Data assimilation and estimation theory

STAT 775
Instructor: Colin Grudzien

Class Information:

Class times: Fall 2021 – MonWedFri 10:00 PM - 10:50 AM

Class room: PE 105

Final exam: 5:00 PM Friday December 10th, see final exam policy

Instructor information:

Contact Office Office Hours
DMSC 218 MonWed 4:00 - 5:30 PM on zoom
  1. 784-7554
or by appointment

Health and safefty updates

The latest guidelines addressing COVID 19 topic, including face covering, social distancing, contact tracing, and testing, are available on the UNR Coronavirus website. The website also includes section that addresses many frequently asked questions regarding COVID 19 vaccinations and what to do regarding positive tests and close encounters.

Face Coverings Required for In-person Attendance

In response to COVID-19, and in alignment local, state, and U.S. Center for Disease Control guidelines, face coverings are required at all times in all University indoor public spaces, including classroom, laboratory, studio, creative space, or any type of in-person instructional activity, and public spaces. Furthermore, individuals who have not been fully vaccinated against COVID 19 are required to wear a face covering at all times while on campus, including all indoor and outdoor public spaces.

A “face covering” is defined as a “covering that fully covers a person’s nose and mouth, including without limitation, cloth face mask, surgical mask, towels, scarves, and bandanas” (State of Nevada Emergency Directive 024).


Anyone 12 and older is eligible to receive the Pfizer COVID-19 vaccine, and anyone 18 and older is eligible to receive the Moderna or Johnson & Johnson vaccine. There are multiple locations in the community offering free vaccination. Vaccinations and testing are also available for free at the Student Health Center for students, faculty and staff.

Vaccination sites


Announcements and course updates will be posted in Canvas. Students are expected to keep up-to-date on the course by reading these announcements for important information on assignments, midterms, etc.

Help & Questions

There will be a classroom discussion forum where you can seek help from the instructor and peers about the assignments. Some assignments will require that you post and discuss results in this forum.

The best way to contact the instructor is via email or message within Canvas. Messages received before 4:00 PM during business days will usually get a response the same day. Emails received after 4:00 PM or during the weekend are not guaranteed a same-day response. Longer questions will not be answered by email and will be directed to appointments or office hours.

The instructor will be available from 4:00 - 5:30 PM MonWed for public office hours and questions, shared with students from STAT 445 / 645. If you cannot attend office hours or if you need to discuss something privately you can set an appointment with the instructor directly.


When communicating online, you should always:

Course description


Data assimilation (DA) refers to techniques used to combine the data from physics-based, numerical models and real-world observations to produce an estimate for the state of a time-evolving random process and the parameters that govern its evolution. Owing to their history in numerical weather prediction, DA systems are designed to operate in an extremely large dimension of model variables and observations, often with sequential-in-time observational data. As a long-studied “big-data” problem, DA has benefited from the fusion of a variety of techniques, including methods from Bayesian inference, dynamical systems, numerical analysis, optimization, control theory and machine learning. DA techniques are widely used in many areas of geosciences, neurosciences, biology, autonomous vehicle guidance and various engineering applications requiring dynamic state estimation and control.

After introducing students to essential elements of linear estimation theory, this course will focus on the development and application of operational, statistical learning algorithms in nonlinear, physics-based models. The course will rely on the Python programming language for many exercises and for the final project, with the DAPPER library providing a framework for the class. The DAPPER library is suitable for small- to mid-scale DA and uncertainty quantification research and provides a pedagogical stepping stone for students to high-performance libraries and / or writing their own solvers. Students do not need to know Python programming in advance, but some familiarity with programming and computation generally will be beneficial. final exam policy.

Structure for online learning

This class will follow a flipped design in which students are expected to watch one or more short lectures before the class session. If a student is unable to or would prefer not to attend in-person, a HyFlex remote learning option will be granted. Video lectures will cover content that is relevant to the day’s activity in class. Class sessions will be held on Zoom and students are expected to attend the Zoom meeting to work on the day’s activities with their cohort. Students should have a laptop for working through activities during class in-person class in the Python programming language, or should be able to attend remotely with a computer that will accommodate this. Each week will require posting a discussion of about the results in the week’s activities. Programming experience in Python is not required and there will be bi-weekly assignments in DataCamp that will build on fundamental skills in the Python scientific programming environment.


This is an applied course with some treatment of essential theoretical foundations. This course is targeted at statistics and applied mathematics PhD and Masters students, but is open to motivated non- mathematics / statistics majors and undergraduates. The minimum background needed is the following, though students will benefit from the recommended background. Exceptions may be made on a case-by-case basis. Please contact the instructor for approval for enrollment

Required software and technology

By signing up for this class you acknowledge the responsibility to have access to: The following software is required for class assignments:

Personal and University computers

This is a class that will involve programming, data manipulation and computation. You are recommended to use a personal device where you have privileges to install statistical software and store persistent data to, and to clone git repositories. Student laptops are available from the UNR library through their equipment lending program. Graduate students in the Statistics and Data Science program may be eligible to borrow a laptop computer from the Department of Mathematics and Statistics if they do not have a personal device, please contact the instructor for more information. Students may also use library computers and / or remote desktop services for attending Zoom sessions online and working on exercises and assignments. Students who attend class in PE 105 are encouraged to bring an audio headset for communication in Zoom group work during the class session

Students using the Windows Remote Desktop client will need to log into Windows Security with user name written in the form UNR\NetID. UNR should be followed by “backslash”, not “slash”. Mac users will need to additionally download the Microsoft Remote Desktop Application to use remote desktop services. UNR remote desktop clients has Anaconda and LaTeX installed already, and assignments may be completed in this environment if a student does not have a computer with these capabilities. It is recommended to save any data / assignment files in cloud storage as assignment progress may not be saved otherwise.

