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Gain Skills to Apply Data Science and Analytics to Real-World Data Sets
Frame analytics problems, identify data sources, determine analytics methodologies, and design and deploy analytics systems at scale.
Communicate data-driven findings to executive stakeholders using analytical skills and tools.
Design, implement, and apply data management techniques using contemporary "big data" tools, including R, Python and its libraries, Tableau, SQL and NoSQL databases, Hadoop, and the Apache suite.
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.
Online Data Analytics Courses
Online Data Analytics Courses
This 30-credit master’s in data analytics focuses on big data and database design processes. It can teach you to design, deploy, and manage technology infrastructure; predictive analytics; and data aggregation, cleaning, storage, and retrieval.
You can earn up to three related graduate certificates along the way to your master's in data analytics. Students who plan to take the stackable credentials pathway to this degree should follow the course lists for each graduate certificate program. To earn each certificate, you must apply, gain acceptance to the program, and complete the required courses.
- Foundations of Data Science (9 credits)
- Data Analytics (9 credits)
- Data Engineering for Analytics (9 credits)
Students who are not pursuing stackable credentials can follow the full course list for the program.
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)
- 3credits
Practical benefits of data mining will be presented; data warehousing, data cubes, and underlying algorithms used by data mining software.
- 3credits
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
- 3credits
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)
- 3credits
The objective of this course is to provide a foundation in the principles of network and predictive analytics along with hands-on experience with statistical analysis software for studying the interrelatedness of cyber-social and cyber-technical aspects of our society as a whole.
- Prerequisite
CSE 453 or IST 815
- 3credits
This course provides a foundation in the principles, concepts, techniques, and tools for visualizing large data sets.
- 3credits
Application and interpretation of analytics for real-life decision-making.
- Prerequisite
STAT 500
Electives (select 9 credits)
- 3credits
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
- 3credits
This course will cover the main theory and approaches of reinforcement learning, along with deep learning and common software libraries and packages.
- 3credits
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.
- Prerequisite
STAT500 and A-I 570 or DAAN 570
- 3credits
Creative projects, including nonthesis research, that are supervised on an individual basis, and which fall outside the scope of formal courses.
- 3credits
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.
- 3credits
The Ethics of Artificial Intelligence is the young branch of applied ethics that seeks to study the far-reaching and diverse ethical issues that arise with the widespread and rapid integration of AI technologies into various aspects of our lives.
- 3credits
This course focuses on the design of computer-based, machine vision systems using appropriate algorithms and best practices. Students will learn image representation and structuring; feature extraction and segmentation; and information extraction, filtering, and analysis.
- 3credits
This course provides a foundation in finance from the perspective of the firm. The objective is to show students that basic financial principles can be useful no matter the type of job.
- 3credits
The course is designed to expand the student's knowledge of the broad field of financial and investment analysis.
- 3credits
Study of quantitative methods used in financial and investment analysis and modeling.
- 3credits
Examines tools and techniques required for data collection and computational procedures to automatically identify and eliminate errors in large data sets.
- Prerequisite
STAT 500
- 3credits
Examination of large-scale data storage technologies including NoSQL database systems for loosely-structured data, and warehouses for dimensional data.
- 3credits
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
- 3credits
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.
- 3credits
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.
- 3credits
The requirements capture, design, and development of relational database applications; analysis of business requirements and development of appropriate database systems.
- 3credits
Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression.
- Prerequisite
STAT 500 or equivalent; matrix algebra
- 3credits
Identification of models for empirical data collected over time. Use of models in forecasting.
- Prerequisite
STAT 462 or STAT 501 or STAT 511
- 3credits
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)
- 3credits
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.
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.
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
Statisticians
Database Architects
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 master's degree?
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.
2024–25 Academic Year Rates
| 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 |
2025–26 Academic Year Rates
| How many credits do you plan to take per semester? | Cost |
|---|---|
| 11 or fewer | $1,078 per credit |
| 12 or more | $12,933 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. Federal financial aid may only be used to pay for credits used to satisfy program requirements.
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 Your Degree
Earn Stackable Credentials on the Way to Your Degree
Earn graduate certificates while you work toward your degree. It’s a great way to quickly gain new skills and add valuable credentials to your résumé — without any additional course work, course fees, or application fees.
Finish the following three certificate requirements and the 3-credit capstone course, and you will have completed the requirements to earn your data analytics master's degree. Please note you must apply and be accepted to each program on an individual basis, but you will not be charged application fees for any program beyond the first.
Complete All of These Certificates (27 credits)
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.
Learn more about the Graduate Certificate in Foundations of Data ScienceThis 9-credit online data analytics certificate program focuses on quantitative business analytics skills needed to interpret and analyze data to support informed business decisions. It can help you prepare for a career as a data analyst or business analyst and is embedded within the base program of the data analytics master’s degree.
Learn more about the Graduate Certificate in Data AnalyticsThis 9-credit online data engineering certificate program focuses on developing, deploying, and maintaining data within large-scale databases. It can help you prepare for a career as a data engineer or data architect and is embedded within the base program of the data analytics master’s degree.
Learn more about the Graduate Certificate in Data Engineering for AnalyticsComplete the Capstone Course (3 credits)
After the certificate requirements have been met, you just need to complete the 3-credit capstone course to finish the master's.
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.
Who Should Apply?
This program is ideal if you are a data scientist, data engineer, data architect, data analyst, research analyst, or data/information analyst or if you work in or desire to work in a position that requires the design and maintenance of big data and data analytics systems, as well as:
- data mining
- data modeling
- data visualization
- predictive modeling
- data architecture
- statistical analysis
- extraction, transformation, loading (ETL) development
- business intelligence (BI) development
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 12
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 12Summer Deadline
Apply by March 15 to start May 18Fall Deadline
Apply by July 1, 2026, to start August 24, 2026
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.
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.
For questions about transcripts, contact:
Penn State Great Valley
Phone: 610-648-3242
[email protected]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.
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.
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:
Degree
- Master of Data Analytics – Base Program
Certificates
- Graduate Certificate in Foundations of Data Science
- Graduate Certificate in Data Analytics
- Graduate Certificate in Data Engineering for Analytics
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.
To learn more about the Master of Data Analytics, follow the guidelines below.
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.
With their broad spectrum of experiences, our faculty can teach you to collect, classify, analyze, and model data at large and ultra-large scales and across domains, using statistics, computer science, machine learning, and software engineering.
Faculty
Adrian S. Barb
- 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.
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).
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.
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.
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.
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.
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