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Graduate Certificate inFoundations of Data Science

Summary Courses Careers Costs and Aid

This 9-credit online data science certificate program covers the foundations of data science methodology. It is ideal for those who wish to pursue a career as a data scientist or data analyst and is embedded within the base program of the data analytics master’s degree.

100% Online

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

Apply by July 15 to start August 26

9 Credits$1,056 per credit

Stackable Credentials

This program is embedded within the Master of Data Analytics – Base Program.

Gain Foundational Data Skills with a Data Science Certificate

  • Apply machine learning techniques to formulate algorithms and build better predictive modeling systems to discover and predict patterns in big data.

  • Create solutions and strategies for complex business problems.

  • Manage data using predictive modeling, data science methodology, and predictive analytics.

  • Use descriptive statistics, hypothesis testing, regression, ANOVA, and chi-square tests to develop solutions for your organization.

Enroll in comprehensive data science classes to develop data science skills essential for working with big data — including data analysis, data wrangling, and exploratory data analysis.

Students in this program will have the opportunity to work with expert faculty on data science projects to gain hands-on experience in predictive analytics and data mining.

  • 3
    credits

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

    • Prerequisite

      INSC 521, or approval of instructor or department

  • 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

    Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and 2-way ANOVA, Chi-square tests, diagnostics.

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.

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.

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This online graduate certificate covers the foundations of data science methodology and is ideal for those who wish to pursue a career as a data scientist or data analyst.

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 graduate certificate?

Apply by July 15 to start August 26. How to Apply 

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

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.

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.

To view the detailed list of cost of attendance elements, select “World Campus” as the location on the tuition site.

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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

Upon completion of this program, Penn State awards you a graduate certificate and a digital badge from Credly.

Penn State Great Valley Graduate Certificate Credly Badge

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.

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

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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.

Information for Military and Veterans

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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.

Note: This program is under review for GI Bill® eligibility, and you may experience delays attempting to use GI Bill benefits toward this program until it has been officially approved.

GI Bill® is a registered trademark of the U.S. Department of Veterans Affairs (VA). More information about education benefits offered by VA is available at the official U.S. government website at https://www.benefits.va.gov/gibill.

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Apply by July 15 to start August 26

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.

  • Fall Deadline

    Apply by July 15 to start August 26

  • Spring Deadline

    Apply by November 15 to start January 13

  • Summer Deadline

    Apply by March 15, 2025, to start May 19, 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.

    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 Graduate School's "Requirements for Graduate Admission" page. Visit the TOEFL website for testing information. Penn State's institutional code is 2660.

  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. 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.

  5. 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: "Certificate Admission"
    • Choose "WORLD CAMPUS" as the campus

    Checking Your Status 
    You can check the status of your application by using the same login information established for the online application form. 

    Technical Requirements  
    Review the technical requirements for this 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.

  6. 6. Complete the application.

    The Graduate School Application

Admissions Help

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

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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]

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

  • Adrian S. Barb

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

  • Youakim Badr

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

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

  • Ashkan Negahban

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

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

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

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

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

  • Hajime Shimao

    • Ph.D., Economics, Purdue University
    • B.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

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

  • Chengfei Wang

    • Ph.D., Computer Science, Auburn University
    • M.S., Computer Science, Auburn University
    • M.S., Biophysics, University of Electronic Science and Technology of China
    • B.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.

Ready to take the next step toward your Penn State graduate certificate?

Apply by July 15 to start August 26. How to Apply 

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