The Master of Science in Statistics + Data Science is a comprehensive graduate program designed to equip students with the advanced theoretical and practical skills necessary to analyze complex data and solve real-world problems across a variety of industries. The program combines a strong foundation in statistical theory and mathematics, with cutting-edge techniques in data science, including machine and deep learning, data mining, and computational statistics. Graduates will be prepared to tackle challenges in diverse sectors such as healthcare, finance, technology, and government, or for entry into a Ph.D. program in Statistics, Data Science or Computational Sciences.
Admission
Program prerequisites
Transcript(s) must show successful completion of at least the following undergraduate courses: Calculus-based Statistical Methods, Calculus III, and Linear Algebra. These courses are equivalent to PSU's Stat 452, Mth 253Z, and Mth 261.
In addition to program prerequisites, applicants must meet the university's minimum admission requirements including English language proficiency.
Degree Requirements
Candidates must complete an approved 45-credit program, which includes at least 33 core credits in courses with the Stat or Mth prefix. In addition, students must satisfy Other Requirements (see below).
A student must have a minimum 3.00 GPA on the courses applied to the program of study, as well as a minimum 3.00 GPA in all graduate-level courses taken at PSU. Although grades of C+, C, and C- are below the graduate standard, they may be counted as credit toward a master’s degree with the specific written approval of the department if taken at PSU after the term of formal admission to the graduate program.
Students are responsible for knowing University-level graduate policies and procedures for obtaining the degree. These policies and procedures are in the Graduate School section of the PSU Bulletin. Several of the most frequently asked about University-level graduate policies and procedures can also be found on the Graduate School website.
Core requirements (33 credits)
The 33 core credits must include courses distributed as follows:
Statistics Core (12 credits):
Data Science Core (15 credits)
Training and Validation Core (6 credits)
Electives (12 credits)
A total of 12 elective credit hours must be completed. The following list of courses is pre-approved for elective credit.
| Stat 565 | Experimental Design: Theory and Methods I | 3 |
| Stat 566 | Experimental Design: Theory and Methods II | 3 |
| Stat 567 | Applied Probability I | 3 |
| Stat 568 | Applied Probability II | 3 |
| Stat 571 | Applied Multivariate Statistical Analysis | 3 |
| Stat 572 | Bayesian Statistics | 3 |
| Stat 573 | Computer Intensive Methods in Statistics | 3 |
| Stat 576 | Sampling Theory and Methods | 3 |
| Stat 577 | Categorical Data Analysis | 3 |
| Stat 578 | Survival Analysis | 3 |
| Stat 580 | Nonparametric Methods | 3 |
| Stat 661 | Advanced Mathematical Statistics I | 3 |
| Stat 662 | Advanced Mathematical Statistics II | 3 |
| Stat 663 | Advanced Mathematical Statistics III | 3 |
| Stat 664 | Theory of Linear Models I | 3 |
| Stat 665 | Theory of Linear Models II | 3 |
| Stat 666 | Theory of Linear Models III | 3 |
| Mth 667 | Stochastic Processes and Probability Theory I | 3 |
| Mth 668 | Stochastic Processes and Probability Theory II | 3 |
| Mth 669 | Stochastic Processes and Probability Theory III | 3 |
| Stat 671 | Statistical Learning I | 3 |
| Stat 672 | Statistical Learning II | 3 |
| Stat 673 | Statistical Learning III | 3 |
| CS 541 | Artificial Intelligence | 3 |
| CS 542 | Advanced Artificial Intelligence: Combinatorial Games | 3 |
| CS 543 | Advanced Artificial Intelligence: Combinatorial Search | 3 |
| CS 545 | Machine Learning | 3 |
| CS 546 | Reinforcement Learning | 3 |
| Ec 572 | Time Series Analysis and Forecasts | 4 |
| USP 655 | Advanced Data Analysis: Structural Equation Modeling | 3 |
| EE 516 | Mathematical Foundations of Machine Learning | 4 |
| EE 515 | Computer Vision | 4 |
| EE 518 | Machine Learning Theory and Algorithms | 4 |
| EE 519 | Deep Learning Theory and Fundamentals | 4 |
| EE 525 | Spectral Estimation | 4 |
| Geog 518/ESM 518 | Landscape Ecology | 4 |
| ESM 565 | Investigating Ecological and Social Issues in Urban Parks and Natural Areas | 4 |
