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 2022 term.
Sat Sep 17 - Sat Dec 10 (12 weeks)
- 12 weeks
- CRN 44117
Class meeting times
|Sep 17 - Dec 03||Sat||09:30 - 12:30||Online|
|Oct 29||Sat||09:30 - 12:30||Burnaby SE12 Rm. 310|
|Dec 10||Sat||09:30 - 12:30||Burnaby SE12 Rm. 310|
Course outline TBD — see Learning Outcomes in the interim.
***IMPORTANT INFORMATION READ CAREFULLY*** COURSE FORMAT IS SYNCHRONOUS ONLINE LECTURES AND HAS TWO MANDATORY IN-PERSON EXAMs FINAL exam will be on Saturday, December 10th, 2022 at 9:30am 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, Oct. 8th (Thanksgiving)
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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