Curriculum and Course Descriptions

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Course Descriptions

Analytics and Information Management
Statistical and technical understanding are essential for the AIM professional. This course focuses on creating a solid foundation of technical skills, which will be applied in downstream courses. Topics will include programming concepts and logic, descriptive, predictive and prescriptive statistics, and data storage techniques. Offered in hybrid and online modalities every fall semester; prerequisites: None

Information Systems Ecosystem
Within the context of data analytics, this course teaches students to manage information as a strategic asset with the potential to create significant business value. Students will be exposed to various approaches to managing the capture, retention and disposition of information. Special emphasis will be placed on the legal/regulatory, ethical, risk management and cybersecurity requirements of managing information. Topics include the role of information systems in an organization, information systems governance (which is designed to ensure that IT investments create organizational value), data governance (which seeks to ensure that organizational data meet the standards for quality data), and strategies for identifying measurable sources of ROI. Offered in an online modality every fall and summer semester; prerequisites: None

Data Querying
Modern organizations use database management systems to store their critical business data. In this course, students learn to answer business questions using various data retrieval, manipulation and transformation tools. For instance, data retrieval for most data-driven business applications relies on Structured Query Language (SQL) - the international standard language for data manipulation and retrieval. To source the data required by analytics projects, students will learn to utilize SQL along with other data-related concepts and languages. Offered in a hybrid modality every fall semester and online in the fall semester of odd-numbered* years; prerequisites: None

Predictive Analytics with Machine Learning
Artificial Intelligence (AI) technologies are widely adopted and applied in various contexts. Applications of AI include financial services, cyber security, web search, targeted product recommendations, self-driving cars, personal assistants, robotic manufacturing, machine translation, and video games. Such applications use AI techniques to interpret information from a wide variety of sources to enable intelligent, goal-directed behavior. Companies are increasingly applying these AI technologies and techniques to uncover new business insights and assist managers with making better informed and timely decisions. This course will cover concepts and algorithms in artificial intelligence with an emphasis on machine learning. Fundamental concepts and base knowledge for understanding AI, such as basic search algorithms for problem solving, knowledge representation and reasoning, pattern recognition, fuzzy logic, and neural networks, will be introduced.
Offered in a hybrid modality every spring semester and online in the spring semester of even-numbered* years; prerequisites: ISYS 610 Analytics and Information Management

Behavioral Analytics with Visualization
E-commerce and social media have experienced rapid growth during the past decade. Billions of users have been generating and sharing a variety of social content including text, images, videos, and related metadata. Social media can be viewed as an indicator reflecting different aspects of the society, and organizations can combine this with their existing data to support organizational decision-making. In this course, students will learn how to analyze behavioral data and apply the analyses to answer business questions. Advanced regression techniques, network concepts, and social theories will be covered throughout the course. Offered in a hybrid modality every spring semester and online in the spring semester of odd-numbered* years; prerequisites: ISYS 610 Analytics and Information Management

Curriculum Guide

*Even denotes academic years when the fall semester's year is an even number (e.g., 2022), odd denotes academic years when the fall semester's year is an odd number (e.g., 2021)