This hands-on course follows on from MATH 1060 - Statistics for Data Analysis and introduces the students to many of the techniques used in the field of data analytics. This introduction will enable students to use general classification and predictive analysis methods. Methods appropriate for scientific data are also discussed. Labs and projects using the open source statistical analysis tool, R, build on the skills learned in Math 1060 to apply these advanced techniques. Upon successful completion, students will be able to effectively analyze large and small data sets while adhering to sound statistical principles. This course is required for COMP 4254 in the Applied Data Analytics Certificate offered by BCIT Computing. MATH 3060 will be offered only in the fall (September) and winter (January) terms.
- 60% in MATH 1060
Below is one offering of MATH 3060 for the Fall 2023 term.
Sat Sep 09 - Sat Dec 09 (14 weeks)
- 14 weeks
- CRN 44117
Class meeting times
|Sep 09 - Dec 09||Sat||09:30 - 12:30||Online|
|Oct 21||Sat||09:30 - 12:30||Burnaby|
|Dec 09||Sat||09:30 - 12:30||Burnaby|
Course outline TBD — see Learning Outcomes in the interim.
- Important course information will be sent to you prior to your course start date. Check your myBCIT email account to access this information.
***IMPORTANT INFORMATION READ CAREFULLY*** COURSE FORMAT IS SYNCHRONOUS ONLINE LECTURES AND HAS TWO MANDATORY IN-PERSON EXAMs at the Burnaby Campus. 1. Midterm Exam: Saturday, October 21st, 2023 2. Final Exam: Saturday, December 9th, 2023 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 Note: No class on Saturday, September 30th (National Day for Truth and Reconciliation), Saturday, November 11th (Remembrance Day)
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.
- Apply appropriate classification algorithms for problems at hand.
- 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.
- Adopt constantly-evolving new tools (algorithms).
Effective as of Fall 2016
Advanced Statistical Techniques for Data Analytics (MATH 3060) is offered as a part of the following programs:
School of Computing and Academic Studies
- Applied Data Analytics
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Programs and courses are subject to change without notice.