100% Online
Complete your Penn State course work at your own pace and 100% online.
Application deadline
Credits and costs
Stackable Credentials
This program is embedded within the Master of Data Analytics – Base Program.
Enhance Your Data Science Skills in This Online Data Analytics Certificate
Employ quantitative business analytics skills to extract insights from diverse data types.
Apply machine learning principles to raw data to make more informed business decisions.
Use data analysis techniques to find patterns, group data, and extract insights from real-world data sets.
Run statistical analysis software to examine the structure of a society or organization to detect and leverage data patterns to predict threats, attacks, and criminal behavior.
Present complex data types using effective data visualization and communication strategies.
Online Data Analysis Courses
Online Data Analysis Courses
The data analysis courses in this online certificate program expand on the foundational quantitative business analytics skills needed to interpret and analyze data to support informed business decisions.
Students can also enhance their technical leaderships skills surrounding data-related activities by learning effective data visualization and communication strategies.
- 3credits
This course will study the inter-relatedness of cyber-social and cyber-technical aspects of an organization or society as a whole.
- 3credits
This course provides a foundation in the principles, concepts, techniques, and tools for visualizing large data sets.
- 3credits
The theory and application of several quantitative decision-making tools will be studied. The usefulness of these tools will be illustrated using projects and case studies throughout the course. Emphasis will be placed on the application of the tools and techniques and the results they generate.
- Prerequisite
STAT 500
Using Certificate Credits Toward a Stackable Credentials Degree Pathway
As part of the stackable pathway, the courses required for this 9-credit certificate can also be applied to a Master of Data Analytics base program if you apply and are accepted into the program. If you have been accepted into this certificate program, you will not be charged an additional fee to apply for the Master of Data Analytics base program.
Courses may also apply to other degree programs. Students should contact their adviser to discuss transfer credit policies and application fees and details.
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
Start or Advance Your Career
This online certificate program covers the foundations of data analysis and offers a unique blend of technical expertise and leadership skills to help students who wish to advance their career as a data analyst or pursue a leadership role.
Career Services to Set You Up for Success
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.
Ready to take the next step toward your Penn State graduate certificate?
Costs and Financial Aid
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.
2026–27 Academic Year Rates
| How many credits do you plan to take per semester? | Cost |
|---|---|
| 11 or fewer | $1,089 per credit |
| 12 or more | $13,062 per semester |
2027–28 Academic Year Rates
| How many credits do you plan to take per semester? | Cost |
|---|---|
| 11 or fewer | $1,111 per credit |
| 12 or more | $13,323 per semester |
Paying for Your Certificate
Students pursuing a certificate are considered "nondegree," a status that is not eligible for federal student aid, including the Federal Direct Stafford Loan program. A private alternative loan may be an option to consider.
Additionally, Penn State offers many ways to pay for your education, including an installment plan and third-party payments. Penn State World Campus also offers an Employer Reimbursement and Tuition Deferment Plan. Learn more about the options for paying for your education.
Students pursuing a degree and meeting all other eligibility requirements may qualify for financial aid.
Military Benefits
Military service members, veterans, and their spouses or dependents should explore these potential military education benefits and financial aid opportunities, as well.
Additional Cost of Attendance Details
To view the detailed list of cost of attendance elements:
- visit the Tuition Information site
- click the plus sign to expand the table
- select a semester from the World Campus row
Technical Requirements
Review the technical requirements for this program.
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 10 or higher with 16GB 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.
Earn Stackable Credentials on the Way to a Master's Degree
Earn Stackable Credentials on the Way to a Master's Degree
This certificate can serve as a stand-alone credential and also as a step on your journey toward a Master of Data Analytics – Base Program. Stackable credentials recognize your new skills and knowledge while you work toward your master's degree — with no additional application fees.
Stackable Credentials
Earning graduate certificates from Penn State World Campus is a great way to gain new skills and quickly add valuable credentials to your résumé.
This graduate certificate’s courses satisfy requirements toward the following degree:
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.
Learn more about the Master of Data AnalyticsShow Your Progress with a Digital Badge
Show Your Progress with a Digital Badge
Upon completion of this program, Penn State awards you a graduate certificate and a digital badge from Credly.
