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Advanced Statistical Techniques for Data Analytics MATH 3060

Mathematics Part-time Studies Course

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 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.

Prerequisite(s)

Credits

4.0

Cost

$746.61

Course offerings

Fall 2022

Below is one offering of MATH 3060 for the Fall 2022 term.

CRN 44117

Duration

Sat Sep 17 - Sat Dec 10 (12 weeks)

  • 12 weeks
  • CRN 44117
  • $746.61
Class meeting times
Dates Days Times Locations
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
Instructor

Takashi Nakamura

Course outline

Course outline TBD — see Learning Outcomes in the interim.

Cost

$746.61

Important information
  1. ***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)
Status

Full

This course offering is full. Please check back next term, subscribe to receive email updates or contact us with your comments or questions.

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

Advanced Statistical Techniques for Data Analytics (MATH 3060) is offered as a part of the following programs:

School of Computing and Academic Studies

  1. Applied Data Analytics
    Certificate Part-time

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  • Privacy Notice: The information you provide will be used to respond your request for BCIT course information and is collected under Section 26(c) of the Freedom of Information and Protection of Privacy Act (FIPPA). For more information about BCIT’s privacy practices contact: Associate Director, Privacy, Information Access & Policy Management, British Columbia Institute of Technology, 3700 Willingdon Ave. Burnaby, BC V5A 3H2, email: privacy@bcit.ca.