Data Analytics

Graduate Program HeadColin J. Neill
Program CodeDAAN
Campus(es)

Great Valley (M.P.S., M.S.)

World Campus (M.P.S.)

Degrees Conferred

Master of Science (M.S.)

Master of Professional Studies (M.P.S.)

The Graduate Faculty

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Penn State Great Valley offers two graduate programs that cultivate the skills to collect, classify, analyze, and model data: the Master of Professional Studies in Data Analytics (MPS-DAAN) and the Master of Science in Data Analytics (MS-DAAN). Both programs are STEM designated.

The M.S. in Data Analytics (M.S.-DAAN) degree is a research-oriented graduate degree program focused on applying predictive and prescriptive analytics to problems across domains. The program will provide students the skills necessary to frame problems in analytical terms amenable to data analysis; identify the datasets necessary to address the problem; the techniques appropriate to reveal the insight sought, and present that insight to stakeholders.

The M.P.S. in Data Analytics (M.P.S.-DAAN) degree is an interdisciplinary master’s program that provides students the 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.

The M.P.S. curriculum is delivered both in residence at the School of Graduate Professional Studies (Great Valley) and online through the Penn State World Campus.

Admission Requirements

Applicants apply for admission to the program via the Graduate School application for admission. Requirements listed here are in addition to Graduate Council policies listed under GCAC-300 Admissions Policies.

Master of professional Studies (M.P.S.)

Admission to the M.P.S. in Data Analytics program will be based on baccalaureate academic records, applicable work experience, and two letters of recommendation from a previous professor or supervisor who can attest to the applicant’s academic potential. Applicants with an undergraduate degree in a quantitative discipline such as science, engineering, or business may apply. Students from other disciplines will be considered based on prior course work and/or standardized test scores. Applications must include a statement of professional goals, a curriculum vitae or resume, and two letters of recommendation. Test scores from the GMAT or GRE exams are required. An undergraduate cumulative grade-point average of 3.0 or better on a 4.0 scale in the final two years of undergraduate studies is required.

The language of instruction at Penn State is English. English proficiency test scores (TOEFL/IELTS) may be required for international applicants. See GCAC-305 Admission Requirements for International Students for more information.

Master of Science (M.S.)

Admission to the M.S. in Data Analytics program will be based on baccalaureate academic records, applicable work experience, and two letters of recommendation from a previous professor or supervisor who can attest to the applicant’s academic potential. Applicants with an undergraduate degree in a quantitative discipline such as science, engineering, or business may apply. Students from other disciplines will be considered based on prior course work and/or standardized test scores. Applications must include a statement of professional goals, a curriculum vitae or resume, and two letters of recommendation. Test scores from the GMAT or GRE exams are required. An undergraduate cumulative grade-point average of 3.0 or better on a 4.0 scale in the final two years of undergraduate studies is required.

The language of instruction at Penn State is English. English proficiency test scores (TOEFL/IELTS) may be required for international applicants. See GCAC-305 Admission Requirements for International Students for more information.

Degree Requirements

Master of Professional Studies (M.P.S.)

Requirements listed here are in addition to Graduate Council policies listed under GCAC-700 Professional Degree Policies.

The M.P.S.-DAAN degree is conferred upon students who earn a minimum of 30 credits of coursework while maintaining an average grade-point average of 3.0 or better in all course work, including at least 18 credits at the 500 or 800 level (with at least 6 credits at the 500 level). The program curriculum includes 9 credits of core courses, 9 credits of either a selected option or the base program, 9 credits of electives, and a 3-credit capstone course.

Students select to follow either the base program, which prepares them to design and deploy predictive analytics systems, or specialized options in Business Analytics or Marketing Analytics. The base program is available both in residence and online; the options are only available online.

Required Courses
STAT 500Applied Statistics3
IE 575Foundations of Predictive Analytics3
SWENG 545Data Mining3
or STAT 557 Data Mining I
Base Program or Option9
Electives 19
Culminating Experience 23
Total Credits30

All students will complete their program of study with the capstone course corresponding to their chosen option. While each capstone course focuses on problems relevant to their specific domains, they all provide students with an opportunity to apply their knowledge of the theories, methods, processes, and tools of data analytics, learned throughout their program, in a culminating and summative experience. The choice of project topic and exact form will be mutually determined by the instructor and each student. A written paper based on the applied project is required and must contain project description, analysis, and interpretation of its findings. Students are encouraged to upload their capstone projects to be available publically via ScholarSphere and to participate in the Graduate Exhibition.

Base Program

(Offered at Penn State Great Valley and through World Campus)

The base program will create graduates who can design, deploy, and manage the technology infrastructure and data analytical processes of predictive analytics including data aggregation, cleaning, storage, and retrieval. These graduates will work in positions that require them to design and maintain data analytics systems and tools such as Data Modeler, Data Architect, Extraction, Transformation, Loading (ETL) Developer, Business Intelligence (BI) Developer, Data Warehouse Developer and Data Analyst.

