Data Analytics student working

Master of Professional Studies inData Analytics

Program summary

Cultivate the knowledge and practical skills required to collect, classify, analyze, and model data at large and ultra-large scales and across domains using statistics, computer science, machine learning, and software engineering with this online data analytics master's degree program.

100% Online

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

Application deadline

Apply by March 15 to start May 13

Credits and costs

30 Credits$1,056 per credit

Nationally Recognized

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Our graduate IT programs are highly ranked by U.S. News & World Report.

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

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

Required Courses (9 credits)

  • 3
    credits

    Survey course on the key topics in predictive analytics. Students will learn methods associated with data analytics techniques and apply them to real examples using the R statistical system.

    • Prerequisite

      STAT 500 or equivalent

  • 3
    credits

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

  • 3
    credits

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

    • Prerequisite

      one undergraduate course in statistics

Base Program Prescribed Courses (9 credits)

  • 3
    credits

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

  • 3
    credits

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

    • Prerequisite

      STAT 500

  • 3
    credits

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

Electives (select 9 credits)

  • 3
    credits

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

    • Prerequisite

      STAT 500

  • 3
    credits

    This course 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

  • 3
    credits

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

  • 3
    credits

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

  • 3
    credits

    Examines tools and techniques required for data collection and computational procedures to automatically identify and eliminate errors in large data sets.

    • Prerequisite

      STAT 500

  • 3
    credits

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

    • Prerequisite

      DAAN 825

  • 3
    credits

    This course will study the inter-relatedness of cyber-social and cyber-technical aspects of an organization or society as a whole.

  • 3
    credits

    This course will explore the development of analytics systems and the application of best practices and established software design principles using the Python programming language and its several toolkits.

  • 3
    credits

    This course provides a foundation in the principles, concepts, techniques, and tools for visualizing large data sets.

  • 3
    credits

    The course examines business intelligence in the era of big data. Emphasis is on the successful implementation of big data in large and small corporations that deliver extraordinary results.

  • 3
    credits

    Introduction, intermediate, and advanced topics in SAS.

    • Prerequisite

      3 credits in statistics

  • 3
    credits

    Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression.

  • 3
    credits

    Identification of models for empirical data collected over time. Use of models in forecasting.

    • Prerequisite

      STAT 462 or STAT 501 or STAT 511

  • 3
    credits

    Analysis and construction of project plans for the development of complex software products; how to manage change and cost control.

Culminating Capstone Experience (3 credits)

  • 3
    credits

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

    • Prerequisite

      IN SC 521, DAAN 825, and DAAN 881

Course Availability

If you're ready to see when your courses will be offered, visit our public LionPATH course search (opens in new window) to start planning ahead.

Start or Advance Your Career

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You can use the knowledge gained from this program and the support of Penn State career resources to pursue careers in a variety of fields, depending on your goals.


Job Titles Related to This Degree

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

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

Employment Outlook for Occupational Fields Related to This Degree

Estimates of employment growth and total employment are provided by the U.S. Bureau of Labor Statistics and are subject to change. While these occupations are often pursued by graduates with this degree, individual outcomes may vary depending on a variety of factors. Penn State World Campus cannot guarantee employment in a given occupation.

Data Scientists

35.2%
employment growth (10 years)
159,630
total employment

Statisticians

31.6%
employment growth (10 years)
30,780
total employment

Database Architects

10%
employment growth (10 years)
62,470
total employment

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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Learn more about this program

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

Apply by March 15 to start May 13. How to Apply 

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.

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

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.

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

Adult student doing course work online while a child plays nearby

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.

Our online courses typically follow a 12- to 15-week semester cycle, and there are three semesters per year (spring, summer, and fall). If you plan to take a heavy course load, you should expect your course work to be your primary focus and discuss your schedule with your academic adviser. 

To Finish Your Degree in One to Two Years

  • Take 3–4 courses each semester

To Finish Your Degree in Two to Three Years

  • Take 2–3 courses each semester 

To Finish Your Degree in Three to Four Years

  • Take 1 course each 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.

How to Apply to Penn State

A new student holding a sign that reads, We Are Penn State and #PennStateBound

Apply by March 15 to start May 13

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.

  • Summer Deadline

    Apply by March 15 to start May 13
  • Fall Deadline

    Apply by July 15 to start August 26
  • Spring Deadline

    Apply by November 15, 2024, to start January 13, 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.

    For questions about transcripts, contact:
    Penn State Great Valley
    Phone: 610-648-3242
    [email protected]

    Test Scores — 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.

  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. 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. 

    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 8GB 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.

  5. 5. Complete the application.

Admissions Help

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

Contact Us

Customer service representative wearing a headset

Have questions or want more information? We're happy to talk.

