Data Analysis

NOTE: See the beginning of Section F for abbreviations, course numbers and coding.

DA2503Packaged Software Decision Aids4 ch (3C 1T)

Examines typical software packages present in information centres and other business environments. Includes selected topics from the following areas: operating systems; network administration; communication software; wordprocessing; spreadsheets; database management systems and graphics.

Prerequisite: 30 ch of university courses including one of IT 1803, CS 1003, or CS 1073.

DA2704Data Analytics using Python (Cross-listed: CS 2704) (A)4 ch (3C 1L) [P]
This course teaches data-driven problem solving. Starting from installing a Python programming environment, students will learn reading data, producing graphs, hypothesis testing and Bayesian statistics with hands-on programming experience. The course is also a stepping stone to more advanced subjects, such as machine learning and AI. Although no prior programming experience is required, there is a substantial programming component to the course.

Prerequisite: STAT 1793 or STAT 2593 or STAT 2263 or BA 1605 or PSYC 2901.
DA2714Text Analytics (Cross-listed: CS 2714) (O)3 ch (3C)
Introduction to the analysis of textual data with a foundation on natural language processing and computational linguistics. Students will learn to develop information extraction pipelines and evaluate performance.

Prerequisites: DA 2704, CS 1083, CS 1103
DA3053Mathematical Software4 ch (3C 1T)

Advanced software packages and programming languages developed for mathematical computations: symbolic, graphical, numerical and combinatorial. Students will be involved in implementing and testing various algorithms. 

Prerequisites: MATH 2003, MATH 1503, or CS 1073.

DA3123Numerical Treatment of Geometric Modeling4 ch (3C 1T)

Presents the nature, development and application of the basic concepts of geometric modeling. The parametric geometry is considered primarily for curves including analytical properties, intersections and transformation. Emphasizes numerical methods and analysis with applications being drawn from such areas as image processing, graphics and computer-aided design. 

Prerequisite: CS 3113.

DA3203Data Analysis Using Statistical Software Packages4 ch (3C)

This is a case-studies based course in which students learn to analyse data in a modern statistical computing environment. The course promotes the use of graphical and other exploratory techniques as a crucial first step in data analysis. Students will be exposed to practical problems often encountered during the data analysis process. The importance of summarizing and communicating results effectively will be emphasized through the strong project-oriented component of the course. 

Prerequisites: 3 ch in each of three subjects: Mathematics, Statistics, and Computer Science. 

DA4123Numerical Solution of Systems4 ch (3C 1T)

Emphasis on linear systems with discussion on topics such as large, small; sparse, full; square, nonsquare systems. Methods of solution involve a survey of direct and interactive techniques. As time permits, the discussions will be extended to include nonlinear systems. Applications drawn from statistics and operations research. Both writing computer programs and working with stored computer programs form an integral part of the course. 

Prerequisite: CS 3113.

DA4403Data Mining (O) (Cross-Listed: CS4403)4 ch (3C 1L)

Data mining (aka knowledge discovery) is an interdisciplinary area of computer science with the goal of extracting new knowledge and insights from big and complex data sets. The course introduces essential pattern recognition methodologies leveraging machine learning and rule-based techniques. Supplementary tasks involving processing, cleaning, integration, and transformation of data are also covered. An etymology of data mining is provided to help students compare and contrast knowledge discovery with contemporary data analytics and decision support methodologies.

Prerequisites: CS 1103, CS 2704 and (STAT 2593 or STAT 2793).

DA4803Independent Studies in Data Analysis4 ch (3C 1T)

Discussion of Data Analysis topics at an advanced level chosen jointly by student, advisor and Department Chair. Topic of course to be entered on the student’s transcript.

DA4813Independent Studies in Data Analysis4 ch (3C 1T)

Discussion of Data Analysis topics at an advanced level chosen jointly by student, advisor and Department Chair. Topic of course to be entered on the student’s transcript.

DA4993Project in Data Analysis4 ch (2S) [W]

Application of correct and appropriate methods of data analysis in one or more areas. A project proposal is required with a final report in which the student describes clearly and concisely the work done, the results obtained, and a careful interpretation of the results in form and language meaningful to workers in the subject area. Students in the Certificate of Data Analysis should choose an industry-related or applied project involving a large amount of data. It should be noted that such a project may require extra time in order to become familiar with the data at hand. 

Prerequisite: Permission of Program Director.