| ESM 566/CE 566 | Environmental Data Analysis | 4 |
| ESM 567 | Multivariate Analysis of Environmental Data | 4 |
| ESM 585 | Ecology and Management of Bio-Invasions | 4 |
| Geog 512 | Global Climate Change Science and Socio-environmental Impact Assessment | 4 |
| Geog 514 | Hydrology | 4 |
| ESM 525/CE 565 | Watershed Hydrology | 4 |
| Geog 572 | Critical GIS | 2 |
| Geog 588/USP 591 | Geographic Information Systems I: Introduction | 4 |
| Geog 592/USP 592 | Geographic Information Systems II: Advanced GIS | 4 |
| Geog 594 | GIS for Water Resources | 4 |
| Geog 596 | Introduction to Spatial Quantitative Analysis | 4 |
| Geog 597 | Advanced Spatial Quantitative Analysis | 4 |
| SySc 514 | System Dynamics | 4 |
| SySc 525 | Agent Based Simulation | 4 |
| SySc 527 | Discrete System Simulation | 4 |
| SySc 531 | Data Mining with Information Theory | 4 |
| SySc 535 | Modeling & Simulation with R and Python | 4 |
| SySc 540 | Introduction to Network Science | 4 |
| SySc 552 | Game Theory | 4 |
| SySc 575 | AI: Neural Networks I | 4 |
| BSTA 517 | Statistical Methods in Clinical Trials | 3 |
| BSTA 519 | Applied Longitudinal Data Analysis | 3 |
| PHE 513 | Introduction to Public Health | 3 |
| Epi 512 | Epidemiology I | 4 |
| Epi 513 | Epidemiology II | 4 |
| Epi 514 | Epidemiology III | 4 |
| Epi 536 | Epidemiological Data Analysis & Interpretation | 4 |
Other courses outside the Department and other mathematics courses may be considered, but must be approved as electives by the Statistics + Data Science graduate program adviser. "Approved as elective" means that it is approved inside the 12 elective credit hours but not inside the core requirements. A course or sequence cannot be counted both within the core and as an elective course or sequence.
Other Requirements
The Training and Validation component of the program consists of one of the following options:
Option 1: 3 credits of Stat 501 and 3 credits of Stat 570
Option 2: 6 credits of Stat 570
Option 3: 6 credits of Stat 501
Stat 501 Literature and Research
In this course, a student works under the supervision of a faculty member in an area of probability and statistics in which the student has acquired the background needed to read current probability and statistical literature, prepare a research paper, and present this research in a colloquium. Requirements for the course are contained in the handout: Guidelines and Deadlines for Stat 501 Literature and Research.
Stat 570 Statistical Consulting
Faculty supervised consulting sessions with clients on appropriate projects brought to the Statistics Consulting Laboratory. Data and/or statistical problems from within and outside the University are provided by clients and interdisciplinary guest lecturers. Introduction to and proficiency with various statistical computing packages and data analytic tools. A community-based learning course. This course may be taken twice for credit.
Planning an MS degree program
The Course Projection Guide (CPG) lists the projected future 600-level course offerings. These projections enable students to plan programs that include any necessary 500-level prerequisites. Students also need to plan a program that will prepare them to complete the Training and Validation core requirement. The program provides students the flexibility to choose courses in statistics + data science, as well as in multiple areas of application. For example, students considering a future career in more specialized areas of data science or statistics are encouraged to choose elective credits among Stat 565, Stat 566, Stat 571, Stat 572, Stat 577, Stat 661-3, or Stat 664-6. Students considering future work in machine learning or artificial intelligence (AI) are encouraged to choose elective credits among Stat 671, Stat 672, Stat 673 sequence, CS 541, CS 542, CS 543, CS 544, CS 545, EE 518, or EE 519. Other possible tracks for which courses have been pre-approved are epidemiology, ecology, computational biology, hydrology, geospatial analysis, geographical information systems, climate science, and systems science.
Students who have completed the core courses and wish to pursue a PhD in Mathematical Sciences are strongly encouraged to meet with the graduate program adviser for information about degree requirements and help with program planning.