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Master of Professional Studies inData Analytics – Business Analytics Option

Program summary

Build a practical skill set that can help support data-driven business decisions. The business analytics option of this master’s degree focuses on preparing students for positions such as business analyst and analytic system designer.

100% Online

Complete your Penn State course work at your own pace and 100% online.

Application deadline

Apply by March 15 to start May 13

Credits and costs

30 Credits$1,056 per credit

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Gain Skills to Excel in Business Analytics with a Master’s Degree

  • Apply big data analytics, data mining techniques, and predictive analytics to meet your organization’s business objectives and achieve a competitive advantage.

  • Collect, classify, analyze, and model data at large and ultra-large scales and across domains using statistics, computer science, machine learning, and software engineering.

  • Make impactful contributions in multiple areas of business administration, such as marketing, supply chain management, operations, and risk management.

  • Excel in positions such as business analyst, analytic system designer, or data scientist to help find and solve business challenges.

Business Analytics Courses — More than Predictive Analytics

The online master's in business analytics program curriculum explores and analyzes data to support data-driven business decisions through a complete spectrum of analytics activities:

  • descriptive (what happened) 
  • diagnostic (why it happened) 
  • predictive (what will happen) 
  • prescriptive (what should happen)

The courses offer you the chance to build expertise in business analytics software such as: 

  • Microsoft Excel 
  • Power BI Desktop 
  • RStudio 
  • Visual Basic for Applications (VBA) 
  • Solver (in Excel) 
  • Generalized Algebraic Modeling System (GAMS) 
  • Silver Decisions

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)

  • 3
    credits

    Survey course on the key topics in predictive analytics. Students will learn methods associated with data analytics techniques and apply them to real examples using the R statistical system.

    • Prerequisite

      STAT 500 or equivalent

  • 3
    credits

    Practical benefits of data mining will be presented; data warehousing, data cubes, and underlying algorithms used by data mining software.

  • 3
    credits

    Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and two-way ANOVA, chi-square tests, diagnostics.

    • Prerequisite

      one undergraduate course in statistics

Business Analytics Option Prescribed Courses (9 credits)

  • 3
    credits

    Explores the use of descriptive analytics concepts, tools, and techniques throughout a wide range of business scenarios and problems.

  • 3
    credits

    Explores the use of predictive analytics tools and techniques throughout a wide range of business scenarios and problems; includes a subset of methods such as neural networks, machine learning, social media analytics, and more.

  • 3
    credits

    Development of methods for prescriptive analytics with a focus on business supply side decisions and risk mitigation.

    • Prerequisite

      BAN 840

Electives (select 9 credits)

  • 3
    credits

    This course develops students’ process modeling, data analytics, and decision-making skills regarding the management of people in an organization. Analytics includes data collection, organization, storage, analysis, and all the tools and techniques used to describe current processes, assess and address challenges, and plan for changes.

  • 3
    credits

    Examines tools and techniques required for data collection and computational procedures to automatically identify and eliminate errors in large data sets.

    • Prerequisite

      STAT 500

  • 3
    credits

    This course will study the inter-relatedness of cyber-social and cyber-technical aspects of an organization or society as a whole.

  • 3
    credits

    This course will explore the development of analytics systems and the application of best practices and established software design principles using the Python programming language and its several toolkits.

    • Note

      Requires prior programming experience and permission from the program

  • 3
    credits

    This course provides a foundation in the principles, concepts, techniques, and tools for visualizing large data sets.

  • 3
    credits

    The course examines business intelligence in the era of big data. Emphasis is on the successful implementation of big data in large and small corporations that deliver extraordinary results.

  • 3
    credits

    Introduction, intermediate, and advanced topics in SAS.

    • Prerequisite

      3 credits in statistics

  • 3
    credits

    Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression.

  • 3
    credits

    Identification of models for empirical data collected over time. Use of models in forecasting.

    • Prerequisite

      STAT 462 or STAT 501 or STAT 511

  • 3
    credits

    Analysis and construction of project plans for the development of complex software products; how to manage change and cost control.

