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MATH 3060 - Advanced Statistical Techniques for Data Analytics

Mathematics Part-time Studies Course

School of Computing and Academic Studies

To support you and your education, BCIT is adapting applied learning in formats appropriate to the unfolding global situation. Spring and Summer PTS classes are being delivered in an online format unless you are notified otherwise.

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.


MATH 1060 or equivalent.





Fall 2020

Below is one offering of this course for the Fall 2020 term.

CRN 44117

Sat Sep 12 - Sat Dec 05 13 Weeks

Class Meeting Times

Dates Days Times Locations
Sep 12 - Dec 05 Sat 09:30 - 12:30 BBY
Sep 12 - Dec 05 Online


Vahid Dabbaghian

Course Outline

TBD – see Learning Outcomes in the interim




  1. 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. 10th (Thanksgiving).
  2. Important course information will be sent to you prior to your course start date. Check your myBCIT email account to access this information.

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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BCIT's Part-time Studies English Language Proficiency Standard is English 12 (or equivalency). Students who do not meet this requirement should upgrade their English skills prior to any future registration.

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