Academics

The Data Science, Analytics and Engineering MS Programs have core, concentration, and elective courses. The core courses are the same for all concentrations. Students should refer to the handbook for their concentration and elective courses

Core courses

Each student must complete 9 credits in core courses.

STP 502 Theory of Statistics II: Inference (3) or EEE 554 Probability and Random Processes (3) or DSE 598/501 Statistics for Data Analysts
CSE 511 Data Processing at Scale (3), CSE 512 Distributed Database Systems (3) or IFT 530 Advanced Database Management Systems (3)
Choose one from the following:
- CSE 572 Data Mining (3)
- CSE 575 Statistical Machine Learning (3)
- EEE 549 Statistical Machine Learning: From Theory to Practice (3)
- IEE 520 Statistical Learning for Data Mining (3)
- IFT 511 Analyzing Big Data (3)
- IFT 512 Advanced Big Data Analytics/AI (3)
- MAE 551 Applied Machine Learning for Mechanical Engineers (3)
- STP 550 Statistical Machine Learning (3)

EE Concentration students- Please note that EEE 554 is required as a concentration course, so students in the EE Concentration must take STP 502 as a core course.

Concentration and elective courses

Each student is required to take a specific number of concentration and elective courses. The concentration and elective courses can be found in the handbook under the Resources page. These courses were selected by the faculty in each school based on the topics that are applicable for students in the program. It is possible that the list of courses could change over time, so students should refer to their handbook frequently.

Culminating experience options

Capstone course

The capstone course, FSE 570 Data Science Capstone, will bring together a highly diverse and interdisciplinary group of students to engage in client-driven group projects involving technical and nontechnical aspects of data science. It is anticipated that although one instructor will coordinate the class, a number of faculty will deliver guest lectures to provide a variety of perspectives on data science. It is expected that this course will be taken during the final semester of study.

Applied Project (Material Science concentration only)

Students in the Material Science concentration should speak with their assigned academic advisor regarding their applied project course.

Thesis

The thesis option is the research master’s degree. A member of the corresponding Graduate Faculty (with co-chair or chair status as specified on the DSAE website) must agree to serve as the program committee chair (faculty advisor) for a student to be transferred to the MS thesis degree program. The Graduate Academic Advising Office will need to have confirmation in writing (email is acceptable) from the faculty member agreeing to serve as the thesis chair before a plan change is made to move to the MS thesis degree. The faculty that is serving as the committee chair, in consultation with the student, will establish the full committee. It must be composed of a minimum of three members from the ASU faculty with at least two being from the DSAE Faculty. If the committee chair has co-chair status on the graduate faculty, the program committee must include a DSAE faculty member with the chair or co-chair status serving as second co-chair. Participation of individuals from institutions external to ASU is encouraged but must be approved by the Graduate Program Chair and the Graduate College.

Once the thesis is completed, the student will submit it to the committee members. There will be an open oral defense following the completion of the thesis. A student can schedule the defense after the student’s committee chair has approved the student’s thesis format. The student must schedule their defense through their iPOS at least 10 working days prior to the defense date.

The supervisory committee evaluates the thesis and the student’s performance on the defense. The committee accepts the thesis, accepts it with changes, or rejects it. A rejected decision is final.