This syllabus contains all relevant information about the course: description, grading criteria, schedules of course materials and weekly assignments and due dates. Consider this your roadmap for the course. Please read through the syllabus carefully and feel free to share any questions that you may have.
Course number and title: PSYC148a Applied Statistical Computing Using R
Instructor: Xiaodong Liu (email: xliu0806@brandeis.edu)
Office Hours/Availability: currently no fixed office
hour time, email me for individual meeting. I will reply within 24-36
hours (unless otherwise noted in a particular week).
All the meetings will be through zoom, my zoom meeting ID: 955
5183 4082.
Class time: This summer course is an asynchronous online course. No fixed course time. Students may choose their own time in each week to study/complete the weekly course materials.
The course materials will be in LATTE with 10-week session, the first week starts on 06/03/2024, the course ends on 08/09/2024.
Course Week: from Sunday to Saturday.
This course is designed for students with introductory statistics background who would like to learn to do statistical computing in R and who would like to further their understanding of inferential statistics and statistical modeling.
R is the platform for this course. It will be introduced in a way that helps make the connection between data preparation and manipulation, data generation (simulation), data presentation (descriptively and graphically), statistical concepts, and statistical modeling. Additionally, through R programming, students will learn to document the analytic process for reproducibility of research work.
Topics of statistical computing include methods of describing data, numerical summary and graphical presentation of data, computing of probability, density, and quantiles based on given distributions (e.g., Gaussian distribution, t-distribution, F-distribution, Chi-square distribution, and binomial distribution), simulation and data generation, and writing functions for custom statistics. While the main theme of this course is on statistical computing with R, we will emphasize the applied aspect through using data from empirical studies.
Students who successfully complete Psyc148A will be able to:
In this course, we will follow the principle of “learning by doing”. Statistical concepts and theories will be introduced through applied examples. Students are expected to review and reinforce the key concepts through re-doing all the examples presented in course readings and through completing weekly assignment.
No prior R experience is required. Some introductory statistics experience will be helpful.
This is a four-credit course. Success in this four-credit hour course is based on the expectation that students will spend on average 18 hours per week in this course.
“Every member of the University community is expected to maintain the highest standards of academic integrity. A student shall not submit work that is falsified or is not the result of the student’s own effort. Infringement of academic honesty by a student subjects that student to serious penalties, which may include failure on the assignment, failure in the course, suspension from the University, or other sanctions (see Section 20)” (from the 2023-2024 edition of Rights and Responsibilities). Please consult Brandeis University Rights and Responsibilities for all policies and procedures related to academic integrity. Students may be required to submit work to TurnItIn.com software to verify originality. A student who is in doubt regarding standards of academic honesty as they apply to a specific course or assignment should consult the faculty member responsible for that course or assignment before submitting the work. Allegations of alleged academic dishonesty will be forwarded to the Department of Student Rights and Community Standards. Citation and research assistance can be found at Brandeis Library Guides - Citing Sources (https://guides.library.brandeis.edu/c.php?g=301723).”
All work students submit for this course shall be their own. In instances when collaborative work is assigned, the assignment should list all team members who participated. It is fine for students to use generative artificial intelligence (AI) tools (such as ChatGPT) to help learning. Any such use must be appropriately acknowledged and cited. It is each student’s responsibility to assess the validity and applicability of any AI output that is submitted, you bear the final responsibility. Violations of this policy will be considered academic misconduct.
We draw your attention to the fact that different classes at Brandeis could implement different AI policies, and it is the student’s responsibility to conform to expectations for each course.
| Tasks | Descriptions |
|---|---|
| Weekly assignment 70% | Students will complete nine weekly assignments by the designated deadline. Each assignment will cover the learning objectives specified in each week and earlier. The points of each assignment may be different depending upon the problems asked, but each will be converted into a 100- scale when computing the final course grade. The assignment with the lowest points will be dropped when computing final course grade. Each assignment will take the form of a single R Markdown file and a related HTML file (except week 1, more details in course). Details of grading criteria for each assignment will be given below. |
| Weekly Discussion forum 20% | Students are expected to actively participate in online discussion forum and activities (through posting your thoughts on and solutions to discussion questions, offering comments on the posts from other students, and asking or answering questions from other students). Details of grading criteria for discussion forum will be given below |
| Final project 10% | The final project will help assess overall to what extent students achieve the learning goals. Details of grading criteria for the final project will be given in the instructions of the final project. The final project will be due on 8/9/2024. |
When doing a homework assignment, students shall NOT use any functions or packages not yet introduced in the then course materials (unless otherwise noted OR students already have knowledge of such functions or packages before seeing the assignment problems, in which case, students should include a note and appropriate citation in their work.)
| Points_allocation | Descriptions |
|---|---|
| HTML file 5% | In week one, no HTML file. In all the other weeks, an HTML file will be submitted. The HTML file should include your name, additional references if relevant, and without error message |
| R markdown file/script file 5% | In week one, you will submit an R script file. In all the other weeks, you will submit an Rmd file which should be knitted successfully into the HTML file you submited. |
| R script styles 10% |
Good script styles: Easy to read and follow up; Appropriate and detailed
comments; Appropriate indentation; Appropriate use of functions;
Reasonable object names; Appropriate use of inline code chunks; and No
extra or unnecessary scripts. With the exception of first two assignments, 15% deduction if failure on at least 2 criteria; 50% deduction for failure on 3 or more of the criteria. |
| Completeness and Correctness 80% | Deductions will be made based upon points assigned to each problem and whether the scripts and answers are correct. |
| Late submission | Each assignment is due by 23:50, Friday of the week (Eastern DST/Boston local time). Your assignment will be automatically time-stamped when you submit your work through LATTE. Late submission will get zero points (unless with pre-arrangement or documented emergency, in which case, students should submit whatever they have done as soon as possible and may submit a finished version at a later time with permission from the instructor). |
The discussion forum in LATTE takes the place of discussions that would occur in a traditional classroom. Keep in mind that postings to the forums will be as rich as you make them. They are required so that you can reflect on the course materials and share your knowledge and ideas while gaining from the insights of your peers as well. The instructor will not lead these discussions but may facilitate through probing questions or comments to which you will be expected to respond.
