Master's Programs in Predictive Analytics

This article covers common courses and entrance requirements for predictive analytics master's degree programs. These programs provide students with the technical skills they need to make predictions about future economic and business trends.

Earning a Master's Degree in Predictive Analytics

A master's degree in predictive analytics prepares students to conduct research and make data-driven predictions for public, private, and nonprofit organizations. These programs generally take around two years to complete full time (or up to 5 years part time). Common core courses include the following.

Macroeconomics

In graduate-level macroeconomics courses, students become familiar with theories that attempt to explain economic growth and decline at a national or international scale. Students may study concepts like basic national income models, business cycles, income growth, inflation, and unemployment. They will likely also discuss and analyze public policies that affect the economy.

Microeconomics

In a microeconomics analysis or theory course, students analyze models of the firm in different types of markets. They become familiar with economic concepts like consumer behavior and demand, theory of production and distribution, factor markets, general equilibrium, and the effects of imperfect competition on economic agents. In many cases, students also work on learning mathematics skills (particularly calculus) that are used to help explain economic theories and concepts.

Econometrics

In this core course, students study the theory, methods, and application of econometrics. Students begin to apply mathematical tools from previous classes to real-world economic data sets. One of the main concepts students will likely play around with is regression analysis. Other topics may include least-squares estimation, hypothesis testing, and inference.

Forecasting

A forecasting class presents students with a variety of tools that economists can use to make accurate predictions of future economic and business trends. Students may spend time learning and analyzing the value of different forecasting methods. Topics may include ARMA models, seasonality, unit roots, and non-stationarity.

International Finance

Because there are so many applications for predictive analytics in the world of international finance, some predictive analytics programs include elective courses in topics like international trade and world markets. In these courses, students may learn about foreign exchange markets and the international equilibrium system. Students may also have the opportunity to analyze and evaluate international finance policy. To this end, they use quantitative methods to conduct their own research about important international trade issues (like tariffs, quotas, or voluntary export restraints). Students may also be challenged to consider alternative ways to organize the international finance system.

Data Modeling

In data modeling courses students learn how to design, validate, and deploy models to a variety of large data sets. Students may learn about general linear models that help analysts work with data sets that have missing values and outliers. They may also practice statistics and programming skills that allow them to develop their modeling techniques. Topics may include neural networks, smoothing methods, model selection, model evaluation, hybrid models, multiway analysis, and hierarchal models.

Database Management

In this course, students learn how data is organized, stored, represented, accessed, and processed. In particular, students learn about different methods for working with large-scale distributed data. Students may learn about different ways that database engineers store data, like open-source solutions, commercial solutions, distributed database systems solutions, and data warehousing. Topics may also include primitive databases, the relational model approach, network and object-oriented models, programming in SQL and Oracle (data management programming languages), and security and ethics issues for data managers.

Entrance Requirements for a Master's Degree Program in Predictive Analytics

Students who wish to pursue a master's degree in predictive analytics must have a bachelor's degree in any subject. Some programs require students to have completed undergraduate coursework in basic economic theory, statistics, and/or calculus. Most programs require applicants to report their undergraduate GPA (competitive programs require a GPA of 3.0 and above) and send in their GRE test scores (although testing requirements may be waived for students who already hold a graduate degree in a related field or have completed an undergraduate STEM major). Other common application requirements include a non-refundable application fee, official transcripts, letters of recommendation from previous professors, a current CV or resume, and a statement of purpose essay. International students from non-English speaking countries may also need to send in TOEFL test scores.

A predictive analytics master's degree program includes courses in economic theory and analysis, mathematical tools and techniques, and technical skills for working with data. These 2-year programs provide graduates with a 21st century skill set that they can apply in a range of industries.

To continue researching, browse degree options below for course curriculum, prerequisites and financial aid information. Or, learn more about the subject by reading the related articles below:

The schools in the listing below are not free and may include sponsored content but are popular choices among our users. Tuition and costs will vary across programs and locations. Be sure to always request tuition information before starting a program.

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