Introduction to Statistical Computing

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

Vaccination sites

• List of vaccination sites in Washoe County
• Washoe County Health Department clinics
• Immunize Nevada list of clinics

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.

Netiquette

When communicating online, you should always:

• Treat your instructor and peers with respect, even in email or in any other online communication.

• Use clear and concise language. Be respective of readers’ time and attention.

• Remember that all college level communication should have correct spelling and grammar.

• Avoid slang terms and texting abbreviations.

• Use standard fonts that are optimized for online reading (e.g., sans serif) along with a consistent and readable size (12 or 14 pt.)

• Avoid using the caps lock feature AS IT CAN BE INTERPRETED AS YELLING.

• Limit and possibly avoid the use of emoticons. Not everyone knows how to interpret them.

• Be cautious when using humor or sarcasm as tone is sometimes lost in an email or discussion post and your message might be taken literally or offensively.

• Be careful sharing personal information online (both yours and other’s).

• Always give proper credit when referencing or quoting another source. (Corollary: Don’t copy and paste another student’s post and claim it as original as that is essentially plagiarism.)

• Don’t repeat someone else’s post without adding something of your own to it. (See corollary above regarding reuse of someone else’s post.)

• Always be respectful of others’ opinions even when they differ from your own. When you disagree with someone, you should express your differing opinion in a respectful, non-critical way. (Corollary: Do not make personal or insulting remarks.)

Content

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.

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.

Canvas

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.

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.

Homework

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:

• Introduction to R
• Intermediate R
• Introduction to Importing data in R
• Introduction to the Tidyverse
• Introduction to Data Visualization with ggplot2
• Intermediate Data Visualization with ggplot2
• Reporting with R Markdown

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

Midterms

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.