Welcome! This course, offered by the University of Toronto's Data Sciences Institute, focuses on the use of tools and skills needed to handle the extensive data generated by advancing information technology. One prominent tool is R, a freely available open-source language and environment specifically designed for data science. The course provides comprehensive coverage of R and data science topics, demonstrating their practical applications using RStudio.
- Description
- Learning Outcomes
- Assignments
- Contacts
- Delivery of the Learning Module
- Requirements
- Resources
- Folder Structure
This course introduces programming using R with a focus on manipulating and visualizing data, and discusses related ethical and professional topics in data science. Participants will set up a functional RStudio workflow, import and manipulate data, use function and loops, create data visualizations, and learn how to solve problems with their programming in a reproducible way. Both base R and tidyverse methods are introduced.
This course is designed for those who have a degree in something other than Computer Science/Statistics who are looking to enhance their data science skills for their career.
By the end of this course, participants will be able to:
- Understand how R fits into data science tools ecosystem
- Import, describe, and manipulate datasets
- Visualize patterns in data with a range of charts and plots
- Diagnose and fix errors in R
- Understand informed consent in data-based studies
- Manage and deliver projects using R and related tools
Participants should review the Assignment Submission Guide for instructions on how to complete assignments in this module.
There are two assignments in this module:
- Assignment 1 (Rmd modifiable template) Due (FILL IN due date) at 11:59 PM EST
- Assignment 2 (Rmd modifiable template) Due (FILL IN due date) at 11:59 PM EST
Assignments Assignments will be introduced in class, can be discussed in tutorial, and questions can be asked of the Instructor or Course Support over email. Assignments are due by midnight. Please arrange for extensions in advance with the Instructor or Course Support. Please submit assignments via Google Form, as an RMarkdown PDF, titled DSI-IntroR: Assignment X, Name. The assignments can be located in the Assessment directory, or below. You will find an .pdf file (knitted Markdown file) for convenient reading purposes, as well as an .Rmd file that can be modified and submitted. To download the files, click on "Raw" and select "Save as." Please note, assignments will be graded as Pass/Fail based on learner's demonstration of learning outcomes (see Assignment's grading rubrics for further details).
Questions can be submitted to FILL IN channel-name on Slack
- Technical Facilitator:
- FILL IN name: email address
- Learning Support Staff:
- FILL IN name: email address
- FILL IN name: email address
This course includes approximately 20 hours of in-class instruction, and 8 hours of optional but highly recommended work periods.
Learning sessions are scheduled FILL IN dates and times. During live learning sessions, the Technical Facilitator will introduce and explain key concepts and demonstrate core skills. Learning is facilitated during this time. Before and after each live learning session, the instructional team will be available for questions related to the core concepts of the module.
Optional work periods are conducted FILL IN dates and times. Optional work periods are to be used to seek help from peers, the Learning Support team, and to work through the practice problems and assignments in the learning module, with access to live help. Content is not facilitated, but rather this time should be driven by participants. We encourage participants to come to these work periods with questions and problems to work through.
Participants are encouraged to engage actively during the learning module. They key to developing the core skills in each learning module is through practice. The more participants engage in coding along with the instructional team, and applying the skills in each module, the more likely it is that these skills will solidify.
.
├── .github
├── 01_materials/slides
├── 02_activities
├── 03_instructional_team
├── 04_this_cohort
├── 05_src/data
├── .gitignore
├── LICENSE
└── README.md- .github: Contains issue templates and pull request templates for the repository.
- materials/slides: Module slides and interactive notebooks (.Rmd files) used during learning sessions.
- activities: Contains graded assignments, exercises, and homework to practice concepts covered in the learning module.
- instructional_team: Resources for the instructional team.
- this_cohort: Additional materials and resources for this cohort, including live coding files.
- src/data: Source code, databases, logs, and required dependencies (requirements.txt) needed during the module.
- .gitignore: Files to exclude from this folder, specified by the Technical Facilitator
- LICENSE: The license for this repository.
- README: This file.
- Participants are not expected to have any coding experience; the learning content has been designed for beginners.
- Participants are encouraged to ask questions, and collaborate with others to enhance their learning experience.
- Participants must have a computer and an internet connection to participate in online activities.
- Participants must not use generative AI such as ChatGPT to generate code in order to complete assignments. It should be used as a supportive tool to seek out answers to questions you may have.
- FILL IN installation of R and RStudio
- We expect participants to have completed the instructions mentioned in the onboarding repo.
- We encourage participants to default to having their camera on at all times, and turning the camera off only as needed. This will greatly enhance the learning experience for all participants and provides real-time feedback for the instructional team.
- Wickham and Grolemund, 2017, R for Data Science, O'Reilly
- Alexander (eds), 2021, DoSS Toolkit
- Alexander, 2022, Telling Stories with Data, CRC Press.
- de Graaf, 2019. Managing Your Data Science Projects: Learn Salesmanship, Presentation, and Maintenance of Completed Models, Apress.
- Healy, 2018. Data Visualization: A Practical Introduction, Princeton University Press
- Timbers et al., 2021. Data Science: A First Introduction.
- Wickham, 2021. Mastering Shiny, O'Reilly.
- [Wiley, Matt, Wiley, Joshua F., 2020.](Advanced R 4 Data Programming and the Cloud)
- Using PostgreSQL, AWS, and Shiny, Apress.
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Slides are adapted from Anjali Silva, originally from Amy Farrow under the supervision of Rohan Alexander, University of Toronto. Slides have been created and modified by Julia Gallucci for Summer 2023.
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We wish to acknowledge this land on which the University of Toronto operates. For thousands of years it has been the traditional land of the Huron-Wendat, the Seneca, and most recently, the Mississaugas of the Credit River. Today, this meeting place is still the home to many Indigenous people from across Turtle Island and we are grateful to have the opportunity to work on this land.