Application deadline
Credits and costs
Online Business Analytics Degree
Learn how to make informed, data-driven business decisions with Penn State's 30-credit online Master of Professional Studies in Data Analytics with an option in Business Analytics. As a student in this data analytics degree program, you can acquire the business intelligence training needed to work in positions such as business analyst, analytic system designer, or data scientist. Learn how to apply big data analytics, data mining techniques, and predictive analytics to meet your organization’s business objectives and leverage competitive advantage.
The Penn State Difference
Delivered in a strong partnership between three academic departments from across the University, the business analytics degree program offers you the opportunity to benefit from the expertise and unique perspectives of faculty with diverse backgrounds. With their broad spectrum of experiences, our faculty can teach you to collect, classify, analyze, and model data at large and ultra-large scales and across domains using statistics, computer science, machine learning, and software engineering.
This program allows for asynchronous learning. That means you can work at your own pace on assignments and not worry about being in class at a specific time. This flexibility makes the program great for working adults, military personnel, or anyone else with a busy lifestyle.
Business Analytics Curriculum: More than Predictive Analytics
The focus of the business analytics curriculum is to explore and analyze data to support data-driven business decisions through the complete spectrum of analytics activities:
- descriptive (what happened)
- diagnostic (why it happened)
- predictive (what will happen)
- prescriptive (what should happen)
In addition to learning how to use optimization and forecasting techniques, you can gain a practical skill set to perform tasks in various areas of business, such as marketing, supply chain management, operations, and risk management.
The courses offer you the chance to build expertise in business analytics software such as:
- Microsoft Excel
- Power BI Desktop
- R
- RStudio
- Visual Basic for Applications (VBA)
- Solver (in Excel)
- Generalized Algebraic Modeling System (GAMS)
- Silver Decisions
Courses
Through the 30-credit online MPS in Data Analytics — Business Analytics option curriculum, you can learn to explore and analyze large data sets to support data-driven business decisions through the complete spectrum of analytics activities: descriptive (what happened), diagnostic (why it happened), predictive (what will happen), and prescriptive (what should happen).
You will take 9 credits in the program's core courses, 9 credits specific to the business analytics option, and 9 credits of electives in consultation with your program adviser. You will then complete your studies with the 3-credit culminating capstone experience.
As key concepts build from one course to the next, prescribed courses (BAN) for the business analytics option must be taken in sequential order starting with BAN 830. BAN 830 is offered each fall, followed by BAN 840 in spring semesters, and BAN 550 in summer semesters. The capstone course, BAN 888, will only be available in fall semesters. This sequencing is specific to the BAN courses and does not necessarily impact core or elective courses, unless where a prerequisite is noted. As always, please consult the program office if you have questions.
Required Courses (9 credits)
Business Analytics Option Prescribed Courses (9 credits)
Electives (select 9 credits)
Culminating Capstone Experience (3 credits)
Course Availability
If you're ready to see when your courses will be offered, visit our public LionPATH course search (opens in new window) to start planning ahead.
Costs and Financial Aid
Graduate Tuition
Graduate tuition is calculated based on the number of credits for which you register. Tuition is due shortly after each semester begins and rates are assessed every semester of enrollment.
| How many credits do you plan to take per semester? | Cost |
|---|---|
| 11 or fewer | $1,046 per credit |
| 12 or more | $12,552 per semester |
Financial Aid and Military Benefits
Some students may qualify for financial aid. Take the time to research financial aid, scholarships, and payment options as you prepare to apply. Military service members, veterans, and their spouses or dependents should explore these potential military education benefits and financial aid opportunities, as well.
How to Apply
Deadlines and Important Dates
Your degree application, including receipt of all materials, must be received by the deadlines below to be considered complete. Space is limited, so you are encouraged to apply early.
To help you manage the application process, our online application management system will provide you with complete details regarding the required elements of your application portfolio — and will even help you track your progress. You can also save your work and return to complete your application at any time.
Admissions Help
If you have questions about the admissions process, contact our admissions counselors.
Admission Requirements
For admission to the Graduate School, an applicant must hold either (1) a baccalaureate degree from a regionally accredited U.S. institution or (2) a tertiary (postsecondary) degree that is deemed comparable to a four-year bachelor's degree from a regionally accredited U.S. institution. This degree must be from an officially recognized degree-granting institution in the country in which it operates.
