- Describe how information is collected, stored, managed, classified, retrieved, and disseminated
- Analyze data to solve problems in practical scenarios
- Apply skills used to program applications, manage systems, and protect data in complex/heterogeneous computing environments
- Apply analytical and statistical methods to retrieve, manipulate, analyze, and visualize data for decision-making
Syllabus
COURSE NAME AND NUMBER: IS381 Statistics and Probability with R
SEMSTER: Fall 2025
CREDITS: 3
PREREQUISITE(S): IS 210, IS 211, IS 361 AND IS 362
INSTRUCTOR: Jason Bryer, Ph.D.
EMAIL: jason.bryer@cuny.edu
OFFICE HOURS:
COURSE DESCRIPTION:
This course covers basic techniques in probability and statistics applied using the R statistical programming language. The course starts with introducing students to R for data import, manipulation, and visualization. Statistical topics include descriptive statistics, sampling techniques, discrete probability models, sampling, statistical distributions, correlation, and null hypothesis testing.
PROGRAM LEARNING OUTCOMES ADDRESSED BY THIS COURSE:
COURSE LEARNING OUTCOMES:
- Effectively use R for conducting analysis, creating reports, and presenting results
- Estimate predictive models using both parametric and non-parametric models.
- Communicate the accuracy of predictive models using a variety of fit statistics
- Have strategies for handling missing data in the predictive modeling pipeline.
- Effectively communicate the results of a predictive models
STUDENTS WILL BE ABLE TO:
- Effectively use R for conducting analysis, creating reports, and presenting results.
- Understand the foundations of probability theory and perform basic probability calculations.
- Build basic stochastic models for commonly encountered data science.
- Explore and summarize data using descriptive statistics.
- Test hypotheses using classical and modern computational techniques.
- Calculate and define the relationship between multiple variables.
REQUIRED TEXTBOOKS:
Open Intro Statistics 4th Edition by David Diez, Mine Çetinkaya-Rundel and Christopher D Barr. Available for free at https://openintro.org
Introduction to Modern Statistics by Mine Çetinkaya-Rundel and Johanna Hardin. Available for free at https://openintro-ims.netlify.app
R for Data Science 2nd edition by Hadley Wickham, Mine Çetinkaya-Runde, and Garrett Grolemund. Available for free at. https://r4ds.hadley.nz
ADDITIONAL RESOURCES:
- R Software – Download from https://cran.r-project.org
- RStudio Desktop – Download from https://posit.co/downloads/
- Windows users should also download and install RTools from https://cran.r-project.org/bin/windows/Rtools/
- Mac users should also download and install Xcode and gfortran. Directions are available here: https://mac.r-project.org/tools/
ASSIGNMENTS AND GRADING:
Data Project (25% Total; Proposal 5%, Final Presentation 20%)
The purpose of the data project is for you to conduct a reproducible analysis with a data set of your choosing. There are two components to the project, the proposal, which will be graded on a pass/fail basis, and the final report. The outline for each of these are provided in the templates. When submitting the assignments, include the R Markdown file (change the name to include your last name, for example LASTNAME-Proposal.Rmd and LASTNAME-Project.Rmd) along with any supplementary files necessary to run the R Markdown file (e.g. data files, screenshots, etc.). Suggestions for possible data sources are included below, however you are free to use data not listed below. The only requirement is that you are allowed to share the data. Projects will be shared with others on this website, so they should be presented in a way that other students can reproduce your analysis.
Labs (50%): R is the statistical software you will use for this course. The labs aim to provide an opportunity for you to apply your statistical content knowledge in the context of problems to solve in R, thus also providing you the opportunity to practice and become familiar with the R platform and language. The labs will be guided; thus, step-by-step procedures will be laid out for you to follow in order to get the desired outputs. Interpretations of results are just as important as the results themselves, so once you have the results, interpret them in the context of the problems. Labs are graded based on completion, accuracy, and thoroughness of results and interpretations.
Final Exam (15%): Exams will consist of conceptual, computational, and application questions, an include a combination of multiple choice, short response questions, as well as a data analysis task. The exams will focus on the material covered during the course of the semester. More detail will be provided about the material assessed by each exam closer in time to the actual exams. It should be noted that most of the statistical skills acquired during this class are constantly building upon earlier learning. This means that even though each exam will focus on the preceding section of the course, students might need to recall skills learned in earlier sections.
Participation (10%): While attendance at synchronous meetups is not required, it is highly encouraged that you do attend: this is where you can ask questions, participate in-situ, and engage with your professor and peers. In addition, announcements and updates relating to coursework will be reviewed during these meetups. With that said, we understand that extenuating circumstances might not allow some of you to attend. Thus, we have built-in diagnostic and weekly formative assessment assignments that will give us an understanding of your current level of knowledge and lingering gaps in knowledge to be completed after attending or watching the recording of every meetup:
- DAACS SRL (https://cuny.daacs.net) and Google Form (only once, at the beginning of the semester)
- Weekly One-Minute Papers
You will receive full points upon completion of each of these assignments.
| Course Assignments | Points or Percentage of Final Grade |
|---|---|
| Participation/ Weekly Formative Assessments | 10% |
| Project Proposal | 5% |
| Final Project Presentation | 20% |
| Labs / Weekly Assignments | 50% |
| Final Exam | 15% |
CUNY SPS UNDERGRAD GRADING SCALE
| Letter Grade | Ranges % | GPA |
|---|---|---|
| A | 93 - 100 | 4.0 |
| A- | 90 - < 92 | 3.7 |
| B+ | 87 - < 90 | 3.3 |
| B | 83 - < 87 | 3.0 |
| B- | 80 - < 83 | 2.7 |
| C+ | 77 - < 80 | 2.3 |
| C | 73 - < 77 | 2.0 |
| C- | 70 - < 73 | 1.7 |
| D | 60 - < 70 | 1.0 |
| F | < 60 | 0.0 |
COURSE OUTLINE AND SCHEDULE
Subject to change
| Week | Topic |
|---|---|
| 1 | Introduction to R and RStudio |
| 2 | R coding basics |
| 3 | Data (importing and structure) |
| 4 | Data visualization with ggplot2 |
| 5 | Reshaping Data |
| 6 | Probability |
| 7 | Distributions |
| 8 | Foundation for inference |
| 9 | Central limit theorem |
| 10 | Inference for proportions |
| 11 | Inference for two-way tables |
| 12 | Inference for numerical data |
| 13 | Analysis of variance |
| 14 | Correlation |
| 15 | Wrap up / Final Presentations |
ACCESSIBILITY AND ACCOMMODATIONS
The CUNY School of Professional Studies is committed to making higher education accessible to students with disabilities by removing architectural barriers and providing programs and support services necessary for them to benefit from the instruction and resources of the University. Early planning is essential for many of the resources and accommodations provided. Please see: Disability Services on the CUNY SPS Website.
ONLINE ETIQUETTE AND ANTI-HARASSMENT POLICY
The University strictly prohibits the use of university online resources or facilities, including Blackboard, for the purpose of harassment of any individual or for the posting of any material that is scandalous, libelous, offensive or otherwise against the University’s policies. Please see: “Netiquette in an Online Academic Setting: A Guide for CUNY School of Professional Studies Students.”
ACADEMIC INTEGRITY
Academic dishonesty is unacceptable and will not be tolerated. Cheating, forgery, plagiarism and collusion in dishonest acts undermine the educational mission of the City University of New York and the students’ personal and intellectual growth. Please see: Academic Integrity on the CUNY SPS Website.
STUDENT SUPPORT SERVICES
If you need any additional help, please visit Student Support Services: Student Support Services.