Introduction to Statistical Computing

STAT 445/645
Instructor: Colin Grudzien

Class Information:

Class times: Fall 2021 – MonWedFri 12:00 PM - 12:50 PM

Class room: DMSC 106

Final exam: Due 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).

Students that cannot wear a face covering due to a medical condition or disability, or who are unable to remove a mask without assistance may seek an accommodation through the Disability Resource Center or utilize the HyFlex option and attend fully online.


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 775. 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


This course introduces students to computational statistical methods within a modern computing environment. The first part of this course will be devoted to learning the R language and basic programming skills. The second part of this course introduces univariate statistical analysis including confidence intervals and hypothesis testing in R. The third part of this course introduces basic concepts and computational techniques in matrix algebra, numerical analysis and optimization, and for analyzing and simulating multivariate distributions. In the fourth and last part of this course, students are introduced to three different frameworks for statistical modeling, using linear regression, maximum likelihood estimation and Monte Carlo techniques. A final report is required for graduate students and is optional for undergraduate students as in the 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 may attend the meeting in Zoom in the computer lab in DMSC 106, or may attend remotely with a computer that will accommodate the Zoom meeting and the day’s activity. The day’s activities will require posting a discussion of the results according to the assignment rubric in the forum for completion credit. Programming experience in R is not required and there will be assignments in DataCamp that will build on fundamental skills in R, in addition to our lectures and activities.

Catalog description

Introduction to statistical computing; data visualization and manipulation; document creation; graphics; simulation techniques; parallel computing; estimation; optimization; advanced statistical methods.


Prerequisite(s): STAT 352 or STAT 467 or STAT 667 or instructor approval.

Required software and technology

By signing up for this class you acknowledge that you to have access to a computer for statistical computation and a reliable internet connection to attend class activities and to access online class content. We will use the following software 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. 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. If a personal or loaned device is not available, the computer lab in DMSC 106 is reserved for those who wish to attend in-person. Students may use their own device in DMSC 106 or one of the computers provided with the classroom. Students who use the DMSC 106 computers are recommended to use UNR Box cloud storage to save their progress on assignments, as data cannot be saved persistently on the classroom computers. Students who attend class in DMSC 106 are encouraged to bring an audio headset for communication in Zoom group work during the class session.

Outside of class times, students may also use library computers and / or remote desktop services for working on exercises and assignments. 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 have R, RStudio 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:

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

Upon completion of this course, students will be able to:

  1. implement statistical simulation, re-sampling techniques, and maximum likelihood estimation;

  2. conduct a simulation-based power analysis; and

  3. write professional quality reports and computer code.

Graduate students will additionally complete a final term paper.

Assignments and grading

Video lectures

Each week there will be at least three short video lectures to watch before the class activities on Monday, Wednesday and Friday’s normal class sessions.

Activities / Participation

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.


There will be weekly homework throughout the course. In each of the first seven weeks there will be one DataCamp course assigned for completion credit. Following this, there will be a weekly homework assignment in the form of a Canvas quiz with unlimited attempts. The classroom activities will build the techniques for solving the weekly homework assignments. DataCamp assignments include:

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


There will be two midterms scheduled tentatively on:

  1. Midterm Project I – due Friday, October 15, 11:59 PM in Canvas
  2. Midterm Project II – due Wednesday, November 24, 11:59 PM in Canvas

There will be a review session before each exam and approximately one week to complete the take-home project.

Final exam policy

There will be no final exam, instead there will be a take-home coding project assigned and due on the day of the normally scheduled final. The final coding project is required for graduate students and optional for undergraduate students. This will be a semi-comprehensive final project with an emphasis on the concluding topics in the course. Undergraduate students may optionally complete a final project in order replace their lowest midterm score with the score on the final exam. The final project must be submitted on Canvas by 5:00 PM on Friday, December 10.

Final grades

Final grades will be calculated according to the following:

Category Percent of final grade for 400 level Percent of final grade for 600 level
Activities / Participation \(40\%\) \(20\%\)
Homework average \(20\%\) \(20\%\)
Midterm 1 Project \(20\%\) \(20\%\)
Midterm 2 Project \(20\%\) \(20\%\)
Final project \(0\%\) \(20\%\)

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 assignments except for university recognized activities or exceptional circumstances, as per university policy. To accommodate unforeseen circumstances, the lowest homework score and the lowest participation score will be dropped automatically from the final grade calculation. For undergraduates, a midterm score can be replaced with the final project score only once. 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.