Information Systems and Business Analytics (ISBA)

Professors Emeriti: Steven Nahmias, Stephen A. Smith

Professors: Narendra Agrawal (Benjamin and Mae Swig Professor), Gangshu Cai (Department Co-Chair), Manoochehr Ghiassi, Haibing Lu (Department Co-Chair), S. Andrew Starbird, Andy A. Tsay

Associate Professors: Ram Bala, Tao Li, Sami Najafi-Asadolahi, Michele Samorani, David K. Zimbra

Assistant Professors: Hussein El Hajj, Jingbo Hou, Wilson W. Lin, Yu-Wei Lin, Amber Xiaoyan Liu, Mohammad Amin Morid, Simrita Singh, Xiang (Shawn) Wan, Yaqiong Wang, Ying (Maggie) Zhang

Associate Teaching Professors: Homi Fatemi, Rick Schaffzin, Sumana Sur

Assistant Teaching Professors: Thunyarat (Bam) Amornpetchkul, Xuan Tan, Xiaochen Zhu

ISBA Courses Offered

ISBA 2000. Linear Algebra

This course provides a practical introduction to linear algebra with a focus on its applications in data science and machine learning. Students will learn essential matrix operations, vector spaces, eigenvalues, least squares regression, and dimensionality reduction techniques such as Principal Component Analysis (PCA). The course also introduces Singular Value Decomposition (SVD) and explores how neural networks leverage matrix computations. Rather than focusing on deep theoretical proofs, this course emphasizes intuitive understanding and hands-on implementation using Python (NumPy, SciPy, and scikit-learn). By the end of the course, students will be able to apply linear algebra concepts to real-world datasets and build a strong foundation for further studies in data science, AI, and machine learning. (2 units)

Prerequisites: None.

ISBA 2400. Math for Business & Analytics with R

The objective of this course is to provide a comprehensive background in the

mathematical topics required for learning Quantitative Finance (QF) and Business Analytics and Data Science (BADS). The mathematical topics covered include Calculus, Linear Algebra, and Probability Theory. Applications of these topics in a variety of business contexts will be included. (4 units)

Prerequisites: None.

ISBA 2401. Data Analytics with Python

Data analytics involves the application of scientific methodologies to extract, understand, and make predictions based on data sets from a broad range of sources. Data analytics requires knowledge and skills from three areas: (i) programming, (ii) math/statistics, and (iii) domain specific expertise. The objective of this course is to teach the programming skills relevant to data science. Students will learn to use a complete set of open source tools for data science in Python, including the Jupyter Notebook, NumPy, Pandas, Seaborn, scikit-learn, Colab, and many others. Students will learn skills that cover the various phases of exploratory data analysis: importing data, cleaning and transforming data, algorithmic thinking, grouping, aggregation, reshaping, visualization, time series, statistical modeling, and data exploration and communication of results. The course will utilize data from a wide range of sources and will culminate with a final project and presentation. (4 units)

Prerequisite: None.

ISBA 2402. Database Management Systems- Fundamentals of SQL

This course aims to give an understanding of hands-on experience with the most widely used database tools. The objective of this course is to convey intermediate to advanced database concepts and to acquaint students with state-of-the-art software tools. The course will rely on classroom discussions, media articles and cases, as well as programming exercises and an integrative group project. Use of database software is required. A lab fee is required. Introduces database management and database management systems (DBMS). Teaches technical and managerial skills in database planning, analysis, logical design, physical design, implementation, and maintenance. Features hands-on training in database design, development, and implementation using relational DBMS software. Emphasizes designing and developing reliable databases to support organizational management. (2 units)

MS Prerequisite: None

ISBA 2403. Machine Learning with Python

This course introduces participants to quantitative techniques and algorithms that are based on big and small data (numerical and textual). We also analyze theoretical models of big systems for prediction and optimization that are currently being used widely in business. It introduces topics that are often qualitative but that are now amenable to quantitative treatment. The course will prepare participants for more rigorous analysis of large data sets as well as introduce machine learning models and data analytics for business intelligence. (4 units)

Prerequisite: ISBA 2401 (Data Analytics with Python)

ISBA 2404. Dashboards with Tableau

This course enables you to transform data into persuasive dashboards that effectively inform and guide management actions. Dashboards are persuasive if they motivate actions in an intended audience. Dashboards are effective if they offer comprehensive and reliable information. This course introduces and discusses the fundamental design principles and technology of dashboards and allows you to design, implement, and critique dashboards. (2 units)

Prerequisites: None.