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 protected]

For general questions about the program, contact:

Dr. Amanda Neill
[email protected]

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

    • Degree
      Ph.D., Computer Science, University of Missouri
    • Degree
      MBA, Finance and Management Information Systems, University of Missouri
    • Degree
      B.S., Industrial Engineering, University of Bucharest

    Dr. Adrian S. Barb, associate professor of information science, teaches databases, data mining, and big data courses. He has worked as a database programmer analyst as well as a web developer at University of Missouri. His research interests include data mining, knowledge discovery in databases, knowledge representation and exchange in content-based retrieval systems, semantic modeling and retrieval, conceptual change, ontology integration, and expert-in-the-loop knowledge generation and exchange.

  • Youakim Badr

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

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

  • Mohamad Darayi

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

    Dr. Mohamad Darayi, assistant professor of systems engineering, focuses his principal research and key publications on infrastructure network resilience and simulation modeling applications in health care, manufacturing, and supply chain management. He teaches courses in system simulation, risk analysis, network modeling, and data analytics.

  • Ashkan Negahban

    • Degree
      Ph.D., Industrial and Systems Engineering, Auburn University
    • Degree
      M.E., Industrial and Systems Engineering, Auburn University
    • Degree
      B.S., Industrial and Systems Engineering, University of Tehran

    Dr. Ashkan Negahban is an associate professor of engineering management. Prior to joining Penn State, he was an instructor at Auburn University, where he taught courses in simulation, probability theory, and statistics. His research interests include the application of different types of simulation (discrete event, agent-based, and Monte Carlo) in design and operation of complex systems. He has developed several e-learning modules that have received worldwide publicity and are used by faculty from leading institutions around the world.

  • Colin Neill

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

    Dr. Colin Neill is a professor of software engineering and systems engineering. He teaches many courses in software and systems engineering and project management. He is the author of more than 80 articles on the development and evolution of complex software and systems and their management and governance. Dr. Neill is a senior member of the IEEE and a member of INCOSE, and he serves as associate editor-in-chief of Innovations in Systems and Software Engineering.

  • Robin G. Qiu

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

    Dr. Robin G. Qiu is a professor of information science at Penn State. He teaches courses on data analytics, information science, software engineering, and cyber security. Dr. Qiu's research includes smart service systems, IoT, big data, data/business analytics, information systems and integration, supply chain and industrial systems, and analytics. He served as the editor-in-chief of INFORMS Service Science. He is an associate editor of IEEE Transactions on Systems, Man, and Cybernetics and IEEE Transactions on Industrial Informatics, and has more than 160 publications.

  • Dusan Ramljak

    • Degree
      Ph.D., Computer and Information Sciences, CST, Temple University
    • Degree
      M.Sc. and B.Sc., Electrical Engineering - Systems Control, University of Belgrade, Serbia

    Dr. Dusan Ramljak, assistant teaching professor of information science, teaches courses on information science, data science, storage systems, and emerging technologies. He has been applying data science on storage systems in NSF IUCRC projects with HPE, Dell, Huawei, and other companies and has more than 20 years of system administration experience facilitating business and research in the U.S., Portugal, and Serbia. His research interests include solving challenging storage systems, provenance, and caching problems, and developing and integrating distributed and parallel data mining and statistical learning technology for an efficient knowledge discovery at large sequence and temporal databases.

  • Raghvinder S. Sangwan

    • Degree
      Ph.D., Computer and Information Sciences, Temple University
    • Degree
      M.S., Computer Science, West Chester University
    • Degree
      B.S., Genetics and Plant Breeding, Haryana Agricultural University

    Dr. Raghvinder S. Sangwan is a professor of software engineering. His teaching and research involve analysis, design, and development of software-intensive systems and their architecture, and automatic/semi-automatic approaches to assessment of their design and code complexity. He actively consults for Siemens Corporate Technology in Princeton, New Jersey, and holds a visiting scientist appointment at the Software Engineering Institute at Carnegie Mellon University in Pittsburgh, Pennsylvania. He is a senior member of the IEEE and ACM.

  • Hajime Shimao

    • Degree
      Ph.D., Economics, Purdue University
    • Degree
      M.S., Economics, Purdue University
    • Degree
      M.S., Decision Science, Tokyo Institute of Technology
    • Degree
      B.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.

  • Satish Srinivasan

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

    Dr. Satish Srinivasan is an associate professor of information science in the engineering division at Penn State Great Valley. He teaches courses related to database design, data mining, data collection and cleaning, design and implementation of predictive analytics system, network and web securities, and business process management. His research interests include social network analysis, data mining, machine learning, big data and predictive analytics, and bioinformatics.

  • Chengfei Wang

    • Degree
      Ph.D., Computer Science, Auburn University
    • Degree
      M.S., Computer Science, Auburn University
    • Degree
      M.S., Biophysics, University of Electronic Science and Technology of China
    • Degree
      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 the Computer Vision and Pattern Recognition (CVPR) Conference.


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

Apply by March 15 to start May 13. How to Apply