vcc.ca
MATH 2710: Introduction to R for Data Science
Effective date
January 2026
Department
UT Math
School
Arts and Sciences
Description
This course introduces students to data science using the R programming language. Students will learn fundamental programming concepts while developing skills in data manipulation, visualization, and analysis. They will also gain practical skills in handling real-world data and applying foundational data science methods.
Credits
3.0
Year of study
2nd Year Post-secondary
Prerequisites
MATH 2700 with a minimum grade of 'C+' or equivalent.
Corequisites
None
Course Learning Outcomes
Upon successful completion of this course, students will be able to:
- Write and execute R scripts to analyze and manipulate data.
- Visualize data effectively using R libraries to identify patterns and trends.
- Perform data wrangling tasks, including importing, cleaning, and organizing datasets.
- Summarize data using statistical measures and exploratory techniques.
- Transform messy datasets into tidy formats for analysis.
- Utilize R for generating actionable insights from real-world data.
- Communicate results through clear visualizations and summaries.
Prior Learning Assessment & Recognition (PLAR)
None
Hours
Lecture, Online, Seminar, Tutorial: 30
Clinical, Lab, Rehearsal, Shop, Kitchen, Simulation, Studio: 30
Total Hours: 60
Clinical, Lab, Rehearsal, Shop, Kitchen, Simulation, Studio: 30
Total Hours: 60
Instructional Strategies
Lectures: Interactive sessions with coding demonstrations.
Lab Work: session with hands-on exercises to apply data science concepts in R and SQL.
Problem-Based Learning: Engage students with practical, data-focused, and real –world problems.
Peer learning and assessment: Collaborative activities for peer learning, teamwork, and mutual feedback.
Lab Work: session with hands-on exercises to apply data science concepts in R and SQL.
Problem-Based Learning: Engage students with practical, data-focused, and real –world problems.
Peer learning and assessment: Collaborative activities for peer learning, teamwork, and mutual feedback.
Grading System
Letter Grade (A-F)
Passing grade
D
Evaluation Plan
|
Type |
Percentage |
Assessment activity |
|
Assignments |
25-35 |
|
|
Midterm Exam |
15-20 |
Midterm 1 |
|
Midterm Exam |
15-20 |
Midterm 2 |
|
Final Exam |
20-35 |
|
|
Participation |
10-15 |
Online and in-class activities |
Course topics
- Data exploration and visualization Customizing visualizations: colors, themes, and labels Data transformation and summarization Filtering, sorting, and summarizing data Grouped operations and aggregations for exploratory data analysis Calculating statistical summaries Data wrangling Importing data from various file formats Cleaning messy datasets: handling missing values, duplicates, and inconsistencies Reshaping and organizing data Data types and structures Understanding common data types in R Working with vectors, matrices, data frames, and tibbles Handling categorical data with factors and managing date-time formats. Workflow for data analysis Setting up and organizing RStudio projects. Writing and running R scripts for reproducible analysis. Writing custom functions to automate repetitive tasks. Basic control structures: if statements and loops (for, while). Debugging and error handling.
Learning resources
R and RStudio with the Tidyverse package (Free to download and use)
Spreadsheet software (Microsoft Excel, included in M365 student license)
Spreadsheet software (Microsoft Excel, included in M365 student license)
Notes:
- Course contents and descriptions, offerings and schedules are subject to change without notice.
- Students are required to follow all College policies including ones that govern their educational experience at VCC. Policies are available on the VCC website at:
https://www.vcc.ca/about/governance--policies/policies/. - To find out if there are existing transfer agreements for this course, visit the BC Transfer Guide at https://www.bctransferguide.ca.