vcc.ca
MATH 2705: Introduction to Data Science
Effective date
January 2026
Department
UT Math
School
Arts and Sciences
Description
This course serves as an introduction to developing key data science skills. By the end of the course, students will be able to implement a complete data science workflow using the R programming language. This includes downloading data from the internet (scraping), managing it effectively (wrangling), and creating tables and figures that tell a meaningful and justifiable story based on the data. Students will also gain proficiency in tools for identifying patterns in data and making predictions about future trends.
Credits
3.0
Year of study
2nd Year Post-secondary
Prerequisites
MATH 2700 with a minimum 'C+' grade or equivalent, and CMPT 1010 with a minimum 'C-' or equivalent.
Corequisites
None
Course Learning Outcomes
Upon successful completion of this course, students will be able to:
- Explain key concepts and the scope of data science, including its role in modern problem-solving and decision-making
- Write basic R scripts to perform data analysis tasks
- Utilize R libraries for data manipulation, visualization, and basic statistical operations
- Write SQL queries to retrieve, filter, and summarize data from relational databases
- Access and extract data from web sources using appropriate tools and techniques
- Import data from local files and web-based sources into R
- Work with diverse data formats, including CSV, JSON, and HTML
- Identify and handle missing, inconsistent, or duplicate data
- Transform and organize datasets to prepare them for analysis
- Generate effective data visualizations
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.
Case Study Analysis: Exploration of real-world examples.
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.
Case Study Analysis: Exploration of real-world examples.
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 |
|
|
Quizzes/Tests |
25-35 |
3-5 quizzes |
|
Project |
30 |
Final project includes project documentation (15%) presentation (10%) and peer feedback (5%) |
|
Participation |
5-10 |
Online and in-class activities |
Course topics
- Introduction to Data Science Introduction to R Programming Language Introduction to SQL and Relational Databases Introduction to Web and Data Technologies Reading in Data Locally and from the Web Cleaning and Wrangling Data Data Visualization
Learning resources
R and RStudio with web scraping libraries (Free to download and use)
MySQL Community Edition (Free to download and use)
Spreadsheet software (Microsoft Excel, included in student M365 license)
MySQL Community Edition (Free to download and use)
Spreadsheet software (Microsoft Excel, included in student M365 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.