Applicants with an undergraduate degree in a quantitative discipline such as science, engineering, or business will be given preferred consideration. Applicants from other disciplines will be considered based on prior coursework, professional work experience, and/or standardized test scores.
What You Need
Applications are submitted electronically and include a nonrefundable application fee. You will need to upload the following items as part of your application:
Official transcripts from each institution attended, regardless of the number of credits or semesters completed. Transcripts not in English must be accompanied by a certified translation. Penn State alumni do not need to request transcripts for credits earned at Penn State, but must list Penn State as part of your academic history. If you are admitted, you will be asked to send an additional official transcript. You will receive instructions at that time.
For questions about transcripts, contact:
Penn State Great Valley
Phone: 610-648-3242
Email: [email protected]
GPA and Test Scores — Postsecondary (undergraduate), junior/senior (last two years) GPA of 3.0 or above on a 4.0 scale is required.
The GRE/GMAT requirement is being waived for those submitting an application for 2023 or 2024 admission.
English Proficiency — The language of instruction at Penn State is English. All international applicants must take and submit scores for the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS), with some exceptions. Minimum test scores and exceptions are found in the English Proficiency section on the Graduate School's "Requirements for Graduate Admission" page.
References (2) — You will need to initiate the process through the online application by entering the names, email addresses, and mailing addresses of two references. Upon submission of your application, an email will be sent to each recommender requesting that they complete a brief online recommendation regarding your professional and/or academic strengths and accomplishments, and your potential for success in an online program. The admissions committee prefers that all recommendations be written within the last six months and reference the applicant's current career goals. Please inform your references that they must submit the form in order for your application to be complete.
Program-Specific Questions/Materials
Statement of Purpose — You should submit a personal statement not to exceed 2-3 paragraphs or 1 page, which should describe your specific career goals and objectives, prior experience relevant to the decision to pursue an advanced degree, and other information that may be useful to the admissions committee. Upload to the online application.
Vita or Résumé — A listing of your professional experience. Upload to the online application.
Start Your Application
You can begin your online application at any time. Your progress within the online application system will be saved as you go, allowing you to return at any point as you gather additional information and required materials.
Begin the graduate school application
- Choose Enrollment Type: "Degree Admission"
- Choose "WORLD CAMPUS" as the campus
Checking Your Status
You can check the status of your application by using the same login information established for the online application form.
Technical Requirements
Review the technical requirements for this degree program.
For courses where submitted work or examples involve Minitab or SAS software, students are strongly recommended to have access to a computer running a Windows operating system (as opposed to Mac OS).
Get the resources you need to make informed decisions about your education. Request information on this program and other programs of interest by completing this form.
Start or Advance Your Career
Start or Advance Your Career
You can use the knowledge gained from this program and the support of Penn State career resources to pursue careers in a variety of fields, depending on your goals.
Earn a Valuable Credential along the Way
Earn a Valuable Credential along the Way
Show mastery of specific subjects before your degree is complete. Thanks to shared courses across programs, students can often earn a certificate along with their degree in less time than if they earned them separately.
Contact Us
To learn more about the Master of Professional Studies in Data Analytics – Business Analytics Option, follow the guidelines below.
For questions about the program and regarding how to apply, contact:
World Campus Admissions Counselors
Phone: 814-863-5386
Email: [email protected]
For general questions about the program, contact:
Dr. Amanda Neill
Email: [email protected]
Faculty
-
Adrian S. Barb
-
DegreePh.D., Computer Science, University of Missouri
-
DegreeMBA, Finance and Management Information Systems, University of Missouri
-
DegreeB.S., Industrial Engineering, University of Bucharest
Dr. Adrian S. Barb, associate professor of information science, teaches databases, data mining, and big data courses. He has worked as a database programmer analyst as well as a web developer at University of Missouri. His research interests include data mining, knowledge discovery in databases, knowledge representation and exchange in content-based retrieval systems, semantic modeling and retrieval, conceptual change, ontology integration, and expert-in-the-loop knowledge generation and exchange.
