CONTENT REPOSITORY COLLECTIONS SOFTWARE ENGINEERING

COURSES IN THE SOFTWARE ENGINEERING COLLECTION

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CSE 551

Foundations of Algorithms

Algorithms, or a step-by-step process to efficiently reach the desired goal, have been part of human history since the 1200s. Algorithms are a fundamental component of any computerized system. This is a ”second” course in algorithms. The goal of this course is to show you some useful algorithms and explain how they work and why they are considered good psychology and cognitive science to enhance the understanding of complex data.

CSE 578

Data Visualization

Amidst the information flood in which we are currently immersed, visualizations can be a well-placed treetop. The rise of big data has the potential to inform decisions, and visual representations can play a crucial intermediate role in our daily information consumption. Covers techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology and cognitive science to enhance the understanding of complex data.

IFT 101

Information Technology Programming Logic

IFT 101 introduces the student to basic concepts of problem-solving computer programming and logical structures. Topics include techniques for problem solving with computers, organizing a solution, program structure, and basic data structures. Topics also include organizational structures and where IT fits within them, and potential automation and informational descriptions (scenarios) of those domains. Graphical and textual models will be used to express the logic and Python will be used to implement the logic.

IFT 103

Operating System Architecture

This course introduces Linux, Windows and Mac operating systems, and basic computer networking and hardware security. It begins with an overview of the hardware of modern computer and operating systems, and it introduces user management, memory management, network and disc storage. It applies hands-on practices to computer and operating system virtualization, and provides an isolated environment to practice and do labs on advanced topics in the field of memory management, different portable data file input/output. Students learn Command Line Interface (Bash/Power Shell) to interact with computers and transfer data flies from one operating system to another operating system.

IFT 300

Intermediate Database Management Systems

This course provides an overview of database architecture, data modeling, and database management. Students will apply principles of database design and techniques of database application development, for efficient storage and query processing. Students will study the SQL query language in detail and gain practical knowledge of its semantics and transaction processing. Security and privacy issues will also be explored.

IFT 360

Applications in Artificial Intelligence

AI is a multidisciplinary area comprising theoretical, experimental, and applied investigations of intelligent systems. Converging technologies along with natural language processing, big data and the Internet of Things (IoT) are driving the growth of AI. In this course, students learn about examples of AI in use today such as web crawlers, how humans detect financial frauds, self-driving cars, facial recognition systems, and natural language processors.

DAT 300

Mathematical Tools for Data Science

This course covers the core mathematical topics that underpin data science as well as some key algorithms used for modern data analysis and how to implement them in Python. At the completion of this course, students will be able to understand the concepts of probability and conditional probability, code in python to visualize functions and run simulations in python.

DAT 250

Data Science and Society

This course examines quantitative literacy from a data and evidence driven perspective. Looks at the literature behind vaccines, climate, and other contentious topics where there is a wealth of scientific literature and yet these areas are still hotly debated. Investigates ways in which data science is abused; how to mislead with statistics, and how these problems have created a lack of trust in science. Through class discussions, case studies and exercises, students learn the basics of ethical thinking in science, understand the history of ethical dilemmas in scientific work, and study the distinct challenges associated with ethics in modern data science.

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