Online Master of Science in Predictive Analytics (MSPA)
This article covers common courses and entrance requirements for online Master of Science in Predictive Analytics (MSPA) programs. Within these programs, students study technical skills, mathematical theory, and business practice.
Earning Master's Programs in Predictive Analytics
An online Master of Science degree in Predictive Analytics (MSPA) prepares graduates to use mathematics, computer programming, and business skills to make data-driven predictions. Students can complete these 1-2 year programs anywhere they have an internet connection. Most programs will require a bachelor's degree and some may require specific coursework. Common courses include regression analysis, data management and predictive analysis.
This interdisciplinary course introduces the blend of business strategy, mathematical modeling, and statistical techniques that are at the heart of predictive analytics. Students will likely learn about various applications for data science across business, non-profit and governmental organizations. Through case studies and group work, they may discuss both opportunities and issues in the field. Topics may include ethics, regulation and laws in data management. There may also be units on data selection tools, statistical software, data mining, data processing, decision-making techniques, and approaches to business research.
The mathematical basis for almost all predictive modeling is a statistics concept known as regression analysis. In this course, students learn about regression and multivariate regression analysis. They'll get a chance to practice using statistics software, like SAS, to work on real-world problems. Topics may include linear regression, stepwise regression, logistic regression, automated variable selection, simple and multiple least squares, curvilinear and piecewise models, and cluster analysis.
The goal for predictive analysis students is to be able to use statistics theory and software to make predictions about economic and business trends. To this end, students in this course pull together their newfound mathematical toolkit and programming skills to work on real-world data. Students will likely learn about various analytical techniques for analyzing time-series data. The course may also include topics like exponential smoothing methods, autocorrelation functions, ARIMA models, seasonal models, regression models, the Box-Jenkins methodology, intervention analysis and multivariate time series analysis.
Predictive analysts must know how to harness massive sets of raw data, organize them, transform them, and use them to make predictions. Before they can do that, they must understand how data is organized and stored. In this class, students may work with various database management systems (like relation SQL database systems and NoSQL database systems). They learn how to locate, mine, clean, prepare and make use of stored data. To this end, students will likely work with programming languages, like Python and R.
In a data modeling course, students learn how to build mathematical models to answer real-world business questions. To do this, students may need to brush up their skills in mathematical theory, linear programming, probability, and calculus. Students may work with computer programs (and programming languages) to apply mathematical concepts in their own data modeling projects. The course may also cover topics like multiway analysis, hybrid models, and neural networks.
Predictive analysts must apply their technical skills in real-world organizations and teams. This course equips students to become business leaders by exploring various leadership styles and techniques. Students may also practice traditional project management methods, like chartering, scope definition, estimating, the Delphi method, precedence diagramming, risk analysis, Gantt charts, and principles of negotiation. In some cases, students may work in groups to discuss case studies and practice self-assessments of their managerial style, strengths, and weaknesses. Within groups, they may evaluate problems and solutions for project managers in various case studies.
Entrance Requirements for a Master's Degree in Predictive Analytics
To apply to a Master of Science in Predictive Analytics, students must have a bachelor's degree. Some programs have a minimum GPA requirement (such as 3.0). Other programs allow students with a high GPA to waive the test score requirement (GMAT or GRE). While students can apply with a bachelor's degree in any field, some universities recommend or require students to have completed prerequisite coursework in calculus, linear algebra, and/or basic economic theory. To apply, students send in their transcripts, letters of recommendation from previous professors, a current resume (or CV), and a statement of purpose essay. In some cases, universities will require students to complete an interview with an admissions counselor as part of the admissions process.
An online Master's of Science in Predictive Analytics (MSPA) program prepares graduates to create models and make data-based predictions in a variety of business, nonprofit and government contexts. Students can complete these flexible programs 100% online, so they can further their education while maintaining a full-time job.