Culminating Capstone Experience (3 credits)

  • 3
    credits

    Sets business analytics in real-world context. Explores project life cycle from business problem-framing to model lifecycle management.

    • Prerequisite

      BAN 830, BAN 550, and BAN 840

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.

Start or Advance Your Career

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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 with growing demand, depending on your goals.


Job Titles Related to This Degree

The following roles are often held by people with this type of degree:

  • Actuarial Analyst
  • Business Intelligence Analyst
  • Data Analyst
  • Information Architect

Employment Outlook for Occupational Fields Related to This Degree

Estimates of employment growth and total employment are provided by the U.S. Bureau of Labor Statistics and are subject to change. While these occupations are often pursued by graduates with this degree, individual outcomes may vary depending on a variety of factors. Penn State World Campus cannot guarantee employment in a given occupation.

Data Scientists

35.2%
employment growth (10 years)
159,630
total employment

Actuaries

23.2%
employment growth (10 years)
25,010
total employment

Career Services to Set You Up for Success

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From the day you're accepted as a student, you can access resources and tools provided by Penn State World Campus Career Services to further your career. These resources are beneficial whether you're searching for a job or advancing in an established career.

  • Opportunities to connect with employers
  • Career counselor/coach support
  • Occupation and salary information
  • Internships
  • Graduate school resources 

Ready to Learn More?

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.

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Ready to take the next step toward your Penn State master's degree?

Apply by March 15 to start May 13. How to Apply 

Costs and Financial Aid

Learn about this program's tuition, fees, scholarship opportunities, grants, payment options, and military benefits.

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.

2023–24 Academic Year Rates

Tuition rates for the fall 2023, spring 2024, and summer 2024 semesters.

How many credits do you plan to take per semester?Cost
11 or fewer$1,056 per credit
12 or more$12,678 per semester

2024–25 Academic Year Rates

Tuition rates for the fall 2024, spring 2025, and summer 2025 semesters.

How many credits do you plan to take per semester?Cost
11 or fewer$1,067 per credit
12 or more$12,805 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.

Earn a Valuable Credential along the Way

A figure walking on a path that includes a certificate part of the way through their progress

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.

Certificate Program Related to This Degree

The following certificate can be earned while completing this degree program:

Develop the quantitative and managerial skills needed to analyze complex data sets and support data-driven business decisions. This online program provides a practical approach to deploying analytics in the areas of marketing, supply chain management, operations, and risk management.

Learn more about the Graduate Certificate in Business Analytics

Set Your Own Pace

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Whether you are looking to finish your program as quickly as possible or balance your studies with your busy life, Penn State World Campus can help you achieve your education goals. Many students take one or two courses per semester.

Our online courses typically follow a 12- to 15-week semester cycle, and there are three semesters per year (spring, summer, and fall). If you plan to take a heavy course load, you should expect your course work to be your primary focus and discuss your schedule with your academic adviser. 

To Finish Your Degree in One to Two Years

  • Take 3–4 courses each semester

To Finish Your Degree in Two to Three Years

  • Take 2–3 courses each semester 

To Finish Your Degree in Three to Four Years

  • Take 1 course each semester

Convenient Online Format

This program's convenient online format gives you the flexibility you need to study around your busy schedule. You can skip the lengthy commute without sacrificing the quality of your education and prepare yourself for more rewarding career opportunities without leaving your home.

A Trusted Leader in Online Education

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Penn State has a history of more than 100 years of distance education, and World Campus has been a leader in online learning for more than two decades. Our online learning environment offers the same quality education that our students experience on campus.

How to Apply to Penn State

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Apply by March 15 to start May 13

Application Instructions

Deadlines and Important Dates

Complete your application and submit all required materials by the appropriate deadline. Your deadline will depend on the semester you plan to start your courses.