ISBA 2405. Prescriptive Analytics with Python

This course helps participants understand the principles of optimization in business decisions and prepare computer based models from problem descriptions and determine optimal solutions using software tools. This course also prepares participants to interpret solutions to obtain insights regarding sensitivity to inputs, resource constraints, and their profitability impacts. (4 units)

Prerequisite: ISBA 2401 (Data Analytics with Python)

ISBA 2406. Object Oriented Analysis and Programming

Provide students with an overview of object-oriented programming methodology using the Java Programming Language. The course will present different programming paradigms, including structured (procedural) and object oriented, compare and contrast these approaches, object oriented programming concepts including data abstraction, abstract data types, classes and objects, inheritance, polymorphism, encapsulation, packages and interfaces, and multithreaded programming. To support GUI-based Java applications, concepts and tools such as event handling, Generics programming, Swing, and JavaFX, (libraries and classes introduced in JDK 8 and beyond) windowing systems and frameworks are introduced to support modern lightweight development of Java-based Applets. (4 units)

Prerequisite: Knowledge of C programming language or equivalent

ISBA 2407. Database Analysis, Design and Management

This course presents technical and managerial approaches to the analysis, design, and management of business data, databases, and database management systems. The topics include structured and unstructured data management, a comparison of relational and object-oriented databases, relational database conceptual and logical design, and database implementation and administration (2 units)

Prerequisite: None.

ISBA 2408. Software Project Management

An overview of software project development methodology, covering both technical and managerial aspects of software development. Examines alternate software life cycle models and introduces modern techniques for definition, design, implementation, and validation of software products. Prerequisite: Knowledge of or experience with a high-level programming language, or permission from an instructor. (4 units)

Prerequisite: ISBA 2406 (Object Oriented Analysis and Programming). MBA Prerequisite: ISBA 2406 or equivalent.

ISBA 2409. Information Systems Strategy and Management

Strategic management and deployment of information systems and technologies (ISTs) to improve business competitiveness. An examination of the role of IST strategy in enabling companies to effectively manage in the turbulent and dynamic business environment brought about by the Internet. Analysis of new business opportunities in electronic commerce brought about by ISTs, including organizational redesign that these technologies require. An examination of implementation and change management issues related to IST deployment in the new environment. Focuses on drawing lessons from the experiences of leading companies that are deploying ISTs to define and support their e-commerce strategies. (4 units)

MS Prerequisite: None. MBA Prerequisite: ISBA 3200.

ISBA 2410. Information Systems Analysis and Design-Systems Modeling

Methodology to assist in the analysis and design of computer-based information systems for business applications. Tools include data flow diagrams, flowcharts, Structured English, pseudo code, hierarchy charts, structure diagrams, and Warnier-Orr charts. Application of these tools to a systems development project is required. Cross-listed with OMIS 372. Credit will not be given for both. (4 units)

MS Prerequisite: None. MBA Prerequisites: ISBA 3000 (Statistics & Descriptive Analytics).

ISBA 2411. Natural Language Processing

This course teaches students the fundamentals of Natural Language Processing. NLP is used to analyze and process large amounts of text data, such as news, online reviews, blog posts, and financial statements. NLP has recently found several applications in business, such as sentiment analysis, text classification, text completion, and chatbots. The course covers traditional NLP techniques, such as bag-of-words, TF-IDF models, topic analysis, as well as state-of-the-art techniques, such as embeddings, transformers, language generation, and fairness in AI. This course is aimed at providing students with a conceptual understanding of the field and its business applications, and a technical toolkit to implement NLP models. (4 units)

Prerequisite: ISBA 2401 (Data Analytics with Python).

ISBA 2412. Data Visualization

This course enables you to explore data, identify insights, and develop evidence-based arguments using data visualization tools such as Tableau and R. Completing this course equips you with a moderate level of data literacy, the ability to interpret, construct and convey arguments through the functional and truthful visual presentation of data. You will wrangle data, customize data visualization technologies, and programmatically develop data visualizations. (2 units)

Prerequisite: Dashboards with Tableau strongly recommended.

ISBA 2413. Big Data Modeling and Analytics

Learn to analyze a massive amount of data with distributed computations using Spark's high-level data transformations. This course will teach scalable approaches to process large amounts of text with MapReduce, Spark, and Amazon Athena (an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL) (4 units)

Prerequisite: ISBA 2401 (Data Analytics with Python) & ISBA 2402 (Database Management Systems- Fundamentals of SQL).