Required Courses
INSC 521Database Design Concepts3
DAAN 825Large-Scale Database and Warehouse3
DAAN 881Data-Driven Decision Making3
Culminating Experience
DAAN 888Design and Implementation of Analytics Systems3

Business Analytics option

(Offered through World Campus)

This option prepares graduates to explore and analyze large data sets to support data-driven business decisions.  Target audiences include business analysts, analytic system designers and the data scientists who have a focus on problems arising in the context of business decision-making. The BAN option is organized around the industry-standard rubric of the spectrum of analytics activities: descriptive (what happened), diagnostic (why did it happen), predictive (what will happen) and prescriptive (what should happen).

Required Courses
BAN 5303
BAN 840Predictive Analytics for Business3
BAN 550Prescriptive Analytics for Business3
Culminating Experience
BAN 888Implementing Analytics for Business3

Marketing Analytics Option

(Offered through World Campus)

The aim of this option is to convey how marketing analytics are (1) applied within organizations, (2) conducted, and (3) meaningfully communicated and applied to business decision-making and strategy. The target market would be college graduates that desire to build their skills in marketing analytics functions, but may have little or no formal training in marketing analytics. The MAN option will be highly industry applicable, since it is aimed at giving students the core marketing analytics knowledge they will need to successfully apply marketing analytics in today’s data-driven organizations.

Required Courses
MKTG 811Driving Business Success with Marketing Analytics3
MKTG 812Evaluating Marketing Communications in the Digital World3
MKTG 813Data-Driven Customer Acquisition & Retention3
Culminating Experience
MKTG 814Analytics for Brand Management and Customer Experience3

Master of Science (M.S.)

Requirements listed here are in addition to Graduate Council policies listed under GCAC-600 Research Degree Policies.

The M.S. degree is an academic degree, which is strongly oriented toward research. To receive the Master of Science degree in Data Analytics, a student must complete at least 30 credits beyond the baccalaureate degree at the 400, 500, 600, or 800 level. At least 18 credits in the 500 and 600 series, combined, must be included in the program.

The program curriculum includes 15 credits of core courses, 9 credits of elective courses, and 6 credits of supervised research. The thesis must be accepted by the advisers and/or committee members, the head of the graduate program, and the Graduate School, and the student must pass a thesis defense.

Required Courses
STAT 500Applied Statistics3
IE 575Foundations of Predictive Analytics3
SWENG 545Data Mining3
DAAN 501Analytics Research and Problem Framing3
DAAN 871Data Visualization3
Additional Courses
An additional 9 credits of elective courses must be selected from the approved list of elective courses maintained by the graduate program office. 9
Thesis Research
DAAN 600Thesis Research 16
Total Credits30

The thesis work should be an in-depth investigation intended to extend the state of knowledge in some specialty area. For thesis guidelines and time lines, refer to the Penn State Graduate School website.

Student Aid

Refer to the Tuition & Funding section of The Graduate School’s website. Students in this program are not eligible for graduate assistantships.

Financial aid for students in on-campus programs is in the form of student loans and a limited number of small scholarships, as described on the Penn State Great Valley website.

World Campus students in graduate degree programs may be eligible for financial aid. Refer to the Tuition and Financial Aid section of the World Campus website for more information.

Courses

Graduate courses carry numbers from 500 to 699 and 800 to 899. Advanced undergraduate courses numbered between 400 and 499 may be used to meet some graduate degree requirements when taken by graduate students. Courses below the 400 level may not. A graduate student may register for or audit these courses in order to make up deficiencies or to fill in gaps in previous education but not to meet requirements for an advanced degree.

Data Analytics (DAAN) Course List

Learning Outcomes

  1. COMMUNICATE Graduates will be able to effectively communicate technical knowledge, including ideas, data analysis, findings, or decision justification in written formats in a manner appropriate to the audience.
  2. APPLY Graduates will be able to analyze large data sets to support data-driven decision making.
  3. KNOW Graduates will demonstrate understanding of machine learning and statistical analysis techniques.
  4. THINK Graduates will be able to discriminate between descriptive, diagnostic, predictive, and prescriptive analytics and the techniques used in each.
  5. KNOW Graduates will demonstrate their understanding of technologies used to develop, optimize, and deploy large-scale databases.
  6. PROFESSIONAL PRACTICE. Graduates will demonstrate knowledge of and ability to practice the professional standards of engineering and professional behavior.

Contact

Campus Great Valley
Graduate Program Head Colin Neill
Director of Graduate Studies (DGS) or Professor-in-Charge (PIC) Colin Neill
Program Contact

Sharon Veronica Patterson
Penn State Great Valley
30 E. Swedesford Road
Malvern PA 19355
svp40@psu.edu
(610) 648-3318

Program Website View
Campus World Campus
Graduate Program Head Colin Neill
Director of Graduate Studies (DGS) or Professor-in-Charge (PIC) Colin Neill
Program Contact

Sharon Veronica Patterson
Penn State Great Valley
30 East Swedesford Road
Malvern PA 19355
svp40@psu.edu
(610) 648-3318

Program Website View