Application deadline
Credits and costs
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)
Base Program 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 | $1,056 per credit |
| 12 or more | $12,678 per semester |
| 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.
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 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. GRE/GMAT scores are NOT required.
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.
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.
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
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Adrian S. Barb
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DegreePh.D., Computer Science, University of Missouri
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DegreeMBA, Finance and Management Information Systems, University of Missouri
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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.
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Youakim Badr
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DegreeH.D.R., University of Lyon
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DegreePh.D., Computer Science, National Institute of Applied Sciences (INSA-Lyon)
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DegreeM.S., Mathematical Modeling and Scientific Software Engineering, Francophone University Agency
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DegreeM.S., Computer Science, Lebanese University
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DegreeB.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).
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Mohamad Darayi
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DegreePh.D., Industrial and Systems Engineering, University of Oklahoma
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DegreeM.S., Industrial Engineering, Tarbiat Modares University
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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.
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Ashkan Negahban
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DegreePh.D., Industrial and Systems Engineering, Auburn University
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DegreeM.E., Industrial and Systems Engineering, Auburn University
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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.
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Colin Neill
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DegreePh.D., Software and Systems Engineering, University of Wales Swansea
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DegreeM.Sc., Communications Systems, University of Wales Swansea
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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.
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Robin G. Qiu
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DegreePh.D., Industrial Engineering, Penn State
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DegreePh.D., (Minor), Computer Science, Penn State
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DegreeM.S., Numerical Control, Beijing Institute of Technology, China
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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.
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Dusan Ramljak
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DegreePh.D., Computer and Information Sciences, CST, Temple University
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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.
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Raghvinder S. Sangwan
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DegreePh.D., Computer and Information Sciences, Temple University
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DegreeM.S., Computer Science, West Chester University
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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.
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Hajime Shimao
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DegreePh.D., Economics, Purdue University
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DegreeM.S., Economics, Purdue University
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DegreeM.S., Decision Science, Tokyo Institute of Technology
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DegreeB.A., Psychology, University of Tokyo
Dr. Hajime Shimao is an assistant professor of data analytics. He teaches courses in predictive analytics and data mining. His research applies machine learning and statistical techniques to a wide range of topics in interdisciplinary social science, including economics, sociology, law, and history. His articles have been published in top conferences, such as the International Conference on Machine Learning (ICML), and academic journals, such as Nature Communications.
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Satish Srinivasan
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DegreePh.D., Information Technology, University of Nebraska at Omaha
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DegreeM.S., Industrial Engineering and Management, Indian Institute of Technology, Kharagpur
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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.
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Chengfei Wang
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DegreePh.D., Computer Science, Auburn University
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DegreeM.S., Computer Science, Auburn University
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DegreeM.S., Biophysics, University of Electronic Science and Technology of China
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DegreeB.S., Biotechnology, University of Electronic Science and Technology of China
Dr. Chengfei Wang is an assistant professor of artificial intelligence. He teaches courses in foundations of AI and analytics programming in Python. His research interests include the robustness problem of deep learning models applied in life-critical missions and business intelligence based on natural language analysis of customer reviews on social media. His research on the robustness of the computer vision model was published at the Computer Vision and Pattern Recognition (CVPR) Conference.
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