Course Overview
This hands-on course is the final course and project for the Applied Data Analytics Certificate (ADAC) offered by BCIT Computing. It follows on from COMP 2853 - Introduction to Python for Data Analysis, COMP 2854 - Data Analytics Fundamentals. Students apply previously learned tools and technologies used throughout the program, building on statistics for data mining and modelling. They will use Python to apply statistical methods and machine learning to develop a significant analytics project. COMP 4254 may also be completed concurrently with or after COMP 2453- Introduction to Microsoft Fabric for Data Analytics and COMP 3854 - AWS Generative AI for Data Analytics. Students practice industry-standard tools and processes to address real-world challenges in obtaining, cleaning, and analyzing large diverse data sources. They will experience how to model a variety of structured and unstructured data sources to analyze and visualize data. Activities and assignments analyze large data sets, written text, databases, images, and video to conduct predictive and prescriptive analytics. Labs and exercises use Python with industry standard tools and methods to address challenges in analyzing disparate data sources. Introductory development with transformer-based models and large language models (LLMs) is explored for text classification modelling, summarization, and using AI-based chat applications. Machine learning techniques are used to develop predictive models and select the appropriate frameworks for managing data. There is a comparison of the effectiveness of different tools for different data analytics problems. COMP 4254 uses a combination of Python, and Data Analysis tools and visualization techniques integrated into an advanced data analytics project. Upon successful completion, students will be able to develop machine learning (ML) enhanced predictive models for in-depth analyses on a broad range of data sources.
Registration requirements
Departmental approval is required to register for this course. You must have approval from the department to register. Students accepted into a program can register directly.
Domestic fees
$708.21
International fees are typically 3.4 times the domestic tuition. Exact cost will be calculated upon completion of registration.
Learning Outcomes
Upon successful completion of this course, the student will be able to:
- Apply Machine Learning (ML) techniques to develop predictive models.
- Develop with generative AI models, transformer models, and natural language processing to perform text classification, summarization, and chat tasks.
- Implement practical applications of clustering.
- Apply advanced algorithms for data preparation for modelling and analysis.
- Compare and evaluate multiple data preparation methods and model types and parameters for optimization.
- Use predictive models, visualization, and analysis for guiding organizational decisions.
- Create an effective variety of visualizations for exploration, statistical validation, and reporting.
- Select the appropriate frameworks for managing data locally and in distributed data environments.
- Apply data mining techniques for knowledge extraction in Python.
- Apply tools and methods to address challenges in obtaining, cleaning, and analyzing large disparate data sources.
- Develop an advanced data analytics project.
Effective as of Winter 2026
Related Programs
Advanced Topics in Data Analytics (COMP 4254) is offered as a part of the following programs:
- Indicates programs accepting international students.
- Indicates programs eligible for students to apply for Post-graduation Work Permit (PGWP).
School of Computing and Academic Studies
- Applied Data Analytics
Certificate Part-time
Course Offerings
Fall 2026
Below is one offering of COMP 4254 for the Fall 2026 term.
CRN 44987
Dates
Sep 11 - Nov 27 Loading
- CRN 44987
- $708.21 Domestic fees
Class meeting times
| Dates | Days | Times | Locations |
|---|---|---|---|
| Sep 11 - Nov 27 | Fri | 18:00 - 21:00 | Downtown DTC Rm. 300 |
Duration
12 weeks
Instructor
Pat McGee
Course outline
Course outline TBD — see Learning Outcomes in the interim.
Important information
- Please email cstflex@bcit.ca for Departmental approval. Include your Student number (A0#) and COMP__ and preferred CRN __ and Program Declaration____. Course is 36 hours on campus. Please Note: Attendance, participation in class and the BCIT Learning Hub are mandatory. You should prepare to spend 2-3 hours on homework for every 1 hour of class time. This course may require an average total time commitment of 9-12+ hours per week. 3 hours of synchronous class time and 6-9+ hours per week for homework. Late registration is not permitted. BCIT Computing is primarily a Microsoft Windows environment. Students must provide their own current model Windows-compatible PC with microphone and video camera. i5 or higher equivalent processor, with 8 GB of RAM minimum, and 256 GB minimum storage. Highspeed internet access is needed for online sections and for homework. Mac users must have the ability to manage and support their iOS computer. They may need to create a virtual Windows environment using Parallels or VMWare Fusion. COMP instructors may not be able to assist Mac users with software compatibility issues. BCIT does not provide access to Parallels or support for students to use a Mac to run Windows.
Confirmation
Required
To proceed with registration and add this course to the cart, please confirm:
No approval yet? Request approval You must have approval from the department to register. Students accepted into a program can register directly.
Status
If you have any questions about this course, please contact us.
Programs and courses are subject to change without notice. Find out more about BCIT course cancellations.