Information Systems: Courses

Formerly MK476. This course introduces students to the foundational machine learning techniques that are transforming the way we do business. Machine learning relies on interdisciplinary techniques from statistics, linear algebra, and optimization to detect structure in large volumes of data and solve prediction problems. You will gain a theoretical understanding of why the algorithms work, when they fail, and how they create value. You will also gain hands-on experience training machine learning models in Python and deriving insights and making predictions from real-world data. Prior programming experience is strongly recommended.

Provides students with an understanding of the important role that information and information technology play in supporting the effective operation and management of business. Elaborates on the themes of "place to space" and the implications for business of the digital enterprise. Focuses on learning IS concepts in the context of application to real business problems.

Combines technical and business approaches to the management of information. It will address technical issues such as cryptography, intrusion detection, and firewalls along with managerial ideas such as overall security policies, managing uncertainty and risk, and organization factors. We will examine different aspects of computer security such as password, virus protection, and managing computer security in dynamic environments. Topics will also include network security and how to secure wireless application and services. These technical details will be placed in a business context. The class will have a practical focus as we examine current "best practices" in area. There will be several guest speakers in the security area. This will be a project-oriented class and students will present their research projects during the last several classes. 4 cr.

Designed to teach business students the strategic value gained from a competitive advantage perspective around the organizational planning, implementation strategies and on-going modification strategies regarding the process of building functioning web applications on multiple platforms. The course will utilize a number of approaches throughout the semester which will enable students who are not hard-core programmers to learn what code does, how to write and utilize existing code modules and why and how it works, doesn't work, creates challenges and opportunities in an organizational structure . The goal at the end of the semester is for students to have a very thorough theoretical understanding, appreciation and application of the development process in surrounding Web strategies and applications for business.

Required for Management Information Systems concentrators. Provides a practical and theoretical introduction to data management focusing on the use of relational database technology and SQL to manage an organization's data and information. Introduces recent topics such as data warehouses and Web databases. Includes a project to design and implement a relational database to manage an organization's data. 4 cr.

This course is designed to provide the students with an overview of Agile Development methodologies. The course introduces the various methods currently used in the industry and then focuses on the primary methodologies used today, SCRUM and Kanban. Students will learn the tools of these software development approaches that produce deliverables to end users every two to four weeks. We analyze the value each of these methodologies bring into the development process and the reasoning behind a corporation selecting one method over the other (or a combination of both). In addition, the students will be introduced to CA Project Management software, the leader in the industry for SCRUM. Students will learn to analyze requirements, create backlogs, schedule "stories" to be developed, hold Standup meetings, Reviews and Retrospectives.

Required for Management Information Systems concentrators. Studies the process of designing and implementing management information systems. Students will learn to analyze organizational information requirements, develop specifications for information systems, manage systems development projects, and understand implementation issues. Key implementation concepts that affect management decisions will be discussed, and reinforced with programming examples. Design support tools will be used to support the design process. Includes a project to design an information system.

Today's most valuable and powerful companies do not offer standalone products or services, but rather platforms which enable transactions between multiple customer groups -- think Alibaba, Airbnb, Amazon, eBay, Facebook, Google, Microsoft, Salesforce, Uber, etc. This course explores the unique strategy challenges and economic foundations of such platform businesses. What makes platforms special relative to regular product businesses? Why are platforms so powerful, yet so hard to build? How should platforms be designed and priced? How much responsibility should platforms take for bad things they enable their participants to do (e.g. fake news and ad scams on Facebook, counterfeits on Alibaba)? When and how can regular products or services be transformed into platforms? How should other businesses deal with the rising power of platforms that they depend on? The course will use a mixture of conceptual frameworks, (light) economic models, and case studies to provide students with a thorough and in-depth understanding of what it takes to build or invest in platforms. Such an understanding is indispensable to anyone seeking a career at technology companies or aspiring to become an entrepreneur or venture capitalist.

