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
- Not offered this term
- This course is not offered this term. Please check back next term or subscribe to receive notifications of future course offerings and other opportunities to learn more about this course and related programs.
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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