-
-
Nathaniel Bastian
-
DegreePh.D., Industrial Engineering and Operations Research, Penn State
-
DegreeM.Eng., Industrial Engineering, Penn State
-
DegreeM.S., Econometrics and Operations Research, Maastricht University
-
DegreeB.S., Engineering Management (Electrical Engineering) with Honors, U.S. Military Academy at West Point
Dr. Nathaniel Bastian is an instructor of supply chain and information systems. His expertise lies in the discovery and translation of data-driven, actionable insights into effective decisions using mathematics, statistics, engineering, economics, and computational science to develop decision-support models for descriptive, predictive, and prescriptive analytics. His teaching interests lie in the areas of decision analytics, data science, and applied econometrics. His research interests lie in the areas of multiple objective optimization and sequential decision-making under uncertainty.
-
-
Mohamad Darayi
-
DegreePh.D., Industrial and Systems Engineering, University of Oklahoma
-
DegreeM.S., Industrial Engineering, Tarbiat Modares University
-
DegreeB.S., Industrial Engineering, University of Tabriz
Dr. Mohamad Darayi, assistant professor of systems engineering, focuses his principal research and key publications on infrastructure network resilience and simulation modeling applications in health care, manufacturing, and supply chain management. He teaches courses in system simulation, risk analysis, network modeling, and data analytics.
-
-
Terry P. Harrison
-
DegreePh.D., Management Science, University of Tennessee
-
DegreeM.S., Management Science, University of Tennessee
-
DegreeB.S., Forest Science/Forest Products, Penn State
Dr. Terry P. Harrison, professor of supply chain and information systems and Earl P. Strong Executive Education Professor in Business, teaches Prescriptive Analytics for Business. He is a Fellow and past president of the Institute for Operations Research and the Management Sciences (INFORMS) and has served as a member of the INFORMS Analytics Certification Board. He has teaching and research interests in supply chain management and modeling, large-scale production and distribution systems, decision support systems, applied optimization, and the management of renewable natural resources.
-
-
Ashkan Negahban
-
DegreePh.D., Industrial and Systems Engineering, Auburn University
-
DegreeM.E., Industrial and Systems Engineering, Auburn University
-
DegreeB.S., Industrial and Systems Engineering, University of Tehran
Dr. Ashkan Negahban is an associate professor of engineering management. Prior to joining Penn State, he was an instructor at Auburn University, where he taught courses in simulation, probability theory, and statistics. His research interests include the application of different types of simulation (discrete event, agent-based, and Monte Carlo) in design and operation of complex systems. He has developed several e-learning modules that have received worldwide publicity and are used by faculty from leading institutions around the world.
-
-
Colin Neill
-
DegreePh.D., Software and Systems Engineering, University of Wales Swansea
-
DegreeM.Sc., Communications Systems, University of Wales Swansea
-
DegreeB.Eng., Electrical Engineering, University of Wales Swansea
Dr. Colin Neill is a professor of software engineering and systems engineering. He teaches many courses in software and systems engineering and project management. He is the author of more than 80 articles on the development and evolution of complex software and systems and their management and governance. Dr. Neill is a senior member of the IEEE and a member of INCOSE, and he serves as associate editor-in-chief of Innovations in Systems and Software Engineering.
-
-
Robin G. Qiu
-
DegreePh.D., Industrial Engineering, Penn State
-
DegreePh.D., (Minor), Computer Science, Penn State
-
DegreeM.S., Numerical Control, Beijing Institute of Technology, China
-
DegreeB.S., Mechanical Engineering, Beijing Institute of Technology, China
Dr. Robin G. Qiu is a professor of information science at Penn State. He teaches courses on data analytics, information science, software engineering, and cyber security. Dr. Qiu's research includes smart service systems, IoT, big data, data/business analytics, information systems and integration, supply chain and industrial systems, and analytics. He served as the editor-in-chief of INFORMS Service Science. He is an associate editor of IEEE Transactions on Systems, Man, and Cybernetics and IEEE Transactions on Industrial Informatics, and has more than 160 publications.
-
-
Dusan Ramljak
-
DegreePh.D., Computer and Information Sciences, CST, Temple University
-
DegreeM.Sc. and B.Sc., Electrical Engineering - Systems Control, University of Belgrade, Serbia
Dr. Dusan Ramljak, assistant teaching professor of information science, teaches courses on information science, data science, storage systems, and emerging technologies. He has been applying data science on storage systems in NSF IUCRC projects with HPE, Dell, Huawei, and other companies and has more than 20 years of system administration experience facilitating business and research in the U.S., Portugal, and Serbia. His research interests include solving challenging storage systems, provenance, and caching problems, and developing and integrating distributed and parallel data mining and statistical learning technology for an efficient knowledge discovery at large sequence and temporal databases.
