Decision Sciences (DS)
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Read more about the courses within this subject prefix in the descriptions provided below.
DS 801 - Business Intelligence
Credits: 3
This course is designed to introduce students to the skills needed to succeed in today's big data environment through the application of data management techniques, business-oriented hands-on cases and exercises. Students will acquire concepts and application of data management techniques, business-oriented hands-on cases and exercises. Students will acquire concepts and techniques in the theory, design, and implementation of relational databases and Data Warehousing (DW) systems, queries in Structured Query Language (SQL), next generation query language (NoSQL).
Grade Mode: Letter Grading
DS 802 - Probability Models for Analytics
Credits: 3
The course is designed to provide an introductory understanding of the fundamentals of uncertainty quantification in business decision making. The course will serve as a building block for subsequent course work in inferential statistics, predictive analytics, time series analysis, and unstructured data analysis. The topics include the axioms of probability theory, random variables, probability distributions, and regression models. An introduction to the programming language R will be part of the learning experience.
Grade Mode: Letter Grading
DS 803 - Fundamentals of Statistical Analysis
Credits: 3
The course is designed to introduce the fundamentals of statistics needed for solving business analytics problems. The course will mainly cover the broadly defined subjects of random sampling, likelihoods, estimation using maximum likelihood, Bayesian inference using priors, computational statistics methods, interval estimation, hypothesis testing for continuous data, Gaussian linear models, and model diagnostics. The course will conclude with a brief introduction to nonparametric analysis.
Prerequisite(s): DS 802 with a minimum grade of B-.
Grade Mode: Letter Grading
DS 804 - Exploration and Communication of Data
Credits: 3
The goal of this course is to expose students to techniques and technologies that will enable them to collect, harvest and transform unstructured and structured data into useful business insights. The first half of the course deals with data management and provides an introduction to data types and sources, data acquisition and harvesting tools and techniques and effective strategies and methods for data aggregation and analysis. In the second half of the course, students learn about the theoretical underpinnings of data visualization and use a variety of software tools to visualize business data in order to generate insightful information that facilitates effective business decision making.
Grade Mode: Letter Grading
DS 805 - Machine Learning for Business
Credits: 3
This course introduces students to statistical tools for modeling and identifying patterns in complex data sets. The goal of machine learning is to develop predictions informed by data. Topics to be covered include model selection and evaluation, data preprocessing techniques, supervised classification models such as knn and ensemble learning techniques, and unsupervised learning via dimensionality and lustering. Application areas include Marketing (e.g., effectiveness of advertising and customer satisfaction), Financial economics (valuation), and Operations Management (resource allocation).
Prerequisite(s): DS 802 with a minimum grade of B-.
Grade Mode: Letter Grading
DS 806 - Optimization Models for Analytics
Credits: 3
This course introduces students to fundamental quantitative methods for modeling, analyzing, and determining the best course of action in complex decision-making situations (i.e., prescriptive analytics). Topics to be covered include decision trees and tables, price of uncertainty, utility theory, linear programming (LP), LP sensitivity analysis, network optimization, integer optimization, and nonlinear optimization. Application areas include Finance, Marketing, Operations Management, and others (e.g., advertising, production and inventory planning, project or personnel scheduling, shipping and distribution, routing, ride matching, etc.).
Grade Mode: Letter Grading
DS 807 - Text and Image Analytics
Credits: 3
This course introduces students to statistical and machine learning tools for modeling unstructured data; including emails, documents, text messages, and social media data. Topics to be covered include text mining, clustering, mixture models, deep learning, and topic models. The course integrates numerous applications to demonstrate practical approaches to analyzing large unstructured collections of data. Application areas include Marketing (Yelp and Trip Advisor reviews), Human Resources (health care plan analysis), Social media (Twitter, YouTube, and Instagram).
Prerequisite(s): DS 805 with a minimum grade of B-.
Grade Mode: Letter Grading
DS 808 - Optimization Methods II
Credits: 3
This course introduces students to more advanced concepts and modeling techniques in mathematical programming. Topics to be covered include integer programming, nonlinear programming, multi-objective optimization, goal programming, and Monte Carlo simulation. Application areas include Marketing (e.g., pricing and revenue optimization), Finance (capital budgeting and portfolio optimization), and Operations management (e.g., production and inventory planning, shipping and distribution, routing, location selection, etc.). The course delivery will be a mix of lectures, hands-on problem solving, and case discussions.
Prerequisite(s): DS 806 with a minimum grade of B-.
