Statistics and Data Science B.S.

The B.S. in Statistics and Data Science prepares students for a rewarding career as a data scientist or statistician. Organizations in all fields utilize large data sets to help them make important decisions. For example, pharmaceutical companies analyze data in the process of developing and testing new drugs, retailers analyze consumer spending patterns to decide what products to sell and to better target their marketing efforts, and pro sports teams use predictive analytics to evaluate players’ value and optimize their performance.

Students in the major are given the tools to not only efficiently extract and process large quantities of data but also to perform sophisticated statistical analysis. Graduates go on to work in business, sports, health, government, weather, space, and social sciences. Jobs include statistician and data scientist.

Contact Information

David Hudak, Ph.D.
Department Head, Mathematics
hudak@rmu.edu
412-397-4056 phone

4-Year Course Plan

Freshman Year, Fall

• MATH 2070 Calculus I
• CSEN/CSCM
• RMU Core (INFS 1020)
• ECON 1020 Macroeconomics
• RMU CORE

Freshman Year, Spring

• MATH 2170 Calculus II
• CSEN/CSCM
• ASCI 2010 Fundamentals of Actuarial Science
• MATH3400 Linear Algebra
• ECON 1030 Microeconomics

Sophomore Year, Fall

• ASCI 4140 Actuarial Data Analysis
• CSEN/CSCM
• STAT 3140 Probability/Statistics I
• STAT4250 Regression
• MATH 3090 Calculus III

Sophomore Year, Spring

• RMU Core
• CSEN/CSCM
• INFS Programming Course
• STAT4230 Time Series
• STAT 3150 Probability/Statistics II

Junior Year, Fall

• ASCI 4200 Statistical Modeling I
• INFS 4220 Data Mining Applications
• MATH 4200 – Stochastic Processes
• RMU CORE
• ELECTIVE

Junior Year, Spring

• STAT4350 Predictive Analytics
• RMU CORE
• ELECTIVE
• ELECTIVE
• ACCT 2030 Introduction to Financial Accounting

Senior Year, Fall

• STAT4300 Health Care Analytics
• RMU CORE
• INFS 4240 Database Management
• FINA 3000 Principles of Finance
• ELECTIVE

Senior Year, Spring

• ELECTIVE
• RMU CORE
• INFS 4260 Data Integration for Analytics
• FINA 3200 Corporate Finance

Additional Program Information

What is Predictive Analytics?

Technology has made it possible for companies in many industries to gather and store vast quantities of information and data related to many variables of interest. Predictive analytics makes use of these large data sets to help draw conclusions about recent activity, to learn about the relationships between variables, to cluster similar data points into groups, or to make predictions about the future. This is done using sophisticated statistical methodologies and algorithms to classify data points, determine if patterns exist, and gain insights and understanding about the various variables in the data set. Companies can apply these insights to improve their procedures and decision-making processes. Predictive analytics is also used in the analysis of defensive weapon systems, discovery of new energy sources, improvement of inventory tracking systems, and enhancement of air traffic control systems. This is only a sample of the increasing usage of predictive analytics in growing areas.

The RMU Advantage

• The RMU statistics and predictive analytics provides students with the ability to analyze large sets of data in a useful way to make predictions and assist in the decision making process.
• The degree is closely related to RMU’s widely acclaimed actuarial science program with a variety of common courses. Many insurance companies with experience in hiring RMU actuarial science students are taking advantage of information derived from large data sets and will be looking for students in the area of analytics.
• Classes are small and informal, taught by experienced professors dedicated to undergraduate education.
• Training to use technology begins in the freshman year and is integrated throughout the curriculum. 

Curriculum

The 120-credit hour curriculum has three components:

1. RMU Core – 40 credits
These are the traditional liberal arts requirements of the university. Studies in humanities, communications skills, and social, behavioral, natural and quantitative sciences are included.

2. Major Courses – 65 credits
This component includes courses in accounting, finance, mathematics, statistics, microeconomics, and information systems.

3. Open Electives – 15 credits
Students may select courses within university offerings to broaden their skills in some area of interest. 

Strong Career Prospects

In the four previous editions of the CareerCast.com, dating back to 2014, statistician and/or data scientist has never been rated lower than fourth. In both 2016 and 2017, statistician was ranked in the top two professions and in 2016, data scientist and statistician were ranked No. 1 and 2 respectively. The editors compile statistics on 200 occupations and rank them based on four key criteria: environment, income, employment outlook, and, stress. The data comes from government sources, such as the U.S. Bureau of Labor Statistics as well as studies from trade associations and industry groups. 

Preparing for RMU

Students seeking to major in statistics and predictive analytics should take as many high school mathematics and computer science courses as possible. A strong background in all areas of pre-calculus mathematics and any programming/database courses provides an excellent basis for university work. Students also should have an interest in developing management and communications skills. Students planning to transfer college credits to RMU should complete courses in calculus, introductory economics, accounting and other business-related areas.

The major is highly interdisciplinary in content. Students are required to take courses in general mathematics, statistics, database management, business, finance, programming, science, and communications. Applicants should have a very high aptitude and solid background in mathematics. An ACT mathematics sub-score of at least 27 or an SAT mathematics sub-score of at least 640 is required for unconditional entrance into the program. The admissions review committee will accept students with lower sub-scores on a probationary basis.