-
-
Raghvinder S. Sangwan
-
DegreePh.D., Computer and Information Sciences, Temple University
-
DegreeM.S., Computer Science, West Chester University
-
DegreeB.S., Genetics and Plant Breeding, Haryana Agricultural University
Dr. Raghvinder S. Sangwan is a professor of software engineering. His teaching and research involve analysis, design, and development of software-intensive systems and their architecture, and automatic/semi-automatic approaches to assessment of their design and code complexity. He actively consults for Siemens Corporate Technology in Princeton, New Jersey, and holds a visiting scientist appointment at the Software Engineering Institute at Carnegie Mellon University in Pittsburgh, Pennsylvania. He is a senior member of the IEEE and ACM.
-
-
Rashmi Sharma
-
DegreePh.D., Supply Chain and Information Systems, Penn State
-
DegreeMBA, Supply Chain Management, Penn State
-
DegreeMaster of Computer Applications, Indira Gandhi National Open University
-
DegreeB.S., Mathematics, Statistics, Computer Applications, University of Rajasthan
Dr. Rashmi Sharma, assistant clinical professor of supply chain and information systems and managing director of the Laboratory for Economics, Management, and Auctions, will teach Predictive Analytics (BAN 840). Before coming to Penn State, she worked in the software industry and managed the development of business intelligence solutions for manufacturing. She has teaching and research interests in supply chain management, management science, business analytics, incentive design, and workforce management.
-
-
Chris Solo
-
DegreePh.D., Industrial Engineering and Operations Research, Penn State
-
DegreeM.S., Operations Research, Air Force Institute of Technology
-
DegreeB.S., Mathematics, Penn State
Dr. Chris Solo, clinical assistant professor of supply chain and analytics, teaches Business Strategies for Data Analytics (BAN 530). Dr. Solo previously served more than 21 years in the U.S. Air Force, where he held leadership positions in the intelligence, acquisition program management, and operations research analyst career fields. During this time, he also served on the faculty at the U.S. Air Force Academy, teaching a variety of quantitative analysis courses. He is an active member of the Institute for Operations Research and the Management Sciences (INFORMS).
-
-
Satish Srinivasan
-
DegreePh.D., Information Technology, University of Nebraska at Omaha
-
DegreeM.S., Industrial Engineering and Management, Indian Institute of Technology, Kharagpur
-
DegreeB.S., Information Technology, Bharathidasan University
Dr. Satish Srinivasan is an associate professor of information science in the engineering division at Penn State Great Valley. He teaches courses related to database design, data mining, data collection and cleaning, design and implementation of predictive analytics system, network and web securities, and business process management. His research interests include social network analysis, data mining, machine learning, big data and predictive analytics, and bioinformatics.
-
-
Serkan Yilmaz
-
DegreeBusiness Analytics, Wharton Business School Executive Education
-
DegreePh.D., Mechanical and Nuclear Engineering, Penn State
-
DegreeM.S, Mechanical and Nuclear Engineering, Penn State
-
DegreeB.S., Nuclear Engineering, Hacettepe University
Dr. Serkan Yilmaz works for Royal Philips HealthCare as a senior manager of global risk management, data analytics, and trending for the Philips corporate global risk management and post-market surveillance analytics team, which drives and executes the Philips global business transformation journey in the risk management, complaint handling, and post-market surveillance processes. He reports Philips Key Performance Indicators (KPIs) to the senior executive team and provides insights using data analytics and problem solution techniques. He leads and provides coaching to business teams on medical device risk profiles, risk management, post-market surveillance, data analytics, statistical and trending methods, data insights, and problem solutions. Dr. Yilmaz brings 17 years of industry, engineering, research, and teaching experience with a diverse background in the following areas: manufacturing engineering, medical device industry, quality engineering, regulatory and compliance, medical device risk management, probabilistic risk assessment and reliability, Lean Six Sigma (LSS) and Design for Six Sigma (DFSS), Design for Reliability (DFR), data analytics and trending, risk quantification, Measurement System Analysis (MSA), kaizen, uncertainty propagation, crystal ball forecasting, manufacturing process capabilities, Statistical Process Control (SPC), Design of Experiment (DOE), new product introduction, software and method development and validation, optimization methods, adjunct faculty six sigma, and nuclear engineering. Dr. Yilmaz teaches BAN 830: Descriptive Analytics for Business.
-