Information Technology

ITE 101 - Management Information Systems (3 credits)

This course will focus on providing an understanding of how information technologies gather, store, process, and communicate information. The course combines a conceptual understanding of the technology necessary for success in the information age, along with an understanding of the hardware and software required for an organization to successfully utilize technology. Attention will also be given to the legal, social, and ethical uses of technology.

ITE 105 - Computer Information Systems (3 credits)

This course is designed to provide a comprehensive foundation in the principles and practices of information systems. Focused on the intersection between business and technology, this course offers an exploration of hardware, software, databases, network architectures, cybersecurity, and systems analysis. Participants gain skills essential for navigating today's tech-driven business environments, including critical thinking, problem-solving, and effective communication in IT settings. This course caters to both beginners and those looking to update their knowledge, ensuring a robust understanding of the strategic role of information systems in organizational success.

ITE 107 - Integrated IT Syst/Emerging Tech (3 credits)

This course is designed to build upon foundational knowledge, diving deeper into complex topics such as database management, advanced networking, cybersecurity practices, systems analysis, and software development. Topics include SQL database queries, object-oriented programming concepts, network infrastructure design, risk assessment strategies, and the integration of emerging technologies into existing systems. Progress is assessed through quizzes, assignments, and a project.Prerequisite(s): ITE 105

ITE 111 - Digital Literacy in 21st Century (3 credits)

Digital Literacy for the 21st Century: Navigating the Digital Landscape is an 8-week course designed to equip students with essential knowledge and skills to thrive in the digital age. This course covers various aspects of digital literacy, including digital tools and online communications technology, artificial intelligence, machine learning, and generative artificial intelligence, cybersecurity, and ethical considerations involved with cutting edge digital tools. Students will gain a general understanding of these topics and obtain the ability to explain the key concepts and apply them to an examination of how such technology may impact their field of study.

ITE 115 - Program Logic & Design with Python (3 credits)

Students will learn the foundational logic for developing software applications. Topics will include data types, variables, I/O and associate formatting, data containers, functions and libraries, decisions, repetition, files and an introduction to classes and object-oriented programming (OOP). Skills will be reinforced using numerous coding exercises. Python will be the primary learning language used to convey the fundamentals.

ITE 117 - Intro to Databases & SQL Program (3 credits)

This course will focus on the design and implementation of SQL and NoSQL databases. Topics include how to design, develop, and implement relational database management systems to solve business problems as well as NoSQL systems.

ITE 203 - Introduction to R Programming (3 credits)

This course provides students with a foundational understanding of R, a powerful language for statistical computing and data analysis. The course covers essential programming concepts in R, including data structures, control flow, and functions, while emphasizing its use for statistical analysis and data visualization. Students will learn how to manipulate datasets, perform basic statistical tests, and create compelling visualizations. By the end of the course, students will be able to use R to efficiently analyze and interpret data, making it an essential tool for data science and research applications.

ITE 204 - Data Preparation & Processing (3 credits)

Data Preparation & Processing focuses on the critical steps involved in preparing raw data for analysis and ensuring its quality and usability. The course covers techniques for cleaning, transforming, and organizing data, including handling missing values, outlier detection, normalization, and feature engineering. Students will explore methods for working with various data types and formats, such as structured, unstructured, and semi-structured data. Additionally, the course emphasizes the importance of data preprocessing for ensuring accurate and reliable results in data analysis and machine learning applications. By the end of the course, students will be able to effectively prepare datasets for analysis in real-world scenarios. Prerequisite(s): DAT 201

ITE 211 - Data Structures and Algorithms (3 credits)

This course provides an in-depth exploration of fundamental data structures and algorithms, essential for efficient problem solving and software development. It is designed for students with a solid foundation in programming who aim to enhance their understanding of how data can be organized and manipulated to optimize performance and resource usage. Prerequisite(s): ITE 115

ITE 215 - Software Develop Method & Languages (3 credits)

This comprehensive course covers a spectrum of software development methodologies, programming languages, and secure coding practices. Students will gain practical experience with Waterfall and Agile methodologies, explore the daily workflows of professional developers, and develop proficiency in key programming languages such as C/C++, Java, C#, Go, and Rust. Emphasis will also be placed on secure coding techniques to protect applications from vulnerabilities and threats. (Formerly Titled: Software Development Process Overview) Prerequisite(s): ITE 211

ITE 217 - Object Oriented Prog/Architectures (3 credits)

This course delves into the principles and practices of object-oriented programming (OOP) and software architecture. Students will gain a solid understanding of OOP concepts, design patterns, and the fundamentals of building scalable and maintainable software architectures. The course will cover advanced topics such as architectural styles, design principles, Test-Driven Development and the modeling of software solutions. Prerequisite(s): ITE 211

ITE 220 - Networking & Data Communications (3 credits)

This course will explore how networks connect multiple devices and allow them to communicate. Topics include: the Transmission Control Protocol / Internet Protocol (TCP/IP) model and network hardware, like routers and modems. It will also focus on network-level vulnerabilities, and explain how to secure a network using firewalls, system hardening, and virtual private networks. (3 credits) Pre-requisite: CSS 101 or ITE 101.

