Continuing Education and Graduate School for Big Data Jobs
Big data is an emerging field so you may not have considered it an option when you were in college. You may not have known exactly what you wanted to do when you were an undergrad. You got a degree in something you were passionate about, but it may not have been all that marketable, so you chose to pursue a graduate degree.
Maybe you have a passion for educational pursuits and research and you want to gain deep understanding in a field, so you find yourself considering a PhD. Perhaps you have your undergraduate degree completed, and you find yourself wanting to specialize in big data but you don’t have the time (or energy) to go back to school full time. Yet you see the value in a formal, degreed education.
Education, like any other product, is demand driven. To that end, you can find a growing number of master’s degrees in data and analytics and a growing number of people pursuing research in the same field.
Programs in analytics
Maybe you’re an old dog that needs to learn some new tricks. That’s okay. In fact, there are more programs in analytics geared toward working professionals than ever before. Every year universities are adding programs that sound something like master’s in data analytics, master’s in information systems management, or master’s in data science. These programs are not hard to find and range in price from $30,000 to more than $60,000.
The format of these programs tends to follow the executive style or online only. Executive formats are for people who need to maintain their full-time day jobs; classes are offered at night or on the weekends. Similarly, online degrees can often happen in the evenings or at the student’s own pace.
In the executive format, the entire program can range from one to two years, with class time ranging from once a month to two times a month. A class is usually completed in an entire weekend, instead of over the course of a semester, like in a traditional format. Sometimes they require a residency for extended periods. With the market success of the executive MBA, the master’s in data science is following suit. Top university brands are moving in this direction.
So, what does a master’s in data analytics look like? Take a look at a very typical plan. This one happens to be from Virginia Commonwealth University, but most schools follow something similar.
Prerequisites and foundation courses
These courses give you a sound foundation in business. You can come into this program without having an undergraduate degree in business. You have the chance to build upon basic business courses.
Calculus: This is the first level of advanced math. It studies change.
Statistical elements of quantitative management: This course uses statistics as a means of analysis and decision management.
Fundamentals of accounting: This is a foundational course in financial accounting for business.
Management theory: This class covers topics of organizational behavior and leadership concepts.
Financial concepts of management: This course is about understanding key ideas for running global firms, including working capital management, capital budgeting, capital structure planning, and dividend policy.
Concepts and issues in marketing: This foundational course is designed for graduate students with little or no undergraduate education in marketing. It’s a study of the philosophy, environment, and practice of contemporary marketing.
The following classes provide the basic skills and tools of the trade. They’re both theory and hands-on oriented.
Business intelligence: This class provides students with techniques and practices for modern decision-making in support of business/corporate performance.
Statistical analysis: This class is an introduction to probability, descriptive statistics, and data analysis. It explores randomness, data representation, and modeling.
Management science: This course gives students experience in the use of operations research techniques for solving organizational problems through the analyses of cases and management simulations.
Stochastic simulation: In this course, students develop skills related to the application of probabilistic models in real-world situations.
Electives allow graduate students to explore specific areas that are of interest to them. They aren’t typically required, and at least in this program, you only have to take two for graduation. Here are the electives this school offers:
Data mining: Students learn how to extract complex information from datasets with modern-day data-mining tools and techniques.
Applied multivariable methods: This class teaches statistical methods for answering complex business problems using methods like factor analysis and cluster analysis.
Forecasting methods: Big data and predictive analytics require techniques to predict future patterns, behaviors, or outcomes. This class teaches common methods, as well as the tools to do this.
Quality management and Six Sigma: Total quality management and Six Sigma strive to use a data-driven approach to gain efficiency and reduce errors in processes. This class covers the foundations of these subjects.
PhD programs for big data
So, you want people to call you “doctor”? Then get ready to master a very specific set of topics. Getting a PhD requires a lot of focus over a long period of time. That may sound boring if you’re ready to go out there, find a job, make money, and change the world. But getting a PhD in math and statistics can allow you to do all those things, too.
Most hard-core data scientists, Wall Street mathematicians called “quants,” and researchers in this field have PhDs in math or statistics. A PhD usually takes up to six years to complete. In the first half of your journey, you take foundational courses in how to conduct scholarly research, as well as advanced courses in your field.
The latter part of the process is focused on writing a dissertation and defending that body of work to leaders in your field. Throughout the process, you spend much of your time writing, researching, and teaching. Lather, rinse, and repeat until you finish.