The ability to manipulate and understand data is increasingly critical to discovery and innovation. The vast majority of scientific and engineering disciplines, including molecular engineering, have entered an era in which discovery is no longer limited by the collection and processing of data, but by the management, analysis, and visualization of data. Therefore, the next-generation of scientists need to be prepared to manipulate and understand large, dynamic data sets.
The Molecular Engineering (MolE) Graduate Program offers our students the option to receive credentialed training in the analysis of large datasets. The goal of this option, known as the Data Science Option (DSO), is to introduce students to the foundations of data science and provide them with techniques and tools that they can apply to their own research. This option is primarily designed to help students with little or no background in data science, computer science, or coding, become proficient "tool users" as opposed to "tool builders".
The additional overall course load required to complete this option is limited since many of the data science courses can also be used to satisfy core MolE Ph.D. requirements. Students who fulfill the requirements outlined below will have the option included as part of the degree title that appears on their transcript.
Requirements
Take a course from two of the following three areas:
- Software development for data science
- Recommended courses:
- Software Development for Data Scientists: (CSE 583)
- Software Engineering for Molecular Data Scientists: (ChemE 546)
- Statistics and machine learning
- Recommended courses:
- Introduction to Machine learning: (CSE 416/STAT 416)
- Introduction to Statistical Machine Learning: (STAT 435)
- Data management and data visualization
- Recommended courses:
- Introduction to Database Systems: (CSE 414)
- Data Visualization: (CSE 512/CSE 412)
- Information for Visualization (HCDE 411/511)
- Interactive Information Visualization: (INFX 562)
Attend at least two quarters of this weekly seminar series featuring scholars who work across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. External speakers from regional partners, governmental agencies and industry are occasionally featured.
MolE students are already required to fulfill the research facet: Theory, Computation and Modeling to attain their Ph.D. in molecular engineering. Courses that satisfy this requirement are offered by a variety of departments including bioengineering, chemistry, chemical engineering, electrical & computer engineering, computer science & engineering, materials science & engineering, mechanical engineering, and physics.
eScience Institute
The MolE data science option is supported by the eScience Institute. Students interested in data science are encouraged to check out other activities organized by the eScience Institute such as tool and method-oriented workshops and hackathons.
Questions? Email our MolE Graduate Program Advisor.