ITE 225 - Computer Organization (3 credits)

This course will focus on the basics of computer organization and architecture. Topics include: Boolean algebra, combinational and sequential circuit design, storage mechanisms and their organization, the instruction cycle in a simple CPU, and the role of assembly language in understanding the hardware/software interface.

ITE 229 - Artificial Intelligence and Ethics (3 credits)

In this course, you will analyze the ethics of Artificial Intelligence (AI) in the fields of management, business, software development, information technology, and healthcare. You will use contemporary ethical frameworks, decision-making tools, and risk models to analyze case studies and evidence, and create evidence-based ethical guidelines and governance principles for the appropriate and productive use of AI in the workplace.

ITE 301 - Intro to AI and Generative AI (3 credits)

This course introduces students to fundamental principles, strategies, and practices necessary for working with and developing generative artificial intelligence (AI). Topics include generative AI models, prompt engineering, neural networks, and large language models. Students examine the use of generative AI in society, ethical issues related to generative AI, and implement AI models to solve problems in the domains of natural language processing and machine learning. Prerequisite(s): ITE 115

ITE 305 - Web-based Development (3 credits)

Web development is a dynamic and multifaceted field that encompasses the creation and maintenance of websites and web applications. As the internet has become an integral part of everyday life, the demand for skilled web developers has skyrocketed. This course aims to provide a comprehensive introduction to web development, covering the essential technologies and frameworks used in the industry. Students will gain a solid foundation is HTML, CSS, Javascript, Django, PHP, and well as databases and the technologies powering the Internet. Prerequisite(s): ITE 115, ITE 211

ITE 307 - Data Analysis with Python (3 credits)

Data Analysis with Python introduces students to the powerful tools and libraries available in Python for data analysis. The course covers key concepts such as data manipulation, cleaning, and exploration using libraries like Pandas and NumPy. Students will also learn to visualize data using Matplotlib and Seaborn and perform statistical analysis to uncover patterns and trends. By the end of the course, students will have the skills to handle real-world datasets, conduct meaningful analyses, and draw insights, making Python a valuable tool in their data science toolkit. Prerequisite(s): ITE 115

ITE 315 - DevOps Methodology (3 credits)

This 8-week intensive course introduces students to DevOps, a set of practices that combines software development (Dev) and IT operations (Ops). The course covers essential DevOps concepts, tools, and techniques aimed at improving the development and delivery of software. Students will learn how to implement continuous integration/continuous deployment (CI/CD) pipelines, automate infrastructure, and ensure high availability and scalability of applications. Prerequisite(s): ITE 211

ITE 330 - Systems Analysis and Design (3 credits)

This course will focus on studying IT systems from various angles. It will introduce students to techniques and strategies to carry out system design, with a focus on a developer's view. The course will consider methodologies to analyze both legacy systems and design of newly specified systems. Other applicable topics such as modular design components, iterative versus flexible design, databases, and data collection will also be studied. Prerequisite(s): ENG 101, ENG 102.

ITE 401 - Introduction to Machine Learning (3 credits)

This course introduces students to machine learning concepts and Python applications. Topics include data acquisition, data modeling, supervised and unsupervised learning, reinforcement learning, neural networks, and deep learning. Prerequisite(s): ITE 301

ITE 402 - Introduction to Computer Vision (3 credits)

This course provides an introduction to the fundamentals of computer vision and image processing, designed to equip students with the essential knowledge and practical skills for building real-world applications. Students will learn to use OpenCV for image and video analysis, Keras for constructing and training deep learning models, and Intel's OpenVINO toolkit for optimizing and deploying these models for high-performance inference. The overarching focus is on bridging theory with practice, ensuring that upon completion, students can create a complete computer vision application, from initial data processing to final, optimized deployment. Prerequisite(s): ITE 301

ITE 410 - Software Engineering (3 credits)

This course will focus on the practice and theory of software engineering. Components to aid in the design of complex systems will be studied by examining modularity, interfaces, data and control flow models, and controlling interaction, coupling, and cohesion, as well as basic data structures and algorithms. Coverage of the differing design methodologies will be discussed including waterfall and stage gate, iterative, RAD, JAD, and project analysis to aid in selecting the most appropriate model(s). Prerequisite(s): ENG 101, ENG 102. Recommended Prerequisite(s): ITE 200-level course or equivalent.

ITE 495 - Software Development Capstone (3 credits)

The Software Development Capstone course is designed to provide students with a comprehensive and practical experience in designing, developing, and delivering a software project from inception to deployment. This course simulates a real-world software development environment, allowing students to apply the knowledge and skills they have acquired throughout their studies. Working in teams, students will engage in all phases of the software development lifecycle, including requirements gathering, design, implementation, testing, deployment, and maintenance. Prerequisite(s): ITE 315

ITE 499 - Info Systems Studies Capstone (3 credits)

This is the capstone course for the Information Systems concentration and should be taken in the student's last semester. The student can have no more than 6 credits remaining in their concentration to complete in their degree program prior to enrolling in this course. The goal of the course is for students to to integrate the concepts of the Information Systems concentration and prepare individuals for positions that use information technology to develop computer-based systems that support organizations. The course must be taken at Charter Oak State College. Prerequisite(s): ENG 101, ENG 102.