ISBA 2414. Deep Learning

Introduction to the topic of Deep Learning Neural Networks (DLNs), Linear Learning models using Logistic Regression, and adding hidden layers to create Deep Feed Forward Neural Networks. Detailed algorithms are used to train these networks using Stochastic Gradient Descent and the resulting algorithm called Backprop. Training processes of these networks are used with Tensor Flow tool and the MNIST and CIFAR-10 image data-sets. Some specialized DLN architectures including the following: (a) Convolutional Neural Networks (ConvNets), (b) Recurrent Neural Networks (RNNs), (c) Reinforcement Learning. Model parameter initialization, underfitting and overfitting are discussed as well as techniques such as Regularization. Issues such as the Vanishing Gradient problem that often cause problems during training are also discussed. (4 units)

Prerequisite: ISBA 2403 (Machine Learning with Python) strongly recommended .

ISBA 2415. Reinforcement Learning

The objective of this course is to provide an in-depth introduction to the topic of Reinforcement Learning (RL) algorithms. These algorithms are based on the theory of Dynamic Programming and Markov Decision Processes, which originated more than fifty years ago. (2 units)

Prerequisite: ISBA 2403 (Machine Learning with Python) strongly recommended.

ISBA 2416. Applied Cloud Computing

Computing is migrating to the cloud. In this course, you will understand as-a-service concepts by using services from major cloud providers and learn how to deploy and manage cloud infrastructure. This course focuses on hands-on skills required to operate on the three prime cloud service platforms from Amazon, Google, and Microsoft. This course will offer an applied perspective on the core features of these platforms such as load balance, auto-scaling, serverless computing, and cloud AI. (2 units)

Prerequisite: None.

ISBA 2417. Cloud Computing Architectures

Technologies make Cloud Computing possible and how IT leverages these technologies to make the enterprise computing environment more efficient. Students will learn how hardware virtualization is made possible through computer architecture advancement, hypervisor-based virtualization and container-based virtualization, and microservices. (4 units)

Prerequisite: None.

ISBA 2418. Machine Learning with Finance

This course prepares participants for advanced analysis of large financial data sets and introduces machine learning models and data analytics techniques for financial decision-making and business intelligence. The course provides a rigorous and broad treatment of AI, machine learning, and deep learning with a focus on finance applications. Participants will use both R and Python programming languages for hands-on learning in applying machine learning methods to real-world financial problems. (4 units)

Prerequisite: ISBA 2401 (Data Analytics with Python).

ISBA 2419. Business Intelligence and Data Warehousing

Introduces the business, technology, and managerial issues related to BI and DW solutions. Students will acquire practical skills in collecting business requirements, planning, defining, designing and developing a BI solution. Emphasis is placed on learning how to derive business value from BI and DW solutions. Hands-on experience will be obtained using a variety of BI tools. (4 units)

Prerequisite: ISBA 2401 (Data Analytics with Python) & ISBA 2402 (Database Management Systems- Fundamentals of SQL).

ISBA 2420. Web Programming

The course will focus on the design and development of web based applications using a number of currently popular tools and strategies; also to be explored is the use of databases as data repositories for web applications. Core technologies including HTML, CSS, JavaScript, PHP, and MySQL will be emphasized. (2 units)

Prerequisite: None.

ISBA 2421. Generative AI for the Enterprise

This course offers an immersive exploration and hands-on experience into Generative AI and Large Language Models (LLMs). Students will gain hands-on experience and build LLM-based applications using existing commercial and open-source platforms. Topics covered include parameters for LLMs, prompt engineering, vector databases, and integration of LLMs with machine learning tasks like classification or recommendation. This class is both for managers and developers interested in developing LLM-based applications. (2 units)

Prerequisite: ISBA 2401 (Data Analytics with Python).

ISBA 2422. Data Structures and Algorithms

The course aims to equip students with the knowledge and hands-on experience to develop efficient algorithms that solve complex computational tasks that arise in different domains, such as optimal matching or vehicle routing. Students will learn the data structures needed for various computational tasks, such as lists, hash tables, double-ended-queues, priority queues or heaps, and disjoint-sets. Topics covered include sorting algorithms, graph algorithms, and algorithm design strategies, such as iteration, recursion, divide-and-conquer, depth-first search, and breadth-first search. Students will be able to analyze the complexity of an algorithm, select the most appropriate data structure to solve a given problem, and develop algorithms that are as efficient as possible. Students are expected to have prior programming experience. (2 units)

Prerequisite: ISBA 2401 (Data Analytics with Python).

ISBA 2423. Foundations of Cybersecurity

Cybersecurity encompasses technologies, processes, and practices safeguarding networks, devices, and data from unauthorized access or harm. With the rise of Software as a Service (SaaS) and its deployment in the Cloud, ensuring security is essential for all enterprises. This course covers several topics in Cybersecurity, including cryptography, ethical hacking, network and web application security, and cloud protection. Hands-on exercises using Python and Linux-based software, both locally and in the cloud, are part of the curriculum. Students will master techniques to counteract common threats like password breaches, denial-of-service attacks, and network intrusions. (4 units)

Prerequisite: ISBA 2401 (Data Analytics with Python) & ISBA 2402 (Database Management Systems- Fundamentals of SQL).

