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Data Analytics for Systems Operations (DASO)

Overview Outcomes Courses Admission Instructors

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Data Analytics for Systems Operations

NEXT START DATE: September 25, 2024
APPLY BY: September 1, 2024

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Program overview

This certificate will provide students the technical expertise for data-driven analysis of complex engineering systems, which will enable optimal decision making for executing the processes and operations in such systems. Those with this certificate will be able to understand and apply machine learning and statistical inference techniques to model and predict the outcomes of the systems under varying operating conditions.

Through the Data Analytics for Systems Operations (DASO) certificate, students will gain expertise in the design and analysis of experiments, including discrete event simulation, which are typically not covered in other engineering certificate programs. Therefore, students will acquire the credentials to assume lead technical responsibilities for running large-scale product/process development and project operations.

Learning outcomes

  • Conduct design of experiments, analyze the data, interpret the results, and draw sound conclusions.
  • Apply the right optimization model to different industries such as health care, transportation and environmental ecology, and validate the models using formal procedures such as cross-validation.
  • Use existing software and R/Python packages to implement statistical predictive models and analyze real-world data.
  • Identify, formulate, and solve data analytics problems by applying benchmark machine learning models such as regression model, decision tree, support vector machine, and LASSO to analyze the data generated from a real-world system.
  • Use toolboxes for state-of-the-art machine learning models (deep neural networks, large language models, vision-language models, etc.) to analyze datasets from the real-world.
  • Apply different kinds of simulation models: discrete event-based simulation, Monte Carlo simulation, agent-based simulation, etc. with input specification, output analysis, and variance reduction.
  • Communicate the results and outcomes effectively to a wide range of audiences with varying knowledge in data analytics.
  • Function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.

Courses

Completion of the certificate requires earning 15 credits among our offered courses. You may be able to take individual courses without enrolling in the certificate program.

Explore all DASO courses

Admission requirements

Applicants will need to have a 3.0 cumulative grade-point-average on a 4 point scale. In addition, applicants need to submit a resume or curriculum vitae (CV), statement of purpose, and unofficial/electronic transcripts to be considered for admission.

All admission requirements

Application deadline: September 1, 2024

Our next program starts on September 25, 2024.

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Meet Your Instructors

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Ashis Banerjee
Associate Professor, UW Industrial & Systems Engineering

Ashis directs the Scale-independent Multimodal Automated Real Time Systems (SMARTS) Lab. He is affiliated with the Boeing Advanced Research Center (BARC), and serves on the advisory board for the UW Amazon Science Hub. Prior to joining the UW, he was a research scientist at GE Global Research, Niskayuna, NY, and a research scientist and postdoctoral associate in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. He obtained both his Ph.D. and M.S. in Mechanical Engineering at the University of Maryland, and B.Tech. in Manufacturing Science and Engineering at the Indian Institute of Technology, Kharagpur.

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John Choe
Associate Professor, UW Industrial & Systems Engineering

Dr. John Choe is an Associate Professor of Industrial & Systems Engineering at the University of Washington, Seattle. He is the Director of the Disaster Data Science Lab, which researches how to leverage data to help others before, during, and after disasters. He received his Ph.D. in Industrial & Operations Engineering (Concentration: Quality Engineering & Applied Statistics) and M.A. in Statistics from the University of Michigan, Ann Arbor. He holds bachelor’s degrees in Physics and Management Science from KAIST in Korea.

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Shuai Huang
Associate Professor, Industrial & Systems Engineering

Dr. Shuai Huang received a B.S. degree on Statistics from the School of Gifted Young at the University of Science and Technology of China in 2007 and a Ph.D. degree on Industrial Engineering from the Arizona State University in 2012. Shuai’s research focuses on data analytics and AI problems with a particular interest on healthcare applications. His research has been funded by the NSF, NIH, DARPA, Juvenile Diabetes Research Foundation (JDRF), and several other research institutes and foundations. Dr. Huang currently serves as Associate Editor for the IISE Transactions in Healthcare Systems Engineering and INFORMS Journal of Data Science.

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Chaoyue Zhao
Associate Professor, UW Industrial & Systems Engineering

Dr. Chaoyue Zhao works on data-driven optimization methodologies to support strategic and operational planning in power systems management. She developed innovative data-driven approaches to enable effective decision-making under uncertainty for power system scheduling problems such as optimal power flow and unit commitment. Her analytical models advance scalable solution methods to improve cost-effectiveness, streamline daily power system operations, and mitigate system. Dr. Zhao has received multiple grants from the federal agencies such as the National Science Foundation, Department of Transportation and Argonne National Laboratory. She is the recipient of awards including the runner up of the Pritsker Doctoral Dissertation Award, and Energy Systems Division Outstanding Young Investigator Award in IISE.