CISC 271/3.0 Linear Data Analytics

Original Author: Randy Ellis
Last Revised: 2019-03-20

Calendar Description

Elements of linear algebra for data analysis, including: solution of linear equations; vector spaces; matrix decompositions; principal components analysis; linear regression; hyperplane classification of vectorial data.

Prerequisites: Level 2 or above and C- in {[CISC 101/3.0 or CISC 121/3.0] and [MATH 110/6.0 or MATH 111/6.0 or MATH 112/3.0] and [MATH 120/6.0 or MATH 121/6.0 or (MATH 123/3.0 and MATH 124/3.0) or MATH 126/6.0].

Exclusions: No more than 3.0 units from CISC 271/3.0; MATH 272/3.0; PHYS 213/3.0; PHYS 313/3.0.

Learning hours: 120 (36L; 84P)

  • This course is a direct prerequisite to:
    • CISC 371/3.0 (Nonlinear Optimization)
    • CISC 471/3.0 (Computational Biology)
  • Degree Planning

    • This course is required for the Biomedical Computation focus and the Data Analytics focus of the COMP degree plan.
    • This course also satisfies a requirement for the Game Development focus of the COMP degree plan.

    Possible Texts

    • Gilbert Strang. Introduction to Linear Algebra, 5th edition. Wellesley Cambridge Press. ISBN: 9780980232776


    This course will explore techniques for analyzing sets of data that are in vectors. These techniques are primarily the use of linear algebra, which will be applied to data gathered by empirical studies. To test, implement, and analyze this material, MATLAB will be used as an interactive tool and programming language. Students are expected to learn basic MATLAB on their own. Some tutorial information will be provided early in the course.

    For basic material in data analytics, students can expect to be instructed in:

    • organization of data into vectors and matrices
    • description of vectors in a space
    • minimal space descriptors
    • linear relations
    • spaces of solutions to linear relations

    For basic material in machine learning, students can expect to be instructed in:

    • linear regression, including methods based on the QR decomposition
    • data reduction by principal components analysis
    • methods based on the singular value decomposition
    • methods based on decision hyperplanes, including artificial neurons and support vectors