Certificate earners are awarded a digital badge for each certificate they complete as part of this stackable credentials group. Badges can be displayed digitally to recognize your accomplishment of attaining a new level of knowledge.
Dig Deeper with Hands-On Data Science Opportunities
Dig Deeper with Hands-On Data Science Opportunities
An education through Penn State World Campus encourages you to explore your talents beyond the classroom. While progressing through your program, you will have the opportunity to participate in extracurricular activities traditionally available to resident students.
Institute for Computational and Data Sciences — Engage with faculty affiliated with the Institute for Computational and Data Sciences (ICDS) to solve problems of scientific and societal importance via institutional research.
Nittany AI Challenge — Join a multi-disciplinary team that works to address pressing global issues and build solutions using AI and machine learning. The Nittany AI Challenge is held at University Park but is open to World Campus students.
Interested in opportunities like these and more? Please consult with the lead faculty to learn more about ways to engage with the Penn State community and apply your knowledge while progressing through your World Campus program.
Set Your Own Pace
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.
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
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.
Information for Military and Veterans
Are you a member of the military, a veteran, or a military spouse? Please visit our military website for additional information regarding financial aid, transfer credits, and application instructions.
How to Apply to Penn State
How to Apply to Penn State
Apply by November 1 to start January 11
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.
Spring Deadline
Apply by November 1 to start January 11Summer Deadline
Apply by April 1 to start May 10Fall Deadline
Apply by July 1, 2027, to start August 23, 2027
Steps to Apply
For admission to the J. Jeffrey and Ann Marie Fox 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.
Applicants not meeting these requirements may be asked to complete bridge course work before starting the program and must achieve a score of 80% or higher on assessments. These highly flexible, self-paced preparatory courses help build the foundational skills needed for success in graduate-level study and can serve as a pathway for individuals seeking to transition into the field of data analytics. After completing, you will receive a badge that can be displayed digitally to recognize your accomplishment. Please reach out to [email protected] with any questions regarding these courses.
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. If you are a Penn State alum, you do not need to request transcripts for credits earned at Penn State but must list Penn State as part of your academic history.
Test Scores — GRE/GMAT scores are NOT required and will not be reviewed.
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 Fox Graduate School's "Requirements for Graduate Admission" page. Visit the TOEFL website for testing information. Penn State's institutional code is 2660.
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.
If you have been previously accepted to a program with stackable credentials, you will not be charged an additional application fee for any associated programs.
Associated programs in the data analytics stack:
Certificates
- Graduate Certificate in Foundations of Data Science
- Graduate Certificate in Data Analytics
- Graduate Certificate in Data Engineering for Analytics
Degree
- Master of Data Analytics – Base Program
If you begin with a certificate and are interested in pursuing the Master of Data Analytics – Base Program, work with your adviser while completing your first certificate to determine which program to apply to next.
Up to 15 credits earned in any of these certificate programs may be transferred to the master's degree in data analytics, subject to restrictions outlined in GCAC-309 Transfer Credit.
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.6. Complete the application.
Admissions Help
If you have questions about the admissions process, contact an admissions counselor at [email protected].
Contact Us
Contact Us
Have questions or want more information? We're happy to talk.
For questions 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
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.
Faculty
Youakim Badr
- DegreeH.D.R., University of Lyon
- DegreePh.D., Computer Science, National Institute of Applied Sciences (INSA-Lyon)
- DegreeM.S., Mathematical Modeling and Scientific Software Engineering, Francophone University Agency
- DegreeM.S., Computer Science, Lebanese University
- 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).
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.
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.
Nil Ergin
- DegreePh.D., Systems Engineering, Missouri University of Science and Technology
- DegreeM.S., Engineering Management, Missouri University of Science and Technology
- DegreeB.S., Environmental Engineering, Istanbul Technical University
Dr. Nil Ergin, associate professor of systems engineering, researches system of systems engineering, complex adaptive systems, model-based systems engineering, and multi-agent systems. Dr. Ergin is also affiliated with the Systems Engineering Research Center (SERC), a DoD–funded University Affiliated Research Center, where she works on collaborative research efforts among multiple universities. She is a member of IEEE and INCOSE.
