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This program helps participants develop data science skills that can be applied to environmental science research, broadly defined as encompassing environmental, climate, earth sciences and ecology. It also helps participants develop strategies for integrating data science into their grant applications, work effectively with data scientists, and build new collaborations.


  • Review of math foundation and introduction to High-Performance Computing.
  • Basics of Machine Learning (ML).
  • Generalized Linear Model and regression.
  • Bayesian models; spatial statistics; time series analysis.
  • Examples of environmental science research projects with data science methods; Developing research ideas and selecting appropriate data and analytical methods.

Academy Details


  • Certification of completion
  • Ability to work on a breadth of data science topics with data science experts as collaborators
  • Skills to abstractly consider data science solutions and apply them to environmental problems

Tuition cost:
We will send payment instructions along with acceptance decisions.

  • $3,000 for external participants (30% discount for U-M Alums)
  • Thanks to support from the University, we are able to offer a reduced price of $100 for U-M personnel and students

Registration Timeline:
Registration closes June 8.

Later registrants will be accepted only if spots are available.


Cancellation Policy:
>14 days before the first day: full refund minus $50 processing fee
Cancellation between 7 and 14 days of the first day: 50% refund
Less than 7 days: no refund

Who should attend:
This academy workshop is open to all U-M and external environmental scientists, but the content is geared towards junior faculty members and those from the public and private sector who are interested in learning about incorporating data science into their research.

College-level math or statistics. No previous coding experience is required.
Students are expected to bring a laptop for programming components of the workshop.

Weiser Hall 10th floor – 500 Church St. Ann Arbor, MI 48109


Yang Chen
Assistant Professor of Statistics

Paramveer Dhillon

Paramveer Dhillon 
Assistant Professor, School of Information

Xun Huan

Xun Huan
Assistant Professor, Mechanical Engineering

Ken Reid

Ken Reid
Data Scientist, MIDAS

Tentative Curriculum

  • Review of linear algebra and probability; Introduction to estimation and inference; Basic regression analysis; Considerations for experimental design; Functional data analysis; Introduction to High-Performance Computing.
  • Basics of Machine Learning (ML); Supervised ML methods; Unsupervised ML methods; Causal inference.
  • Generalized Linear Model; Generalized Estimating Equations; Multilevel regression; Nonparametric regression; Factor analysis; Dimension reduction regression.
  • Bayesian models; Computational algorithms; Spatial statistics; Time series analysis.
  • (with guest speakers) Examples of environmental science research projects with data science methods; Developing research ideas and selecting appropriate data and analytical methods (all participants are welcome to present their research ideas and receive coaching from the instructors).

Follow-up Sessions

Follow-up sessions will be offered to help attendees develop the data science components of their grant proposals and connect with collaborators.


For questions, please contact MIDAS Data Scientist and session coordinator Ken Reid (