ISBA 2424. Mobile Programming

Mobile platforms and apps have become core components of business today. With over 2.5 million apps available for smartphones, understanding the basic concepts underlying mobile apps has become a core necessity for business practitioners – particularly for executives and entrepreneurs. Business professionals now need to obtain in-depth knowledge of what is inside a mobile app, how one gets built, and where the business value resides. The best and most practical way for these professionals to develop this knowledge is for them to actually build a set of mobile apps, hands-on from the ground up. This course presents the business and technical foundation for mobile platforms and app development. (2 units)

Prerequisite: None.

ISBA 2425. Enterprise Systems and Analytics

Enterprise systems are data troves that can be used to improve business performance. Enterprise systems offer historic data that can be used to optimize business processes or uncover fraudulent activities. Gives students an understanding of enterprise system fundamentals, design options for business processes, and enterprise systems data. (4 units)

Prerequisite: None.

ISBA 2426. Artificial Intelligence

The course begins by describing what the latest generation of artificial intelligence techniques can actually do. After an introduction of some basic concepts and techniques, the course illustrates both the potential and current limitations of these techniques with examples from a variety of applications. We spend some time on understanding the strengths and weaknesses of human decision-making and learning, specifically in combination with AI systems. Exercises will include hands-on application of basic AI techniques as well as selection of appropriate technologies for a given problem and anticipation of design implications. In a final project, groups of students will participate in the creation of an AI-based application. (4 units)

Prerequisite: ISBA 2401(Data Analytics with Python). Prior knowledge of statistics and programming are required.

ISBA 2427. Information Technology: Ethics and Public Policy

This course examines the intersection of law and commerce as it relates to technology companies such as those based in Silicon Valley. Students will learn to understand how to operate in this environment and the typical issues that arise in this environment as they relate to contracts, securities, intellectual property, privacy, and employment. (2 units)

Prerequisite: None.

ISBA 2428. Analysis, Design, and Management of Enterprise Platforms

Introduces the information technology infrastructures that enable within and across firm operations, and the competitive advantages that information technology can offer various firms. Focuses on how firms effectively utilize information technology resources in their business models and operations. (2 units)

Prerequisites: ISBA 3000 Quantitative Methods (Statistics & Descriptive Analytics).

ISBA 2429. Analytics-driven Organizational Transformation

This course introduces key business functions and studies how analytics is used in those business functions. Various business applications across different functional areas and industry verticals will be analyzed using real and simulated data to get a deeper understanding of analytics principles. The objective of the course is to provide the right level of exposure to the day-to-day life of an analytics professional. Examples of business functions that will be studied are sales and marketing, finance, supply chain, HR, and finance. (2 units)

Prerequisites: ISBA 3200 (Business Analytics (Predictive & Business Analytics)).

ISBA 2430. New Product Development

Introduces students to the methods companies use to develop and release new products. New product development is a challenging, rewarding activity that can make the difference between success or failure for a company, especially in technology-based industries. The traditional view that new product development is an “art” practiced by engineers has now given way to an understanding that it is a discipline that must be learned and practiced to be successful. Examines the sequence of activities needed to successfully develop and launch a new product or service; understand how the different functions and roles in product development interrelate and work together; learn how to balance strategic and tactical activities in successful product development; develop a better understanding of how to determine and satisfy customer needs; understand the financial aspects of product development; develop the skills to analyze and improve product development efforts within a company. (4 units)

Prerequisites: ISBA 3200 (Business Analytics (Predictive & Business Analytics)).

ISBA 2431. Supply Chain Management

Focuses on the key challenges and issues relating to design, analysis, and management of supply chains to gain competitive advantage. The goal of the course is to assess supply chain performance and improve execution by effectively managing inventory, capacity, logistics and supply chain relationships. Additional topics include the role of information technology in this context, supply chain network design, and managing supply chains in environments with product innovation and proliferation. (4 units)

MS Prerequisites: None. MBA Prerequisite: ISBA 3300 (Operations Management).

ISBA 2432. Global Supply Chain Management

Global supply chains are the vitally important dynamic networks that connect people, information, processes and resources across the world involved in the production and services to customers. This course explores global supply chain relationships, transactions, channels and logistics infrastructure. Trends and challenges with respect to analytics, politics, economics and sustainability concerns in global supply chains are discussed. (2 units)

Prerequisite: ISBA 3200 (Business Analytics (Predictive & Business Analytics)).

