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MATH 3060 - Advanced Statistical Techniques for Data Analytics

Mathematics Part-time Studies Course

School of Computing and Academic Studies

Course Details

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 the Applied Data Analytics Certificate offered by BCIT Computing.

Prerequisite(s)

MATH 1060 or equivalent.

Credits

4.0

Cost

$670.71

Winter 2018

Below is one offering of this course for the Winter 2018 term.

CRN 85238

Wed Jan 10 - Wed Mar 28 12 Weeks

Class Meeting Times

Dates Days Times Locations
Jan 10 - Mar 28 Wed 18:00 - 20:00 BBY SW01 Rm. 1205
Jan 10 - Mar 28 Wed 18:00 - 21:00 BBY SE06 Rm. 107
Jan 10 - Mar 28 Online

Instructor

Takashi Nakamura

Course Outline

Cost

$670.71

Notes

  1. This course consists of a total of 4 hours per week - 3 hours in class plus 1 hour as an online component. Course is a total of 48 hours.

This course offering is in progress. Please check this page for other currently available offerings or subscribe to receive email updates.

In Progress

Spring/Summer 2018

Below is one offering of this course for the Spring/Summer 2018 term.

CRN 65337

Wed Apr 11 - Wed Jun 27 12 Weeks

Class Meeting Times

Dates Days Times Locations
Apr 11 - Jun 27 Wed 18:00 - 21:00 BBY SW01 Rm. 2016
Apr 11 - Jun 27 Online

Instructor

Takashi Nakamura

Course Outline

TBD – see Learning Outcomes in the interim

Cost

$670.71

Notes

  1. This course consists of a total of 4 hours per week - 3 hours in class plus 1 hour as an online component. Course is a total of 48 hours.

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.
  • 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

Related Programs

MATH 3060 is offered as a part of the following programs:

School of Computing and Academic Studies

  1. Applied Data Analytics
    Part-time   Certificate

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