The discussions are an extension of your academic work, so they will require a formal style of writing. They are not the equivalent to discussion postings that you may find in online media sites or in social media (See detailed grading criteria below).
For each week, you should post and response to the discussion forum on at least 2 different days of the course week (and no later than Thursday if it’s your initial post or no later than Saturday if it’s your response to other student’s post).
| Post | Descriptions | Max_points |
|---|---|---|
| Thoughtful post with supporting evidence (if relevant) | Your post should reflect your effort and careful thoughts, including references or citations to weekly readings or other external references. | 5 |
| Detailed response completeness | Answers all the question(s) completely. | 3 |
| Mechanics | Complete sentence, little to no spelling or grammatical errors. | 2 |
| Total points for post | 10 |
| Response | Descriptions | Max_points |
|---|---|---|
| Substantive | Follow-on points from your personal insights and/or from the readings, meet minimum requirement specified in each discussion. | 4 |
| Mechanics | Complete sentence, little to no spelling or grammatical errors. | 2 |
| Total points for response | 6 |
Late post and response: each week, your initial post should be no later than Thursday. Initial post 1 day late will get 4 points deduction; Initial post 2 days or more late will get 8 points deduction. Your response should be no later than Saturday. Response 1 day late will get 3 points deduction; and no response points if it is after Sunday.
| course.points | letter.grade |
|---|---|
| 94 & above | A |
| 90 to <94 | A- |
| 85 to <90 | B+ |
| 80 to <85 | B |
| 75 to <80 | B- |
| 70 to <75 | C+ |
| 65 to <70 | C |
| 60 to <65 | C- |
| <60 | no credit |
Students who have already enrolled in a graduate program and would like to get graduate credit for this course will need to do extra problems given in some assignments and/or in the final project.
Note: B- is the minimum letter grade for graduate credits.
Brandeis seeks to create a learning environment that is welcoming and inclusive of all students, and I want to support you in your learning. If you think you may require disability accommodations, you will need to work with Student Accessibility Support (SAS). You can contact them at 781-736-3470, email them at access@brandeis.edu, or visit the Student Accessibility Support home page. You can find helpful student FAQs and other resources on the SAS website, including guidance on how to know whether you might be eligible for support from SAS. If you already have an accommodation letter from SAS, please provide me with a copy as soon as you can so that I can ensure effective implementation of accommodations for this class. In order to coordinate exam accommodations, ideally you should provide the accommodation letter at least 48 hours before an exam.
There are no required textbooks. Students don’t need to purchase any course material.
All required readings will be posted on LATTE (the course website) two weeks earlier before the topic will be discussed. All the course materials will be posted completely online using Brandeis’ LATTE site, available at (http://latte.brandeis.edu). “LATTE is the Brandeis learning management system: http://latte.brandeis.edu. Login using students’ UNET ID and password.” The site contains the course syllabus, assigned reading materials, links/resources to other course-related materials, discussion forum, practices and assignments, and due dates.
Here are a few recommended references if you would like some additional readings:
Pace, L.(2012). Beginning R – an introduction to statistical programming. New York: Apress. (online version may be available through Brandeis library)
Crawley, M. J. (2013). The R Book. Hoboken, N.J.: John Wiley & Sons Inc. (online version may be available through Brandeis library)
Temple, M. (2016). Simulation for Data Science with R. Packt publishing (advanced reading, devoted to simulation in R, online version may be available through Brandeis library)
Jones, O., Maillardet, R., & Robinson, A. (2014) Introduction to Scientific Programming and Simulation Using R (2nd ed.). Chapman and Hall/CRC. (advanced reading, covering advanced statistical computing materials).
R (https://www.r-project.org) will be the statistical platform for this course. We will also use Rstudio (https://rstudio.com/products/rstudio/) for most of our course work. R is free, Rstudio has free version.
Details of getting R and RStudio will be given in first week.
Students will need a computer or similar equipment for this course.
For those who experience financial challenge to secure the minimal set of hardware, software, and/or course related supplies that are needed to be successful in this course, undergraduate students from Brandeis School of Arts and Sciences should contact Student Financial Services to discuss options available to purchase equipment and other technology and supply needs. Graduate School of Arts and Sciences students should contact Financial Aid in GSAS. Students from other school should contact their corresponding school main office. The library has short-term laptop loan program if needed.
“The Brandeis Library collections and staff offer resources and services to support Brandeis students, faculty and staff. These include workshops, consultations, collaboration, materials and instruction on emerging trends in technologies such as machine learning, emerging trends in research such as data visualization, and emerging trends in scholarship such as open access. Librarians at the Circulation Desk, Research Help Desk, Archives & Special Collections, Sound & Image Media Studios, MakerLab, AutomationLab, and Digital Scholarship Lab are available to help you.”
The weekly schedule here is subject to change/adjust by the instructor.