COMPUTER SCIENCE

This course introduces the Python programming language. Students will learn how to write programs dealing in a wide range of application domains. Topics covered include the language syntax, IDE, control flow, strings, I/O, classes and regular expressions. Students may use either a PC (Windows) or a Mac (Linux) to complete their programming assignments.

This course introduces modern database concepts while emphasizing the relational database model. Topics include design methodologies, normalization of tables to reduce redundancies, supertypes and subtypes to reduce nulls, data integrity, referential integrity, and using locks and other techniques for concurrency control in a multi-user database. Factors that should be balanced during the design of a database are described. To document databases, entity relationship diagrams, relational schemas, and data dictionaries are described. Principles are applied by performing exercises using MySQL or other database management system. SQL and other languages are used to create and fill tables, retrieve data, and manipulate it by stored programs.

This course offers a broad introduction to networking concepts and analyzes different network architectures. Introductory topics include network topologies, media and signaling, protocols, addressing, and distributed networks. The varied ways to connect computers are explored as are the resulting architectures. The course explores subnetting, both physical and virtual and internetworks are constructed in the lab. Server programs are introduced to demonstrate their signature socket-API structure. Specific real-world services such as the apache web server, BIND name server, NFS and Samba file system servers, DHCP address server, and others are discussed.

In this introductory course students will learn how to defend and protect critical computer assets from various security threats including computer worms and viruses. This course will describe fundamental techniques and principles for modeling and analyzing security. Students will learn how to express security requirements, translate requirements into policies, implement mechanisms that enforce policy, and ensure that these policies are effective. Current industry best practices for safeguarding computer resources will be discussed. Various case studies will outline the typical way that security failures get exploited by attackers and how these attacks can be discovered, understood, and countered.

In this course, students will learn the principles and techniques of network forensics investigation and the use of available forensics tools in the list of the International Association of Computer Investigative Specialists (IACIS) certification. This course explores security incidents and intrusions, including identifying and categorizing incidents, responding to incidents, using log analysis, analyzing network traffic, applying various tools, and creating an incident response team. Students will also learn about ethical implications of computer forensics reporting and the laws regarding computer evidence.

This course provides an in-depth understanding of how to protect IT infrastructure. The course combines ethical hacking methodologies with the hands-on application of security tools to secure computer and other digital systems. Students are introduced to common countermeasures that effectively reduce and/or mitigate attacks. In addition, the course covers what an ethical hacker is and how important it is to protect data from cyber attacks. Students will review TCP/IP concepts and practice footprinting, scanning, enumeration, exploitation, and social engineering.

Technology, through the use of cellphones, tablets, desktops and embedded systems, surrounds us everywhere and is a part of our daily life. With the ubiquity of device use, and global-scale data transfers, users are vulnerable to the temptations of cyber-criminals. In this course, students learn how to use technology safely. The course also introduces basic concepts of cybersecurity and explores careers in this field. This course is intended for any non-major student who wants to be a savvy user in the world today.

This course addresses cloud database management which supports a number of different approaches for storing data. In the course, students define, operate and scale both SQL and noSQL data storage solutions. This course considers factors that should be balanced during the design of a storage solution. Principles are applied by performing exercises using Amazon RDS and SQL to create and fill tables, retrieve and manipulate data. Object-based APIs are used to serialize objects to Amazon DynamoDB for noSQL solutions. Topics include automated backups, transaction logs, restoration and retention.

In this course, students explore how cloud computing systems are built using a common set of core technologies, algorithms, and design principles centered around distributed systems. Students will use the Amazon Web Services (AWS) Management Console to provision, load-balance and scale their applications using the Elastic Compute Cloud (EC2) and the AWS Elastic Beanstalk. The course discusses, from a developer perspective, the most important reasons for using AWS and examines the underlying design principles of scalable cloud applications.

This course focuses on protecting the confidentiality, integrity and availability of computing systems and data.Students learn how Amazon Web Service (AWS) uses redundant and layered controls, continuous validation and testing, and a substantial amount of automation to ensure the underlying infrastructure is continuously monitored and protected. Students examine the AWS Shared Responsibility Model and access the AWS Management Console to learn more about security tools and features provided by the AWS platform.

This course will cover how business decisions can be made into machine learning problems for deeper business insight. We will cover the terms and concepts required to help you learn and build a good foundational understanding of machine learning, artificial intelligence and deep learning. You will learn the various Amazon Web Services Machine Learning stack, Artificial Intelligence and Deep Learning services, using application use cases, frameworks and infrastructure that will allow us to build, train, and deploy learning models at scale. Data is a vital part of machine learning, we will cover how business data is stored, moved and processed throughout the machine learning pipeline.

In this course, students will explore the field of data science and the possible career pathway that can be taken. Students will learn how the data science process can be used to address real-world problems. The course will cover a basic introduction to the key areas of data science including data acquisition and management, data modeling, analysis visualization, and data reporting. Students will be introduced to tools to analyze and visualize data for data-driven decision making.

In this course students will focus on the data science pipeline including problem formulation, data cleaning and preprocessing, exploration of data with visualization, model prediction and inference for decision making. Students will use different software tools and programming for each step of the data science pipeline, include data exploration and transformation, algorithms for machine learning concepts such as classification, regression, and clustering. In addition, students will learn how to effectively present any findings to an audience.

R is a commonly used programming language for data analysis, data visualization, machine learning, and data science. In this course students will learn the fundamentals of R syntax, how to organize and modify data, prepare data for analysis, and create visualizations.

This course builds on a first level course in Python exposing students to more advanced topics and applications to industry. Topics cover object-oriented programming, creating classes and using objects, web applications, and some common libraries and their functions used for data manipulation. Students may use either a PC (Windows) or a Mac (Linux) to complete their programming assignments.

Please see Independent Studies section.