Joseph Gallego
- DegreePh.D., Systems and Computing Engineering, National University of Colombia, Colombia (Meritorious Distinction)
- DegreeM.S., Systems and Computing Engineering, National University of Colombia, Colombia
- DegreeB.S., Systems and Computing Engineering, National University of Colombia, Colombia
- DegreeB.S., Industrial Engineering, National University of Colombia, Colombia
Dr. Joseph Gallego, assistant professor of computer science, teaches courses in machine learning operations, predictive analytics, natural language processing, and computer vision. His research interests include quantum machine learning, computer vision, natural language processing, agents, MLOPS, software development, and the application of machine learning to remote sensing and astrophysics. He has served as a jury member in programming, machine learning, and entrepreneurship contests and worked as a software engineering manager for almost 15 years.
Everton Tavares Guimaraes
- DegreePh.D., Software Engineering, Pontifical Catholic University of Rio de Janeiro
- DegreeM. S., Computing and Systems, Federal University of Rio Grande do Norte
- DegreeB.S., Information Technology, Federal Institute of Education, Science, and Technology
Everton Tavares Guimaraes is an assistant professor specializing in software engineering. He teaches algorithms, programming, software development, architecture, and mobile computing. For a decade, he has partnered with companies domestically and internationally. His research focuses on empirical software engineering, software quality (e.g., maintenance and evolution), technical debt, AI techniques, and mobile software engineering.
Nathalia Nascimento
- DegreePh.D., Computer Science, Pontifical Catholic University of Rio de Janeiro (PUC-Rio)
- DegreeM.S., Informatics, Pontifical Catholic University of Rio de Janeiro (PUC-Rio)
- DegreeB.S., Computer Engineering, State University of Feira de Santana (UEFS)
Dr. Nathalia Nascimento is an assistant professor of software engineering and a faculty affiliate of Penn State's Center for Socially Responsible Artificial Intelligence. She teaches courses in software testing, requirements engineering, deep learning, predictive analytics, and analytics research. Her research focuses on empirical AI engineering, with emphasis on AI for software engineering, software engineering for AI-enabled systems, multi-agent systems, data science automation, sensor-based AI applications, and responsible AI.
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.
Sally Sue Richmond
- DegreeM.S., Information Science, Penn State
- DegreeB.A., Art, Penn State
Sally Sue Richmond is an instructor in information science. She worked as an IT professional for more than 20 years prior to teaching. Her research interests include human-computer interaction, engineering education, and teams.
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 with expertise in the analysis, design, and development of large-scale, software-intensive systems and the use of AI engineering to design and develop intelligent systems that are safe, secure, and trustworthy. His research focuses on the improvement of these practices, and he has taught related courses to engineers and project managers at many prestigious academic, government, and industry organizations worldwide. Dr. Sangwan 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 distinguished contributor and senior member of IEEE and a senior member of ACM.
Doug Schumer
- DegreePh.D., Electrical Systems and Engineering, Rensselaer Polytechnic Institute
- DegreeM.S., Measurement and Control, Carnegie Mellon University
- DegreeB.S., Physics, Carnegie Mellon University
Dr. Doug Schumer is assistant professor of engineering design and faculty director of the REV-UP Center for Entrepreneurship. Following 20 years in industry leading medical device R&D, Dr. Schumer held the position of Rensselaer Polytechnic Institute's professor of practice in biomedical engineering and served as Georgia Tech's first director of design innovation and education. He is a member of the New York Academy of Sciences and the American Physical Society.
Hajime Shimao
- DegreePh.D., Economics, Purdue University
- DegreeB.A., Psychology, University of Tokyo
Hajime Shimao is an interdisciplinary social scientist who explores unique applications of machine learning and artificial intelligence in a wide range of domains, including economics, management, finance, law, history, and art. His research aims to unify views and methodologies from traditional disciplines with the cutting-edge technologies to develop novel research frameworks in social science.
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.
Chengfei Wang
- DegreePh.D., Computer Science, Auburn University
- DegreeM.S., Computer Science, Auburn University
- DegreeM.S., Biophysics, University of Electronic Science and Technology of China
- 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 top-tier AI conference Computer Vision and Pattern Recognition (CVPR) Conference.