Grade Mode: Letter Grading
DS 809 - Business Forecasting
Credits: 3
The course is designed to introduce forecasting techniques needed in the estimation/analysis of temporal data (time series) in various business disciplines. The course focuses on traditional regression and stationary univariate models. Some examples of business application areas include demand forecasting, financial asset return modeling, stochastic volatility modeling of financial indexes and securities, mortgage default risk assessment, call center arrival modeling, online webpage click-rate modeling, and market share modeling.
Prerequisite(s): DS 802 with a minimum grade of B-.
Grade Mode: Letter Grading
DS 810 - Big Data and AI: Strategy and Analytics
Credits: 3
This course provides students with the knowledge and skills to manage and model vast quantities of data for business analytics and extract meaningful business insights including causal relationships. The course covers deep neural networks and large-scale data processing using ecosystems of computing tools and emerging methods. Students learn how to store, analyze, and derive insights from large-scale datasets and develop an understanding of the implications of deep learning and causal AI for business. As a part of the project experience, students complete a team project that focuses on using big data and artificial intelligence for business insights, and present and discuss their work.
Prerequisite(s): DS 801 with a minimum grade of B- and DS 804 with a minimum grade of B- and DS 805 with a minimum grade of B-.
Grade Mode: Letter Grading
DS 811 - AI Applications for Business
Credits: 3
This course offers a practical introduction to the modern data science and AI tools and technologies that are reshaping business analytics. We will focus on the full lifecycle of machine learning applications and MLOps, including deployment using containers, versioning, and maintenance with Git. We'll also cover the essential applications of Large Language Models (LLMs), AI Agents, and the Agent Workflows that orchestrate multiple AI tools to tackle complex analytical challenges and business processes. The emphasis is on a grounded, hands-on approach, utilizing current platforms and frameworks to solve real-world business problems.
Grade Mode: Graduate Credit/Fail grading
DS 815 - Programming for Business Analytics
Credits: 3
This course introduces students to business programming. The course covers the Python programming language and students learn to collect, wrangle and manipulate data. Students also gain hands-on experience generating and presenting meaningful visualizations of quantitative and qualitative data to aid peer/managerial decision-making.
Grade Mode: Letter Grading
DS 816 - Tools for Business Analytics
Credits: 3
The goal of this course is to expose students to popular software tools used in all stages of data analytics in business, to create actionable insights. The course will cover and introduce tools for the three key areas of data analytics: a) Data Preparation & Blending b) Data Analysis & Visualization c) Model Building for Predictive Analytics .Students learn about the overall capabilities of these tools and will practice applying them to diverse types of sample data.
Grade Mode: Letter Grading
DS 872 - Predictive Analytics and Modeling
Credits: 3
The course introduces students to commonly used predictive analytics methods and necessary programming with a focus on regression analysis, classification, and model building. The course coverage is supported using real data applications and illustrations. The topics include linear and non-linear regression model building/selection, residual analysis, search algorithms, generalized linear models/classification, and applied machine learning methods for business use.
Prerequisite(s): ADMN 510 with a minimum grade of C-.
Grade Mode: Letter Grading
View Course Learning Outcomes
- At the end of the course, you will have, learned data analytics concepts and techniques needed to make effective use of large data sets, developed an understanding of important concepts in predictive analytics, modeling, regression, classification, association and causation; and learn how these can aid better decision-making.
- Studied a range of business applications -such as targeting in marketing campaigns, customer retention, fraud detection, loan default prediction, collaborative filtering (recommender systems)- so as to be better equipped to develop effective business solutions.
- Learned how to avoid common pitfalls and ensure a successful data modeling project outcomes.
- Gained hands-on experience using real business datasets and analytics and modeling software.
DS 874 - AI and Emerging Technologies in Business
Credits: 3
This course immerses students in the intersecting realms of technology and business. Students will explore key domains such as Artificial Intelligence, Cybersecurity, Global e-Business, Application Design, and Enterprise Systems, engaging in a hands-on, collaborative curriculum. Students will develop a strategic perspective on using IT innovations to drive business value, tackle real-world challenges, and build in-demand skills for dynamic technology careers.
Prerequisite(s): ADMN 410 with a minimum grade of C-.
Grade Mode: Letter Grading
View Course Learning Outcomes
- Identify how information technologies can be used in creating competitive advantage.
- Understand the importance of information in organizations and should be able to anticipate ways in which information systems can assist them in the performance of their functional duties.
- Explain the key challenges in and some of the solutions to managing data, information, and knowledge.
DS 898 - Topics in Business Analytics
Credits: 3
Special Topics; may be repeated. Pre- and co-requisite courses vary. Please consult time and room schedule for the specific 898 topics section you are interested in for details.
Repeat Rule: May be repeated for a maximum of 12 credits.
Grade Mode: Letter Grading