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Introduction to Engineering Analysis

Course Code: ENGR 1180
Faculty: Science & Technology
Department: Engineering
Credits: 3.0
Semester: 15 weeks
Learning Format: Lecture, Lab
Typically Offered: TBD. Contact Department Chair for more info.
course overview

An introduction to MATLAB and its use in engineering. The course introduces basic features of MATLAB programming. MATLAB is used to implement and analyze various algorithms used in data analysis within the context of engineering disciplines such as signal and image processing, robotics and communications engineering.

Course Content

  1. Introduction to MATLAB
  2. Flow control and Data structures
  3. Plotting in MATLAB
  4. Strings and file input/output
  5. Complex numbers
  6. Combinatorics
  7. Linear Algebra
  8. Statistics and Data Analysis
  9. Polynomial approximation and curve fitting
  10. Root finding and numerical differentiation
  11. Numerical integration
  12. MATLAB executable files

Methods of Instruction

Lectures, Labs (using MATLAB), Assignments

Means of Assessment

Evaluation will be carried out in accordance with Douglas College policy.  The instructor will present a written course outline with specific evaluation criteria at the beginning of the semester.  Evaluation will be based on some of the following criteria:

  1. Labs    0 – 25%
  2. Tests    20 – 60%
  3. Assignments/Group work     0 – 20%
  4. Attendance    0 – 5%
  5. Course Project   0 - 20%
  6. Final examination    30 – 40%

Learning Outcomes

Upon completion of ENGR 1180 the student should gain enough familiarity with MATLAB to:

  • understand basic input and output syntax
  • create different object types such as variables, vectors, arrays, matrices and structures
  • perform operations on different object types using built-in commands
  • write scripts and functions to execute and simplify multiple commands
  • understand flow control including: if-then, for, while, break, try/catch, switch
  • create various plots types used in science and engineering, such as:  2D plots, 3D plots, subplots, overlaid plots, piecewise plots, bar plots, vector fields
  • use object handles to automate the task of modifying and formatting plots
  • understand strings; perform basic operations on them
  • import, export, read and write various file types such as: text, binary, Excel, image and video
  • understand the need for, and the use of, MEX files
  • understand various toolboxes used in engineering analysis
  • perform arithmetic operations on complex numbers; work with complex valued objects
  • compute permutations and combinations
  • perform arithmetic operations on vectors and matrices; calculate numerically the determinant and inverse of a matrix
  • solve numerically systems of linear and non-linear equations; solve numerically practical problems containing systems of equations
  • perform computations with the Binomial and Poisson probability mass functions for discrete random variables, the Normal probability density function and cumulative distribution function for continuous random variables
  • compute the mean, variance and standard deviation for a sample data set
  • perform linear regression and correlation analysis on a sample data set
  • perform polynomial interpolation on a sample data set
  • determine numerically the solutions to non-linear equations using the bisection method or the Newton-Raphson method
  • compute numerically the derivative of a function using finite-differences
  • compute numerically the value of an integral using various numerical quadrature methods: midpoint, trapezoid, Simpson’s rule, adaptive methods

course prerequisites

CMPT 1110

MATH 1120


The following courses must be completed prior to or at the same time as ENGR 1180:

MATH 1220

MATH 2232

curriculum guidelines

Course Guidelines for previous years are viewable by selecting the version desired. If you took this course and do not see a listing for the starting semester/year of the course, consider the previous version as the applicable version.

course schedule and availability
course transferability

Below shows how this course and its credits transfer within the BC transfer system. 

A course is considered university-transferable (UT) if it transfers to at least one of the five research universities in British Columbia: University of British Columbia; University of British Columbia-Okanagan; Simon Fraser University; University of Victoria; and the University of Northern British Columbia.

For more information on transfer visit the BC Transfer Guide and BCCAT websites.


If your course prerequisites indicate that you need an assessment, please see our Assessment page for more information.