Surveys the organizational implementation, uses, and impacts of advanced information technology including decision support systems, management support systems, and expert systems. Includes a group project to design and develop a decision support system.

Directed study in Management Information Systems. 2 or 4 cr. Application available on Undergraduate Program website.

This course will cover the fundamentals of programming for data science using R, the command line, and version control. These skills will be reinforced via lectures and hands-on exercises focused on elevating common programming challenges and highlight best practices. The aim of this course is to provide the pathway to fluency in the tools required to analyze data and fully manage data science projects both as an individual contributor as well as in team settings.

This course will primarily focus on data and the key techniques that are necessary when working programmatically. Data is obtained from a data source; students will learn how to work with the most common data sources and how to load it into R. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset.

This course focuses on data munging and the standard techniques that are necessary to work with any structured dataset. Students will learn how to work with the most common data sources and how to load them into python. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset. Data munging will be the core of this course, where students will learn how to clean the data, handle missing values, perform data transformations and manipulations, and prepare it for analysis. Through learning data visualization, exploratory techniques, and summarizing methods students will become competent to perform exploratory data analysis. These techniques are typically applied before any modeling begins and can help to formulate or refine the business problem. They are also stepping stones in informing the development of more complex statistical models. The course will conclude with creating data reports and interactive dashboards, two major communication tools required in any data science project.

It has been reported that as much as eighty percent of the world's data is unstructured. This course will cover the methods being applied to both unstructured and unlabeled datasets. Through a series of lectures and hands-on exercises, students will examine the techniques to unlock insights from data that appear to lack a known outcome. The goal of this course is to compare and contrast the application of various methods being applied today and provide the foundation to develop impactful insights from these datasets.

This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.

This course will introduce you to the Python programming language and the ecosystem of software packages needed for Data Science and to build and train Neural Networks in Python, including: NumPy, Pandas, SKlearn, and PyTorch. After reviewing key Python building blocks, the course will focus on Neural Networks and Deep learning Concepts and implementation in PyTorch. This is an intensive course and the majority of it will be presented through interactive python notebooks (Google Colab).

In this course we will open the neural network (NN) "black box" and examine how these mathematical modeling tools evolved to become the powerful data analysis engines that many companies rely on today. We will start with simple, comprehensible, few neuron models that we can build from scratch on our devices, and byte by byte grow our skills to understand and manipulate the enterprise- scale networks with complex architectures that are currently used in businesses ranging from Alpha-Go to Tesla. As we explore the mathematical and computational representations of different network architectures, you will obtain a solid understanding of how to choose and customize NN models that fit best to the task at hand, aware of their strengths and challenges, and what these mean for practitioners in business analytics. We will also draw examples related to global challenges such as climate crisis.

This course aims to introduce PhD students in Management to Machine Learning methods with an emphasis on their application in social science research. The first half of the course discusses popular predictive models (regression models, SVM, tree-based methods, etc) and related concepts. The second half discusses graphical models to develop and estimate probabilistic models. The course will have a set of programming/estimation assignments based on recent relevant papers and one final exam. By the end of the course, students will be equipped to spot a machine learning problem in their line of research, specify a model for it, and estimate and evaluate it.

Digital technologies are fundamentally transforming the health sector. Health information technology now permeate every segment of the health value chain, starting with the search for health information, to improving patient outcomes, to improving health. In this course students explore the evolving digital health landscape through a mix of case studies, practitioner talks, individual papers and team projects. Students will enhance their digital health requirements and systems selection toolbox. They will develop competence in current digital health technology standards, gain a deeper understanding of the strategic drivers of digital health through the eyes of the healthcare CIO and CMIO, the operational challenges from the perspective of the end user and the healthcare providers, and challenges of incorporating digital health technologies into existing workflows.

