Course Overview
This hands-on course follows on from MATH 1060 - Statistics for Data Analysis and introduces advanced techniques and tools used in the field of data analytics. Through a variety of exercises, students will practice using general classification and predictive analysis methods. Labs and projects will use R Programming language packages as a statistical analysis tool for advanced statistical computing and data mining techniques. Scientific data analysis methods will also be discussed. Participants have an introduction to mathematical principles of Artificial Intelligence and Machine Learning (AI/ML). MATH 3060 is a prerequisite for COMP 4254-Advanced Topics in Data Analysis and a required course in BCIT Computing's Applied Data Analytics Certificate (ADAC). Upon successful completion, students will be able to effectively collect and explore large and small data sets to apply appropriate statistical methods for data mining, and data analysis to discover patterns and trends with a focus on data quality.
Prerequisite(s)
- 60% in MATH 1060
Credits
4.0
Domestic fees
$793.06
International fees are typically 3.25 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:
- Compute confidence regions and perform basic hypothesis testing with multivariate normal distributions.
- Perform dimensionality reduction of high-dimensional data sets using principal component analysis.
- Describe principles of various regression and classification/clustering algorithms.
- Describe different clustering algorithm methods, with a focus on K-Means Clustering and DBSCAN Clustering.
- Describe principles of various time series analysis methods.
- Apply appropriate time series analysis for problems at hand.
- Apply basic Bayesian statistics in decision making and risk assessment.
- Describe principles of neural network algorithms.
- Use the R Programming language packages for statistical computing, data mining and data analysis.
- Describe basic mathematical principles of Artificial Intelligence and Machine Learning (AI/ML).
Effective as of Spring/Summer 2025
Related Programs
Advanced Statistical Techniques for Data Analytics (MATH 3060) 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
Spring/Summer 2026
Below is one offering of MATH 3060 for the Spring/Summer 2026 term.
CRN 65337
Dates
Apr 11 - Jun 27 Loading
- CRN 65337
- $793.06 Domestic fees
Class meeting times
Duration
12 weeks
Instructor
Mengdi Hua
Course outline
Important information
- **IMPORTANT: Prerequisite is MANDATORY and will be verified - NO EXCEPTIONS. Course information will be sent to you prior to your course start date. Check your myBCIT email account access this information. If you have any questions about your pre-requisite, please email mathpts@bcit.ca. THIS COURSE WILL BE DELIVERED WITH ONLINE LECTURES AND IN-PERSON TESTS AT THE DOWNTOWN CAMPUS. On the start date of your class (not before), log into learn.bcit.ca to access your course link. This class meets once per week for 3 hours in a virtual classroom AND requires an additional 1 hour per week as an online component. Course is 48 hours – 36 in class and 12 online. Midterm: Saturday, May 16th, 2026 (DTC) Final: Saturday, June 27th, 2026 (DTC)
Confirmation
Required
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Status
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