DATA 200
Introduction to Data Science with R
3 Credits          Weekly (2-2-0)

This course introduces the fundamental concepts of data science using the R programming language. Students will learn how to acquire, clean, manipulate, visualize, and analyze data, as well as build predictive models and extract valuable insights from data sets.

Prerequisites: Minimum grades of C- in CMPT 103 and one of STAT 151 or STAT 161.

DATA 220
Introduction to Data Systems and Tools
3 Credits          Weekly (3-2-0)

This course introduces the systems and tools used in data science, covering techniques for ingesting, processing, analyzing, and visualizing data at scale. Key computational environments are explored, including cloud platforms and command-line interfaces. Students will gain hands-on experience with modern data science tools and platforms, including scripting techniques, programming, version control systems, and data visualization methods. The course also explores various data sources and storage models, emphasizing ethical practices and privacy considerations in data management.

Prerequisites: A minimum grade of C- in CMPT 103.

DATA 351
Introduction to Sampling and Experimental Design
3 Credits          Weekly (3-1-0)

This course introduces students to design and analysis of common sample surveys and experimental designs. Topics include: designing sample surveys, probability sampling, simple random sampling, stratified random sampling, principles of design, completely randomized designs, randomized complete block designs, factorial design, and two-level factorial design. Note: credit cannot be obtained in STAT 350/STAT 353 and in DATA 351.

Prerequisites: A minimum grade of C- in STAT 266.

DATA 495
Special Topics in Data Science
3 Credits          Weekly (3-1-0)

In this course, students examine an advanced topic in one or a combination of Computer Science, Mathematics, or Statistics. Topics vary and are announced prior to registration. Consult with faculty members in Mathematics, Statistics, or Computer Science for details regarding current offerings. Note: This course may be taken up to three times for credit, provided the topic is different each time.

Prerequisites: Minimum grade of B- in a 300-level STAT, MATH, or CMPT course and consent of the department.

DATA 496
Data Science Capstone Project
3 Credits          Total (45-0-60)

The Data Science Capstone Project is the culminating experience for students pursuing a Data Science major. This course integrates the knowledge and skills acquired throughout the program and allows students to apply data science techniques to real-world problems. Students complete a comprehensive data science project from inception to completion, including problem formulation, data collection, preprocessing, analysis, modelling, and interpretation of results. Students must demonstrate their ability to effectively communicate complex technical concepts to a non-technical audience through written reports and presentations. Additionally, ethical considerations and best practices in data science, including data privacy, transparency, and reproducibility, will be incorporated throughout the course.

Prerequisites: A minimum grade of C- in at least two 300-level CMPT, DATA, MATH, or STAT courses, and consent of the department.

DATA 497
Data Science Internship
3 Credits          Total (0-0-135)

This course provides students with practical experience in data science by engaging in work-integrated learning through employment or internship with an external organization. Any placement needs department approval. After the successful completion of the placement, there is a critical analysis/demonstration of the learning accomplished. The contact hours are a minimum of 90 hours but can involve more depending on the placement.

Prerequisites: A minimum grade of C- in at least two 300-level STAT, MATH or CMPT courses and consent of the department.