This is an applied consulting project course that aims to develop reflexive practitioners who can elicit client requirements, translate requirements into a problem statement and develop actionable solutions that meet client needs. The course uses a mix of case studies, individual memos and team project deliverables to systematically apply skills developed over the course of the MBA to solve real-world health sector problems. Students work on the consulting assignment in teams of up to four students based on having a shared interest in a prospective consulting project. These projects are curated in partnership with sponsor organizations to be executable within the framework of an academic semester. Projects in the past have ranged from improving the departmental revenue cycle within an academic medical center, developing an international pricing strategy for the introduction of a new product by a pharmaceutical company, to improving safety culture at a large hospital. These projects all have active involvement of the project sponsors who provide access to their organizations and provide ongoing feedback over the lifecycle of the consulting engagement.

This case-based course demonstrates the role that information technology plays in shaping business strategy and business models. It provides an overview of the key technologies that are important in today's business environment and introduces organization and management concepts relating to the information technology function. The course also illustrates the relationships between organizational performance and the ability to leverage knowledge assets.

This case-based course demonstrates the role that information technology plays in shaping business strategy and business models. It provides an overview of the key technologies that are important in today's business environment and introduces organization and management concepts relating to the information technology function. The course also illustrates the relationships between organizational performance and the ability to leverage knowledge assets.

The objective of this course is to provide an overview of the concept of systems architecture and how it has evolved from a technical notion to an important business issue. The course has several themes: (1) Students develop an appreciation of how a business may leverage architectural design choices for operational and competitive advantage, both at a technical and business level. (2) Students obtain insight into how interface driven systems (those using APIs) enable flexibility and increased innovation. (3) Students are confronted with the difficult aspects of modern systems, such as parallelism and concurrency, and how these technical challenges are managed. An introduction to programming in Python provides a context to help students develop a hands-on understanding of these concepts. Care is taken to not just apply the technical material to management contexts, but also inform business strategy and organization and operations through these architectural principles.

When constructing digital artifacts, either for internal or external consumption, creating a coherent and well-motivated design is essential. This class will explore the language and key principles of design and how these impact the user experience. We will also cover the essentials of project management, including scoping as a design constraint. Students will be asked to critique existing designs and offer alternatives. Additionally, we will cover the programming topic of object orientation, and study both it and the structures it enables from a design perspective.

The modern economy is driven by dynamic websites: those whose content is created on the fly by programs, not humans. In this class we will learn the technology behind both static and dynamic websites. Students will be exposed to the concepts through lectures and labs, and ultimately by the construction of a complete dynamic website in Python using our custom infrastructure built on Google App Engine.

FinTech is the intersection of finance, technology, and regulation. The course aims to help students understand FinTech and recognize the challenges and opportunities the innovations within FinTech pose. Students gain a basic understanding of the foundations and principles that enabled the rise of FinTech and examine cases for a more in-depth analysis of specific innovations such as blockchain and cryptocurrencies. The course advances students' knowledge of FinTech, much needed skill in the industry, and gives students insight into the applications, limitations, and risks of technological innovations in finance.

This non-programming-based analytics course examines how the abundance of data has transformed decision making in organizations and the strategic implications of this transformation. We explore how data are being used, ranging from the core principles of properly identifying data sources to the actual analytical methods being used to solve a wide range of business problems. Students will have some hands-on work with advanced Excel, Tableau, and two database applications, Microsoft Access and Neo4j (Neo4j is used to compare and contrast SQL and NoSQL databases in an analytics context). At the end of this course, students will have gained a big-picture perspective on business analytics as well as hands-on experience with commonly-used business analytics software.

To thrive in the modern economy, managers, entrepreneurs and investors need a thorough understanding of platform businesses. Indeed, today's most powerful and valuable firms, from Airbnb, to Amazon, Facebook and Salesforce, operate as platforms connecting buyers and sellers, or users and advertisers, or users and third-party developers. These platforms derive their value from network effects and the ability to harness innovation from their users. Drawing on cases from social media, entrepreneurship, enterprise software, mobile services, and consumer products, we will analyze and learn to launch platform startups, convert existing businesses into platforms, and compete in a platform-centric world. Students will learn to apply concepts from industrial economics, market design, and game theory to real problems.

