UMBC Online Master’s in Information Systems

This course is designed to provide an introduction to data science concepts and techniques. The course will include both theoretical foundations of commonly used data science methods as well as hands-on exercises using open source libraries like Python Scikit learn. Topics will include techniques such as data preprocessing, classification, clustering, and visualization. Various algorithms on each of these techniques will be covered in the course. Examples of such algorithms include the Apriori algorithm for logistic regression, support vector machines, and decision trees for classification; and k-means, DBSCAN, and hierarchical algorithms for clustering, and t-SNE for visualization. Several real-life applications will be discussed for each of these techniques.

Prerequisite: IS 633 or an equivalent.

This course provides a solid understanding of what deep learning is, when it is applicable, and what its limitations are. The students will be familiar with the standard workflow for approaching and solving machine-learning problems and know how to address commonly encountered issues. Students will be able to use Keras and TensorFlow to tackle real-world problems ranging from computer vision to natural-language processing: image classification, time series forecasting, sentiment analysis, image and text generation, and other advanced topics such as reinforcement learning. Some prior background in machine learning with Python is expected.

Prerequisite: IS 675 or an equivalent.

The rise of social media has brought fundamental changes to individuals, businesses, and organizations in how people and organizations interact with one another. Social media have helped to not only connect everyday users with their friends and like-minded others, but also give them a voice that can have considerable influence on individual and business decision making. Social media transforms how individual users retrieve, organize, store, and share information, how they create and use knowledge, how they interact with one another, and how they build new relationships and maintain existing relationships, etc. This course will take an integrative approach to studying social media by providing an in-depth look into social media phenomenon, social network data, social network analysis, and social network application. The course will introduce relevant concepts, methods, knowledge, perspectives, and practical skills required to leverage the opportunities inherent in social media and user-to-user social interactions for achieving business, marketing, organizational, and personal objectives.

Prerequisite: IS 631

This course explores advanced topics in Information Systems that are not covered in other courses. Since the topics vary each semester, this course may be repeated for credit.

If a certain semester’s IS 698 course is eligible towards a track, it will be stated in the Course Schedule.

This is a course in independent research for master’s students, and is supervised by a member of the Information Systems faculty. The purpose of this course is to give students the opportunity to study a topic of interest which is not available from the existing course offerings.

Prerequisite: Consent of the instructor.

Note: A particular faculty member must agree in writing to supervise the proposed study before the student may register for this course. The approval of the Department is required before the student registers.

This course provides an introduction to the principles of cybersecurity. It focuses on theory and practice of cybersecurity concepts shedding a light on hacking, theft, and exploitation of information assets. Topics include authentication, access control, password management, cryptography, software vulnerabilities and malware, network security attacks, operating system attacks, firewalls, intrusion detection and prevention, etc.

This course surveys threats to computer and network security and methods for preventing intrusions. We study how vulnerabilities to these threats arise in the development and use of computer systems and survey the controls that can reduce or block these threats. The course will consist of weekly readings, homework questions, and hands-on labs.

Prerequisite: IS 632

Cyber security is a pervasive problem affecting individuals, organizations, and governments. This is due to the acceptance and adoption of technology in the form of multiple types of non-traditional devices. Thus, cybersecurity has to address challenges emerging in the areas of not only computer networks but also sensor networks, industrial control systems and user devices.

One common thread in all these types of devices and end users is data. Increasingly, the focus of cybersecurity is shifting to analyzing data in not only a retrospective manner but also a prospective manner across different segments of cybersecurity domain such as software vulnerabilities, network data from intrusion detection systems, network traffic data, and user roles to name a few. Due to the seamless nature of the internet it has become more important to attribute cyber security events to geographic domains. Thus, data analytics has to go beyond the traditional themes of security and seamlessly weave across several domains including geospatial data and temporal data. This course is an introduction to data analytics for cybersecurity.

The course will look at data from different perspectives such as geospatial, temporal, social network, and sensor networks to assess cyber threats and knowledge about cyber-attacks. The course will provide an introduction to cybersecurity and different aspects of it, study different types of cyber attacks, anomalies and their relationship to cyber threats, introduction to data mining and big data analytics, methods for discovering anomalies, tools for data analytics and anomaly detection, and hands-on exercises for data analysis. The course will include lectures and hands-on analytics tasks.

Prerequisite: IS 633 or experience in database design and query processing.

This course will help students move into or advance in the cybersecurity field by developing skills in five areas of ethical hacking:

• Reconnaissance: hackers gather information about a target system before conducting an attack
• Scanning: hackers identify a way to gain access to the system
• Gain Access: hackers access the system, applications, and network and escalate their privileges
• Maintain Access: jackets continue to maintain access to the system
• Cover your Tracks: hackers eliminate evidence of the system being hacked

The course includes hands-on activities in which students apply theoretical concepts in a simulated business environment. By the end of the course, students will be prepared to plan a cyber attack on an information system to identify potential system vulnerabilities.