  • Summer Deadline

    Apply by March 15 to start May 13
  • Fall Deadline

    Apply by July 15 to start August 26
  • Spring Deadline

    Apply by November 15, 2024, to start January 13, 2025

Steps to Apply

  1. 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 course work, professional work experience, and/or standardized test scores.

    GPA — Postsecondary (undergraduate), junior/senior (last two years) GPA of 3.0 or above on a 4.0 scale is required.

  2. 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 protected]

    Test Scores — GRE/GMAT scores are NOT required.

    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.

  3. To begin the online application, you will need a Penn State account.

    Create a New Penn State Account

    If you have any problems during this process, contact an admissions counselor at [email protected].

    Please note: Former Penn State students may not need to complete the admissions application or create a new Penn State account. Please visit our Returning Students page for instructions.

  4. 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.

    • 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
    Given the scale of data used in the data analytics program and the continuous advances in tools and platforms used in data science, students are urged to check individual course technical requirements vigilantly. At a minimum, students will need a PC that runs Windows 7 or higher with 8GB of RAM and 250GB of free space on the hard drive. Mac OS machines are not compatible for most courses in the program and are not recommended.

  5. 5. Complete the application.

Admissions Help

If you have questions about the admissions process, contact an admissions counselor at [email protected].

Contact Us

Customer service representative wearing a headset

Have questions or want more information? We're happy to talk.

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 protected]

For general questions about the program, contact:

Dr. Amanda Neill
[email protected]

Learn from the Best

Delivered through a strong partnership between three academic departments from across the University, the program offers you the opportunity to benefit from the expertise and unique perspectives of faculty who have 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.

Faculty

  • Adrian S. Barb

    • Degree
      Ph.D., Computer Science, University of Missouri
    • Degree
      MBA, Finance and Management Information Systems, University of Missouri
    • Degree
      B.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.

  • Mohamad Darayi

    • Degree
      Ph.D., Industrial and Systems Engineering, University of Oklahoma
    • Degree
      M.S., Industrial Engineering, Tarbiat Modares University
    • Degree
      B.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

    • Degree
      Ph.D., Management Science, University of Tennessee
    • Degree
      M.S., Management Science, University of Tennessee
    • Degree
      B.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

    • Degree
      Ph.D., Industrial and Systems Engineering, Auburn University
    • Degree
      M.E., Industrial and Systems Engineering, Auburn University
    • Degree
      B.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

    • Degree
      Ph.D., Software and Systems Engineering, University of Wales Swansea
    • Degree
      M.Sc., Communications Systems, University of Wales Swansea
    • Degree
      B.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

    • Degree
      Ph.D., Industrial Engineering, Penn State
    • Degree
      Ph.D., (Minor), Computer Science, Penn State
    • Degree
      M.S., Numerical Control, Beijing Institute of Technology, China
    • Degree
      B.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

    • Degree
      Ph.D., Computer and Information Sciences, CST, Temple University
    • Degree
      M.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

    • Degree
      Ph.D., Computer and Information Sciences, Temple University
    • Degree
      M.S., Computer Science, West Chester University
    • Degree
      B.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

    • Degree
      Ph.D., Supply Chain and Information Systems, Penn State
    • Degree
      MBA, Supply Chain Management, Penn State
    • Degree
      Master of Computer Applications, Indira Gandhi National Open University
    • Degree
      B.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

    • Degree
      Ph.D., Industrial Engineering and Operations Research, Penn State
    • Degree
      M.S., Operations Research, Air Force Institute of Technology
    • Degree
      B.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

    • Degree
      Ph.D., Information Technology, University of Nebraska at Omaha
    • Degree
      M.S., Industrial Engineering and Management, Indian Institute of Technology, Kharagpur
    • Degree
      B.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

    • Degree
      Business Analytics, Wharton Business School Executive Education
    • Degree
      Ph.D., Mechanical and Nuclear Engineering, Penn State
    • Degree
      M.S, Mechanical and Nuclear Engineering, Penn State
    • Degree
      B.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.


Ready to take the next step toward your Penn State master's degree?

Apply by March 15 to start May 13. How to Apply