a firm grasp on database design as well.
Step 3: Pursue an advanced degree in data science
While not all universities offer specific coursework in data science, there are now many cutting-edge master’s and doctoral programs in data science. Obtaining an advanced degree will likely have a positive effect on your salary, as well as keep you at the forefront of new technologies. Regardless of the degree you hold, you’ll need to continue pursuing classes in data science advancements for the entirety of your career.
Step 4: Get hired as a data mining specialist
You can find positions as a data mining specialist in many different industries. You may want to begin your career as a data mining specialist with a company that provides opportunities to contribute to a team working at the forefront of data science. Software corporations and computer manufacturers are example industries where you are likely to find this type of opportunity.
What is a data mining specialist?
The data mining specialist must find patterns and relationships within large amounts of data in order to make predictions about the future and advise a business about strategy. A data mining specialist is able to turn the information he or she finds into actionable insights. These insights could be used for a number of purposes: minimizing risk and costs, increasing revenue, discovering new markets, and understanding consumer behavior better to produce tailored marketing campaigns, to name just a few. The data mining specialist knows which questions to ask of the data, and understands the difference between correlation and causation. A data mining specialist must also be able to identify outliers and anomalies in the data.
What does a data mining specialist do?
Data mining specialists have a number of tasks within an organization. The data mining specialist uses data analysis programs to research, mine data, model relationships, and then report these findings to the client using data visualization techniques, such as graphs, bar charts, scatterplots, and so on.
Data mining specialists work with three types of data: transactional, non-operational, and metadata. Transactional data is the kind that is produced on a daily basis in sales, for example, or data created in client visits, as well as inventory and operating costs. Non-operational data involves the data produced by an industry that can be mined for insights for a particular company, and is often forecast. Metadata concerns the design of the database that holds this other data. Data mining specialists need to be comfortable with the different uses and advantages that mining each of these sources allows.
Data Mining Specialist job description
- Store and manage data in multidimensional databases
- Collect data and analyze for trends, patterns, summaries, and meaning
- Determine reasons behind a business’s past successes or failures via data analysis
- Use statistical techniques and programming software to create algorithms and predictive models
- Create visualizations of data to report to shareholders in the form of reports or presentations
- Predict future trends within the organization and in the industry as a whole
- Produce actionable business insights, such as strategic recommendations
- Identify new markets and ways to increase productivity, or other improvements
- Train internal staff in technical processes, and provide mentoring to staff
Skills needed to become a Data Mining Specialist
A data mining specialist needs a unique combination of technological, business, and interpersonal skills. The technical skills that a data mining specialist must master include the following:
- Familiarity with data analysis tools, especially SQL, NoSQL, SAS, and Hadoop
- Strength with the programming languages of Java, Python, and Perl
- Experience with operating systems, especially LINUX
In order to make use of the patterns that a data mining specialist finds in an organization’s data, he or she must have keen business sense. Data analysis is nothing without a clear view of the business’s model and aims for the future. Data mining specialists thus must understand their own organization’s goals, as well as have knowledge of industry trends and best practices.
The data mining specialist must then be able to translate technical findings into presentations that non-technical colleagues can understand. Therefore, the data mining specialist should have strong public-speaking skills and the ability to communicate results to internal and external shareholders.
Data Mining Specialist salary
According to payscale.com (2020) “a Data Mining Specialist earns an average salary of $62,225 per year.” People generally move on to other job titles within 20 years, though salary does increase with experience. The best-paid data mining specialists have strong skills in SAS and SQL.
Data Mining Specialist job outlook
The demand for data mining specialists is expected to grow substantially—20% in the next 5 years. This trend is likely to continue as more and more companies in a wide variety of fields turn to data to increase sales and profits, reduce inefficiencies, and compete in a more technologically advanced society. The data mining specialist is an essential member of the data science team, and thus this position is likely to be valued much more in the years to come at companies of all sizes.
The term “data mining” was coined in the 1990s, though the practice of looking through data to make decisions has been in use for a much longer time. However, the confluence of statistics, artificial intelligence, machine learning, and the ability to store vast amounts of data has advanced the study of data science exponentially. Data mining specialists are now able to search extremely complex data sets, which are then able to produce relevant insights that would have otherwise been hidden. Organizations in the fields of healthcare, finance, criminal justice, education, retail, manufacturers, telecommunications, and insurance all find ways now to optimize their practices through the analysis of data.