Data Analysis

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.

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. 

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. 

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. 

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. 

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. 

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.

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.

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.