This course introduces students to classic techniques and common tools used to secure applications and storage in the cloud. The course uses the Amazon Web Services (AWS) platform and discusses multiple tools and techniques available in AWS to control access, and secure data and applications. Resources and tools covered include but are not limited to AWS Config, AWS Cloud Trail, AWS Artifact, the AWS Compliance Center, AWS Organizations, and AWS Web Application Firewall (WAF).

This course explores advanced topics in Information Systems that are not covered in other courses. Since the topics vary each semester, this course may be repeated for credit.

If a certain semester’s IS 698 course is eligible towards a track, it will be stated in the Course Schedule.

This is a course in independent research for master’s students, and is supervised by a member of the Information Systems faculty. The purpose of this course is to give students the opportunity to study a topic of interest which is not available from the existing course offerings.

Prerequisite: Consent of the instructor.

Note: A particular faculty member must agree in writing to supervise the proposed study before the student may register for this course. The approval of the Department is required before the student registers.

This course is designed to provide an introduction to data science concepts and techniques. The course will include both theoretical foundations of commonly used data science methods as well as hands-on exercises using open source libraries like Python Scikit learn. Topics will include techniques such as data preprocessing, classification, clustering, and visualization. Various algorithms on each of these techniques will be covered in the course. Examples of such algorithms include the Apriori algorithm for logistic regression, support vector machines, and decision trees for classification; and k-means, DBSCAN, and hierarchical algorithms for clustering, and t-SNE for visualization. Several real-life applications will be discussed for each of these techniques.

Prerequisite: IS 633 or an equivalent.

This course focuses on the theory and practice of integrating systems and information with an emphasis on semantics. The problem of integrating information is extremely common in today’s world. When one organization acquires or merges with another, it usually inherits an entire IT department which may or may not be compatible with its existing infrastructure. Data systems and information must easily interoperate to meet the business needs of the organization.

This course investigates the various technologies in the field of information integration with an emphasis on semantics. Topics that are covered include: Data Integration Architectures, Modeling Data Semantics, Semantic Interoperability, Metadata, Semantic Integration Patterns, Context-Awareness, Semantic Networks, Mediation and Wrapper techniques, etc.

Prerequisite: IS 633

This course provides a solid understanding of what deep learning is, when it is applicable, and what its limitations are. The students will be familiar with the standard workflow for approaching and solving machine-learning problems and know how to address commonly encountered issues. Students will be able to use Keras and TensorFlow to tackle real-world problems ranging from computer vision to natural-language processing: image classification, time series forecasting, sentiment analysis, image and text generation, and other advanced topics such as reinforcement learning. Some prior background in machine learning with Python is expected.

Prerequisite: IS 675 or an equivalent.

Cyber security is a pervasive problem affecting individuals, organizations, and governments. This is due to the acceptance and adoption of technology in the form of multiple types of non-traditional devices. Thus, cybersecurity has to address challenges emerging in the areas of not only computer networks but also sensor networks, industrial control systems and user devices.

One common thread in all these types of devices and end users is data. Increasingly, the focus of cybersecurity is shifting to analyzing data in not only a retrospective manner but also a prospective manner across different segments of cybersecurity domain such as software vulnerabilities, network data from intrusion detection systems, network traffic data, and user roles to name a few. Due to the seamless nature of the internet it has become more important to attribute cyber security events to geographic domains. Thus, data analytics has to go beyond the traditional themes of security and seamlessly weave across several domains including geospatial data and temporal data. This course is an introduction to data analytics for cybersecurity.

The course will look at data from different perspectives such as geospatial, temporal, social network, and sensor networks to assess cyber threats and knowledge about cyber-attacks. The course will provide an introduction to cybersecurity and different aspects of it, study different types of cyber attacks, anomalies and their relationship to cyber threats, introduction to data mining and big data analytics, methods for discovering anomalies, tools for data analytics and anomaly detection, and hands-on exercises for data analysis. The course will include lectures and hands-on analytics tasks.

Prerequisite: IS 633 or experience in database design and query processing.

This course explores advanced topics in Information Systems that are not covered in other courses. Since the topics vary each semester, this course may be repeated for credit.

If a certain semester’s IS 698 course is eligible towards a track, it will be stated in the Course Schedule.

This is a course in independent research for master’s students, and is supervised by a member of the Information Systems faculty. The purpose of this course is to give students the opportunity to study a topic of interest which is not available from the existing course offerings.

Prerequisite: Consent of the instructor.

Note: A particular faculty member must agree in writing to supervise the proposed study before the student may register for this course. The approval of the Department is required before the student registers.

