Big data, user data, real-time data: In the era of digital transformation, tech automation and cloud computing, data has become the unofficial currency of the business world. And there’s only one way to access all that data has to offer: data mining.
In the past few years, data mining — the process of collecting and analyzing data to identify patterns and correlations — has unlocked unprecedented business opportunities. Every industry has felt the impact of data mining in business, from banking and insurance to education and retail. Banks use data mining to detect fraudulent activity. Manufacturers use it to predict the lifespan of a product, anticipate maintenance and maximize production. Even schools are using data to predict student performance and develop intervention strategies in advance.
Of course, companies need data analysts to identify and take advantage of these opportunities, and demand for data experts has skyrocketed in recent years. Along with this growth, the data mining field has evolved rapidly. Today, data miners need an impressive skill set and a diverse knowledge base. But even more importantly, they need the ability to stay at the cutting edge of the industry.
To address these growing needs, some schools have made data mining skills a central part of business education. Programs like the University of Maryland’s Robert H. Smith School of Business Online Master of Science in Business Analytics (MSBA) are designed to provide students with the skills and industry expertise they need to thrive in the age of data.
The Basic Data Mining Toolkit
Data mining is a complex field that requires a diverse range of skills, including software, systems and data management. To succeed in the field, a capacity for critical and creative thinking is key — using resources and strategies innovatively can unlock valuable data patterns and insights. In addition, successful data mining requires mastery of many hard skills, from cutting-edge programming languages to technology resource management.
Python
Often referred to as the Swiss Army knife of the coding world, Python is a simple but versatile language. Its straightforward syntax results in readable, maintainable code, but its benefits go beyond ease alone. It’s an extremely adaptable language — equally useful for processing, analyzing and visualizing data — with almost endless applications. Many data analysts turn to Python for managing databases and developing regression models. Plus, it can be integrated into any existing infrastructure, which makes it useful in nearly every industry, from banking and sales to education and communication.
R and SQL
R and SQL are both popular programming languages among data miners. R was specifically made for statistical computing and is useful for storing data and accessing existing databases. In addition, it comes packaged with hundreds of libraries built specifically for data mining. SQL is the ideal counterpart to R, designed to request, extract, update and replace data in external databases. Combined, R and SQL provide an ideal environment for data mining, enabling businesses to seamlessly store, review and dissect data.
Quantitative Modeling
At its core, data mining is about extracting meaning from an otherwise unrelated group of data points. Quantitative modeling enables data analysts to do just that. Quantitative models are representations of data that identify, depict and even predict data patterns. By accounting for complex variables, these models essentially enable data analysts to “time travel,” shedding light on past revenue challenges and predicting future business opportunities. Nearly every aspect of a business can benefit from well-designed quantitative models, which makes expertise in quantitative modeling fundamentally important and highly attractive.
Infrastructure Management
Every organization needs to manage resources responsibly. In the data mining field, that means infrastructure management. Infrastructure managers ensure the overall effectiveness of data systems, equipment and processes. While they may not work with data directly, infrastructure managers need a solid understanding of data mining to maintain well-organized databases and support effective data mining teams.
Building Skills for Success in Data Mining
Data mining is a rapidly evolving field, which means developing the tools necessary for successful data mining in business requires innovative instruction. The Maryland Smith online master’s in business analytics program offers a cutting-edge curriculum designed to help data miners and analysts develop their expertise. Courses range from Data Mining and Predictive Analytics to Financial Accounting and Database Management Systems, including:
Big Data and Artificial Intelligence for Business
As digitization makes its way into more aspects of modern life, businesses are gaining access to massive amounts of data. While that data presents impressive business opportunities, many companies don’t know how to tap into it. This course prepares students to leverage big data to gain business insights, improve products and contribute to the bottom line. Throughout the course, students gain expertise in capturing, storing, managing and analyzing big data — and then using that end-to-end expertise to drive big data strategies.
Advanced Marketing Analytics
Data mining presents huge opportunities for product marketers. From strategic marketing and market segmentation to product development, sales promotion and sales force allocation, data can enhance nearly every aspect of marketing. In this course, students learn to acquire, analyze and interpret data to make strategic marketing decisions. Students not only explore the latest developments in data-driven marketing but also master advanced methods of market analysis, including choice and count data models, mixture and mixture regression models, and conjoint analysis using data-based cases.
Data Processing and Analysis in Python
Over the last few years, Python has consistently ranked as one of the most popular programming languages in the world. To be competitive in the field of data mining, analysts need a solid grasp of Python and its many applications. This class is designed to help students master basic Python functionality and apply hands-on expertise: writing code to process, analyze and visualize data.
Lead the Next Generation of Data Analysts
Data mining isn’t going anywhere. As more services, products and processes are digitized, data will become an increasingly fundamental part of business. The University of Maryland’s Robert H. Smith School of Business Online MSBA program is designed to hone your expertise in business analytics — expertise that can translate to stronger job security, higher pay and better leadership opportunities.
The MSBA challenges students to grow professionally and personally. With a diverse curriculum that includes coursework in everything from healthcare operations management to advanced marketing analytics, leadership and finance, the program lets students tailor their degree to fit their individual goals. The classes are completely online, so you can get a degree, prepare to advance your career and live your life at the same time.
Recommended Readings
What Is It Like to Be a Data Analyst?
The Benefits of Big Data: How Business Analytics Can Increase Inclusion
What Is a Management Analyst? Using Data Analytics to Improve Organizations
Sources
IBM, Structured Query Language (SQL)
Irish Tech News, “How Python Is Used In Data Science?”
Medium, “Python: 7 Important Reasons Why You Should Use Python”
University of Maryland, Online Master of Science in Business Analytics Curriculum