Since December 2013, online job postings for statisticians and data science professionals have risen by 256%, according to the Indeed Hiring Lab. These in-demand “super analysts” are able to do deep dives into quantitative analysis. In response to that trend, the Fox School of Business has developed a 30-credit Master of Science in Statistics and Data Science with full-time and part-time options.
With this new degree, the school will help students and future graduates take advantage of the increasing number of job opportunities in this emerging career area. The Fox MS in Statistics and Data Science is built for students who want to develop in-depth statistical knowledge. These lessons are paired with a modern and data-focused educational experience, with a curriculum full of opportunities to develop critical thinking skills in the context of real-world problems in the industry.
“The primary mission of this new program is to graduate students with the highest levels of quantitative understanding,” says Eric Eisenstein, director of marketing and supply chain management graduate programs. “We are preparing them for highly specialized positions in business.”
Courses will cover statistics, business intelligence, data analytics, data mining, big data engineering, program management, causal learning and discovery, and machine learning. As a result of the program, statistics and data science professionals will understand prescriptive, descriptive, and predictive analytics, and will be able to embrace these technologies as they evolve. They will learn how to use cutting-edge analytical tools and software, as well as enhance their communication skills in order to translate modern data into insights for decision makers.
Students also participate in a three-credit capstone course designed to integrate “real world” problems into the curriculum. During the project, organizations will explain a data-driven challenge and partner with a student who will identify, estimate, evaluate, and communicate analytic solutions. Students may work on problems associated with their job at their current employer, while others will work in teams as consultants to a sponsoring corporation.
“The curriculum and the professors teaching the courses will put the Fox School on the map for thought leadership and innovation within the field of data science,” says Eisenstein.
Scott Bruce, a PhD student at the Fox School of Business, publishes in top statistics research journal, Biometrics.
Researchers are up to their elbows in data. But new methods are needed to address the increasing size and complexity of modern data structures, especially those with time-varying dynamics.
“Frequently, with biomedical experiments, you have time series data from many different participants, along with other clinical and behavioral information for each,” explains Scott Bruce, a fifth-year PhD student in the Department of Statistical Science at the Fox School.
“However,” he continues, “much of the statistical literature focuses on the analysis of a single time series, so there is a need for new theory and methods that can analyze more complex data generated from these kinds of modern biomedical experiments.”
Bruce’s article on his recent research—under the advisement of Dr. Robert Krafty (University of Pittsburgh) and Dr. Cheng Yong Tang (Temple University)—was accepted for publication by the prestigious statistics journal, Biometrics (and appears online through “Biometrics Early View”). He has developed a new method called “conditional adaptive Bayesian spectrum analysis,” or CABS, which can be used to analyze associations between the dynamics of time series data and other data of interest.
The University of Pittsburgh’s AgeWise Caregiver Study, which examined the relationship between stress and sleep in older adults serving as the primary caregiver for an ill spouse, was the motivation for Bruce’s research. Study participants were monitored during a night of sleep to obtain their heart rate variability time series data, and they completed a questionnaire in order to formulate an individualized sleep score (the Pittsburg Sleep Quality Index).
The goal was to measure the association between physiological stress captured in the heart rate variability data and sleep quality measured by the PSQI score.
“This new method allows you to analyze these associations between the covariates and the time series,” explains Bruce. “It properly reflects the temporally-evolving nature of the relationship and helps us better understand dynamic biological processes. Researchers and practitioners who study time series data in conjunction with other types of data will be interested in this work.”
Bruce and his collaborators also developed user-friendly functions available in MATLAB that allow researchers and practitioners to use CABS in their own work. It’s available at the Biometrics website.
Learn more about Fox School Research.