Required book

There is no required textbook for this class. Students will be expected to follow the video lectures and the class activities to learn the content of this course, but additional recommended resources are the following:

The course will loosely follow Chapters 1 - 5 and 7 from Stochastic processes, estimation, and control and Chapters 1 - 3 and 6 - 11 of Data assimilation: methods, algorithms, and applications. Other sources of material will include relevant journal articles and surveys.

Electronic resources


Students are responsible for checking their email accounts and Canvas for announcements. Students are assumed to be aware of all information posted to these sources prior to each meeting. Announcements, homework and grades will be posted in Canvas.

Course archive page

This page includes an archive of lecture notes and activities from the class, along with the current schedule for the semester.

Student learning outcomes

Students will learn:

Assignments and grading

Video lectures

Each week there will usually be three video lectures to watch before the class activities during the normal class sessions.


In a normal class session, students will work through a class activity that uses the content from the video lectures that week. Participation credit requires a written summary and analysis at the end of the week according to the rubric in Canvas.


The following DataCamp courses will be required for completion credit in the fall:

Certificates of completion for these courses completed before the course start will be accepted for full credit.

Final exam policy

The final exam will consist of a final term report written on one of several available topics to choose from in the assignment. The final project must be submitted on Canvas by 5:00 PM on Friday, December 10th. Details on the assignment are in the final project description.

Final grades

Final grades will be calculated according to the following:

Category Percent of final grade
Class activities / Participation \(50\%\)
DataCamp Homework \(25\%\)
Term Paper \(25\%\)

The final letter grade will be assigned according to the weighted score as in the following table:

Weighted score \(x\) Final letter grade
\(90\% \leq x \leq 100\%\) A
\(80\% \leq x < 90\%\) B
\(70\% \leq x < 80\%\) C
\(60\% \leq x < 70\%\) D
\(0 \% \leq x < 60\%\) F

Important dates

Please see the UNR Academic Calendar for important dates in the semester.

Late policy and exceptions

There are no makeups for participation assignments, DataCamp assignments or the final term paper except for university recognized activities or exceptional circumstances, as per university policy. To accommodate unforeseen circumstances, the lowest DataCamp score and the lowest two participation scores will be dropped from the final grade calculation. If a student needs to miss class due to participation in official university activities or a religious observance, they must make arrangements with the instructor at least one week prior to the date in question – the absence will not be given consideration without this advanced notice. In cases of absences due to extended illness, family emergency, bereavement, or other compelling reason, students should notify the instructor as soon as possible and within one week of the start of the absence. The instructor has the right to request formal, written documentation in such cases as they deem appropriate. Please see the full policy statement on absences.

Diversity statement

The University of Nevada, Reno is committed to providing a safe learning and work environment for all. Students are expected to treat each other and the instructor with respect. No form of harassment, discrimination or bullying will be tolerated. If you believe you have experienced discrimination, sexual harassment, sexual assault, domestic/dating violence, or stalking, whether on or off campus, or need information related to immigration concerns, please contact the University’s Equal Opportunity & Title IX Office at (775) 784-1547. Resources and interim measures are available to assist you. For more information, please visit the Title IX website for UNR

Disability services

Any student with a disability needing academic adjustments or accommodations is requested to speak with the Disability Resource Center (Pennington Student Achievement Center, Suite 230) as soon as possible to arrange for appropriate accommodations. More information can be found at the DRC website.

Academic conduct

No laptops, cell phones, mp3 players, or other electronics are to be used for personal reasons in class. If you are being disruptive during class you will be asked to leave. Disruptions in this context include inadequate participation. Please see our official Student Code of Conduct.

Academic success services

A common habit among successful students is to seek help outside of the classroom. Your student fees cover use of the Math Center (784-4433), Tutoring Center (784-6801), and University Writing Center (784-6030). These centers support your classroom learning; it is your responsibility to take advantage of their services.

Statement on Audio and Video Recording

Zoom meetings will generally be recorded and you will be asked for consent to be recorded to participate in these meetings. Surreptitious or covert video-taping of class or unauthorized audio recording of class is prohibited by law and by Board of Regents policy. This class may be videotaped or audio recorded only with the written permission of the instructor. In order to accommodate students with disabilities, some students may be given permission to record class lectures and discussions. Therefore, students should understand that their comments during class may be recorded.

Academic dishonesty

Cheating, plagiarism, or otherwise obtaining grades under false pretenses constitutes academic dishonesty according to the code of this university. Academic dishonesty will not be tolerated and penalties can include canceling a students enrollment without a grade or giving an F for the assignment or for the entire course. For more details, see the University of Nevada, Reno general catalog. The University Academic Standards Policy defines academic dishonesty, and mandates specific sanctions for violations. See the University Academic Standards policy: UAM 6,502.