Data Analytics student working

Master of Professional Studies in
Data Analytics

Program summary

Cultivate the knowledge and practical skills required 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 with this online data analytics master's degree program.

Application deadline

Apply by July 15 to start August 21

Credits and costs

30 Credits $1,046 per credit

Earn a Master's Degree in Data Analytics Online

Learn to design, implement, and apply data management techniques with Penn State's 30-credit online Master of Professional Studies in Data Analytics degree program. This fully online program helps professionals acquire the technical expertise and analytical skills to help them uncover and leverage large data sets to efficiently make informed decisions that may increase productivity and profits, expand organizational offerings, better define competitive advantage, and more.   

As a student, you can:

  • learn how to frame analytics problems, identify data sources, determine analytics methodologies, and design and deploy analytics systems at scale
  • demonstrate a fundamental understanding of data mining principles, including supervised and unsupervised machine learning and statistical modeling
  • gain skills to effectively communicate data-driven findings to executive stakeholders
  • obtain practical, hands-on experience with contemporary "big data" platforms and tools, including R, Python, and its libraries, Tableau, SQL and NoSQL databases, Hadoop, and the Apache suite
  • apply data science and analytics to real-world data sets across domains

The Penn State Difference

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.

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.

Data Analytics Base Program Course Work

In the comprehensive base program curriculum, the focus of instruction is on big data and database design processes. You can learn data management technologies and techniques for descriptive, prescriptive, and predictive analytics used to leverage competitive advantage in an array of disciplines. Courses are designed to help you become an expert in the data sciences and enable you to apply analytics techniques to business problems involving high volumes of structured and unstructured data. 

You can also choose a program option to concentrate in business analytics or marketing analytics.

Who Should Apply?

This degree program is an ideal choice for data scientists, data engineers, data architects, research analysts, and data/information analysts who work in positions that require the design and maintenance of big data and data analytics systems, as well as:

  • data mining
  • data modeling
  • data architecture
  • extraction, transformation, loading (ETL) development
  • business intelligence (BI) development

Courses

The curriculum of the 30-credit online MPS in Data Analytics can help you learn to design, deploy, and manage the technology infrastructure and data analytical processes of predictive analytics, including data aggregation, cleaning, storage, and retrieval.

You will take 9 credits in the program's core courses, 9 credits of prescribed courses offered to help you design and maintain data analytics systems and tools, and 9 credits of electives chosen in consultation with your program adviser. You will then complete your studies with the 3-credit culminating capstone experience.

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

Base Program Prescribed Courses (9 credits)

  • 3
    credits

    Examination of large-scale data storage technologies including NoSQL database systems for loosely-structured data, and warehouses for dimensional data.

    • Prerequisite

      INSC 521

  • 3
    credits

    Application and interpretation of analytics for real-life decision making.

    • Prerequisite

      STAT 500

  • 3
    credits

    The requirements capture, design, and development of relational database applications; analysis of business requirements and development of appropriate database systems.

Electives (select 9 credits)

  • 3
    credits

    This course covers basic as well as advanced concepts to gain a detailed understanding of Natural Language Processing tasks such as language modeling, text to speech generation, natural language understanding, and natural language generation. Students can learn the necessary skills to design a range of applications, including sentiment analysis, translating between languages, and answering questions. The practical implementation of these applications with deep neural networks is also discussed.

  • 3
    credits

    This course will teach the foundations of AI and give students a practical understanding of the field. This course gives an overview of the core concepts of AI, including the intelligent agents, knowledge and reasoning, reinforcement learning, planning and acting, belief networks, computational learning, Markov decision process, and more.

  • 3
    credits

    This course examines fundamental concepts and ideas in demography and U.S. and world population trends associated with these concepts.

  • 3
    credits

    This course provides an overview of key demographic data sets, and promotes familiarity with, and appropriate use of, these data.

  • 3
    credits

    This course provides an overview of applications in applied demography in business, nonprofit organizations, public policy, and health, including a focus on international applications.

  • 3
    credits

    This course will cover the foundations on neural networks and deep learning networks. It covers the core concepts of deep neural networks, including the convolutional neural networks for image recognition, recurrent neural networks for sequence generation, and generative adversarial networks for image generation.

    • Prerequisite

      STAT 500

  • 3
    credits

    This course will cover the main theory and approaches of reinforcement learning, along with deep learning and common software libraries and packages.