ISBA 2433. Supply Chain Analytics

This course will introduce students to a selection of important problems from all key areas of SCM-Plan, Source, Make and Distribute. Through real world case studies, students will learn about the broader business context within these problems. They will learn how to apply appropriate descriptive and prescriptive techniques to solve these SC problems, and the use of cloud based technology platforms to store, access, visualize and analyze data to make and communicate effective decisions. (2 units)

Prerequisites: ISBA 3200 (Predictive & Business Analytics).

ISBA 2434. Supply Chain Finance: Mechanism, Risk Analytics, and Technology

This course considers the framing of business problems from an enterprise resource planning (ERP) perspective. In addition, it considers the utility and selection of ERP, logistics, warehousing, and emerging supply technologies. At the end of this course students will be able to: analyze supply chain business problems from an ERP perspective, assess ERP and best of breed technologies for integrated business processes. and evaluate the utility of and select logistics, warehousing, cloud-based planning and online marketplaces. (2 units)

Prerequisite: None.

ISBA 2435. Supply Chain Technology

Prerequisite: None.

ISBA 2436. Introduction Smart Manufacturing and Industry 4.0

This course introduces students to Smart Manufacturing and Smart Supply Chains, driven by technologies like Industrial IoT, computer vision, robotics, blockchain, big data analytics, and Causal AI. Framed within Industry 4.0 and the emerging Industry 5.0, students will explore how these innovations create digital twins, enable real-time decision-making, and transform operations across industries. They will examine the impact of these technologies on operational efficiency, cost-effectiveness, product quality, and sustainability across manufacturing, distribution, and service sectors. Through lectures, case studies, and team projects, students will gain practical skills to evaluate and apply smart technologies and continuous improvement frameworks such as Lean Six Sigma in real-world supply chain contexts. (2 units)

Prerequisite: For the MBA program: ISBA3000, Statistics and Descriptive Analytics. For the MSBA and MSIS programs respectively: ISBA 2400, Math for Business Analytics, and ISBA 2401, Data Analytics with Python.

ISBA 2700/2701. Industry Practicum

Experiential Learning project for M.S. involving external industry partners. (4 units for MSFA; 6 units for MSBA/MSIS)

Prerequisite: ISBA 2401 (Data Analytics with Python).

ISBA 2702/2703. Design Capstone

Experiential Learning project for M.S. involving internal partners. (4 units for MSFA; 6 units for MSBA/MSIS)

Prerequisite: ISBA 2401 (Data Analytics with Python).

ISBA 3000. Quantitative Methods (Statistics & Descriptive Analytics)

Introduction probability and statistical analysis, emphasizing applications to managerial decision problems. Discusses descriptive statistics, probability theory, sampling distributions, statistical estimation, hypothesis testing, and simple and multiple regressions. Additional topics may include exploratory data analysis, analysis of variance, and contingency tables. (2 units)

Prerequisites: None.

ISBA 3100. Analytical Decision Making (Prescriptive Analytics & Decision Making)

This course covers how to rigorously formulate decision problems, understand mathematical optimization, deal with the uncertainties inherent in real business problems, while introducing computer modeling tools like important Excel add-ons, R, Mathematica, CrystalBall, and @Risk. (2 units)

Prerequisites: ISBA 3000 (Statistics & Descriptive Analytics).

ISBA 3200. Business Analytics (Predictive & Business Analytics)

Business Analytics is the scientific analysis of data to make better business decisions. Students in this course will learn to use analytics platforms across a wide variety of applications such as marketing, finance, and management. They will become familiar with current technological environments for statistical/machine learning and visualization. (4 units)

Prerequisite: ISBA 3000 (Statistics & Descriptive Analytics) and ISBA 3100 (Prescriptive Analytics & Decision Making).

ISBA 3300. Operations Management

This course introduces how firms get the right products and services to the right people, in the right place, at the right time and cost. In addition to firms that provide physical goods, this course covers information-enabled, supply-demand matching networks like Uber and AirBnB that vastly reduce cost and increase convenience in operationally intensive industries. (2 units)

Prerequisites: ISBA 3100 (Analytical Decision Making).

ISBA 3201. Sports Operations*

Course Description TBA (2 units)

Prerequisites: Open only to MS Sports Business students and ISBA 3000 (Statistics & Descriptive Analytics).

ISBA 3202. Sports Analytics*

Course Description TBA (2 units)

Prerequisites: Open only to MS Sports Business students and ISBA 3000 (Statistics & Descriptive Analytics).

*Course titles are subject to change