This course explores the main data gathering and analysis methods and processes that underlie the user-centered design of information systems. Students will learn to conduct user research for user experience design. The course also provides students the opportunity to apply these concepts through the practice of requirements gathering, design, and evaluation of designs in a hands-on project.

The course starts by discussing fundamental psychological concepts needed to understand how humans interact with computer systems and how those systems can be better designed to support that interaction. Design and evaluation methods are presented to achieving this goal. This module builds on earlier courses, particularly Systems Analysis and Design (IS634), but adds much more material about how to design for human interaction. These concepts are important for any information system in which human interaction is required.

Interaction design is the practice of designing interactive computer systems and devices. It involves designing for the Web, mobile devices, wearables and other ubiquitous systems as well as laptops, desktops, server and client systems. Interaction design draws knowledge and skills most strongly from the fields of human-computer interaction and computer supported co-operative work (and their foundational fields, such as computer science, information systems, psychology, anthropology and sociology). It is also informed by aesthetic design disciplines such as graphic design, typography, architecture and computer art.

Interaction design makes use of a wide variety of tools and techniques developed and practiced during the last thirty years. However, many aspects of interaction design and human-computer interaction do not conform to the expectations of an ‘exact science’. To a large extent interaction design involves putting into practice a body of tried and tested knowledge, skills and techniques and then iteratively improving designs through series of user tests. Consequently, unlike some fields there is rarely a right or a wrong design, but as you will discover there are certainly good designs and very poor designs, and designs that are better than other designs. In this course you will develop knowledge, skills and learn a set of techniques, which if used appropriately, will enable you to produce much better human-computer interfaces and user-computer interactions than you could possibly achieve using just your own best judgment. In order to benefit from this course you must therefore be prepared to iteratively refine your best efforts through systematic user testing.

The course aims to:

1. Introduce you to the concept of interaction design and teach you the main psychological, sociological, and anthropological knowledge and skills to evaluate and design the interaction components of interactive systems or parts of systems.
2. Teach you a range of interaction design techniques so that you can design small interactive systems.
3. Teach you a range of evaluation techniques so that you can confidently and thoroughly evaluate interactive systems and give you experience through project work.
4. Make you aware of a wide range of interactive systems.
5. Provide experience and practice in designing and evaluating the interaction component of a system or part of a system.
6. Teach you how to use synchronous and asynchronous communication technologies effectively to collaborate and exchange ideas with other students and your instructor.

These seven aims can also be described as behavioral learning objectives as follows. After completing the Interaction Design course, you will be able to:

1. Describe interaction design and discuss the role that psychological, sociological, anthropological knowledge and skills in interaction design.
2. Perform a range of interaction design techniques.
3. Confidently perform and report the findings of evaluations using a variety of techniques appropriate for the circumstances.
4. Describe a wide variety of different kinds of interactive systems.
5. Design and evaluate the interaction design of a small interactive system or part of a system.
6. Work collaboratively with others to develop a web-based class resource.
7. Use synchronous and asynchronous communication technologies to collaborate with others effectively.

Prerequisite: IS 634

As the web matures, so do users’ expectations about what a site should do. In addition to a pleasing design and working links, they also want sites that are clearly organized, relevant, accurate, up-to-date, and have interesting and easy-to-find content. This course will focus on the principles and practices of the user-centered information architecture design of websites that address these needs. We will study the creation and organization of web content that meets the information needs of end-users and serves the intentions or purposes of a site’s sponsors or creators. We will learn about the basic principles of writing and labeling web content and the usable design of websites. We will also learn about users’ web browsing and searching behavior and the design of search and navigation systems to support this behavior. We will explore options to set up search within sites and optimizing the findability of a site through search engines.

This course, however, is NOT a web graphics design, HTML or Web programming class, we will not build a website. Students will be researching the content and context of websites and the needs of users and sponsors. They will develop the purpose and strategy for a specific site of their choosing. They will design the information organization and labeling systems and develop the navigation system of the website. They will design page layouts and create content for the selected website. The will achieve these goals by planning and creating information architecture deliverables for the site prototype that facilitates consensus building among stakeholders and guides a designer or programmer in the production of a working web site. Students will also analyze the information architecture, navigation structure, audience awareness and usability of good and bad web sites.

This course explores advanced topics in Information Systems that are not covered in other courses. Since the topics vary each semester, this course may be repeated for credit.

If a certain semester’s IS 698 course is eligible towards a track, it will be stated in the Course Schedule.

This is a course in independent research for master’s students, and is supervised by a member of the Information Systems faculty. The purpose of this course is to give students the opportunity to study a topic of interest which is not available from the existing course offerings.

Prerequisite: Consent of the instructor.

Note: A particular faculty member must agree in writing to supervise the proposed study before the student may register for this course. The approval of the Department is required before the student registers.