  • 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 provides an exploration of current and emerging big data solutions for handling large quantities of data in real-time. In particular, this course investigates methods to design, develop, and implement several systems used for real-time data analysis and storage such as document databases, column-based databases, queueing systems, and real-time processing systems.

    • Prerequisite

      DAAN 825

  • 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.

  • 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

    Models and measures of vital processes (fertility, mortality, migration) and their effects on growth and age structure of human populations.

  • 3
    credits

    Exposes students to the spatial analysis tools and analytical methods applied to demographic research.

    • Prerequisite

      a graduate course in statistics

  • 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

    Design and implement data science and analytics systems using contemporary tools and techniques. Choice of project topic mutually determined by student and instructor. Students must complete all core and required courses before enrolling.

    • Prerequisite

      IN SC 521, DAAN 825, and DAAN 881

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.

2022–23 Academic Year Rates

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. 

  • Fall DeadlineApply by July 15 to start August 21
  • Spring DeadlineApply by November 15 to start January 8
  • Summer DeadlineApply by March 15, 2024, to start May 13, 2024

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 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.

Please note: 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. With some exceptions, international applicants must take and submit scores for the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS). Minimum test scores and exceptions are found in the English Proficiency section on the Graduate School's "Requirements for Graduate Admission" page. Visit the TOEFL website for testing information. Penn State's institutional code is 2660.

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 — In one page, 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 your one-page statement 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 

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 5GB 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.

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.

* required 1/3

I agree to be contacted via phone, email, and text by Penn State World Campus and affiliates. I understand my information may also be shared with select providers to offer ads that may be of interest to me. Privacy Policy. reCAPTCHA protected. Google Privacy Policy and Terms of Service.

Learn more about this program

Download Program Brochure All my programs 

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

Apply by July 15 to start August 21. How to Apply

Start or Advance Your Career

Two business professionals reviewing numbers

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.


Job Titles Related to This Degree

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

  • Data Analyst
  • Data Engineer
  • Data Scientist
  • Information Architect
  • Information Modeling Specialist

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

35.8%
employment growth (10 years)
105,980
total employment

Statisticians

32.7%
employment growth (10 years)
31,370
total employment

Database Architects

10.3%
employment growth (10 years)
50,440
total employment

Career Services to Set You Up for Success

Student having a virtual meeting on a laptop with a career counselor

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 

Contact Us

To learn more about the Master of Professional Studies in Data Analytics, follow the guidelines below.

For questions 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

    • 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.

  • Nathaniel Bastian
    • Degree
      Ph.D., Industrial Engineering and Operations Research, Penn State
    • Degree
      M.Eng., Industrial Engineering, Penn State
    • Degree
      M.S., Econometrics and Operations Research, Maastricht University
    • Degree
      B.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.

  • Youakim Badr

    • Degree
      H.D.R., University of Lyon
    • Degree
      Ph.D., Computer Science, National Institute of Applied Sciences (INSA-Lyon)
    • Degree
      M.S., Mathematical Modeling and Scientific Software Engineering, Francophone University Agency
    • Degree
      M.S., Computer Science, Lebanese University
    • Degree
      B.S., Computer Science, Lebanese University

    Dr. Youakim Badr, professor of data analytics, teaches courses in analytics programming, analytics systems design, data mining and predictive analytics. His research interests include smart service computing, IoT, information security, big data, machine learning, and built-in analytics. Dr. Badr is a professional member of IEEE, a lifetime member of ACM, and associate member of the ACM special interest group on knowledge discovery and data mining (SIGKDD).

  • 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.

  • Partha Mukherjee

    • Degree
      Ph.D., Information and Technology, Penn State
    • Degree
      M.S., Computer Science, University of Tulsa
    • Degree
      M.Tech., Computer Science, Indian Statistical Institute
    • Degree
      B.Eng., Mechanical Engineering, Jadavpur University

    Dr. Partha Mukherjee, assistant professor of data analytics, teaches courses in analytics programming, data mining, predictive analytics, and analytics systems design. He is a member of ACM, ACEEE, AIS, AiR, and ASE, and has published papers in peer-reviewed IEEE, Elsevier, and ACM Journals and conferences. Dr. Mukherjee’s research interests include social computing, web analytics, data mining, e-commerce, and natural language processing with a focus on text simplification.

  • 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.

  • 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.


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

Apply by July 15 to start August 21. How to Apply