This MBA elective (also open to undergraduates) will combine a technical and business approach to the management of information. It will address technical issues such as cryptography, intrusion detection and firewalls along with managerial ideas such as overall security policies, managing uncertainty and risk and organization factors. We will examine different aspects of computer security such as passwords, virus protection and managing computer security in dynamic environments. Topics will also include network security and how to secure wireless application and services. These technical details will be placed in a business context. The class will have a practical focus as we examine current best practices. There well be several guest speakers in the security area. This will be a project oriented class and students will present their research projects during the last several classes.

This course will introduce students to programming-based tools and techniques for becoming an analytically-minded manager. The course covers both a hands-on introduction to the concepts, methods and processes of business analytics as well as an introduction to the use of analytics as the basis for creating a competitive advantage. In the first half of the course, we will learn how to extract value from data by asking the right questions and using the appropriate analytical methods and tools. These methods comprise data preprocessing steps, explanatory analysis, optimization routines, and machine learning techniques. Having developed an understanding of the fundamentals, in the second half of the class our attention shifts toward learning how managers can use these techniques as the basis for decision making and creating competitive edge from their enterprise data. Basic programming in python (e.g., IS717, taken by all MSDi students) is a prerequisite.

This course will introduce students to programming-based tools and techniques for becoming an analytically minded manager. The course covers both a hands-on introduction to the concepts, methods and processes of business analytics as well as an introduction to the use of analytics as the basis for creating a competitive advantage. In the first half of the course, we will learn how to extract value from data by asking the right questions and using the appropriate analytical methods and tools. These methods comprise of data preprocessing steps, explanatory analysis, optimization routines, and machine learning techniques. Having developed an understanding of the fundamentals, in the second half of the class our attention shifts towards learning how managers can use these techniques as the basis for decision making and creating competitive edge from their enterprise data. No prior programming experience is required; learning basic programming is part of successful completion of the class.

The widespread proliferation of IT-influenced economic activity leaves behind a rich trail of micro-level data about consumer, supplier and competitor preferences. This has led to the emergence of a new form of competition based on the extensive use of analytics, experimentation, and fact-based decision making. In virtually every industry the competitive strategies organizations are employing today rely extensively on data analysis to predict the consequences of alternative courses of action, and to guide executive decision making. This course provides a hands-on introduction to the concepts, methods and processes of business analytics. We will learn how to obtain and draw business inferences from data by asking the right questions and using the appropriate tools. Topics to be covered include data preparation, data visualization, data mining, text mining, recommender systems as well as the overall process of using analytics to solve business problems, its organizational implications and pitfalls. Students will work with real world business data and analytics software. Where possible cases will used to motivate the topic being covered. Prior courses in analytics, data management and statistics (such as IS823) will be helpful but are not required.

This programming-based analytics course will cover how to perform statistical analysis of large datasets that do not fit on a single computer. We will design a Hadoop cluster on Google Cloud Platform to analyze these datasets. Utilizing Spark, Hive, and other technologies, students will write scripts to process the data, generate reports and dashboards, and incorporate common business applications. Students will learn how to use these tools through Jupyter Notebooks and experience the power of combining live code, equations, visualizations, and narrative text. Employer interest in these skills is very high. Basic programming in python, and basic analytics are prerequisite.

A digital strategy is an organization's plan to leverage digital technologies to create new business models, products, and services, as well as to improve internal processes and interactions with customers, suppliers, and partners. For business leaders, a digital strategy is crucial to staying competitive and grow their businesses. In this course, students will learn how to design, execute and communicate digital strategies in a complex organization. We will also learn key management concepts such as project delivery, change management, as well as financial modelling of digital investments. In addition to case studies and class exercises, we will bring in industry experts to share their real-world experience. In lieu of a final exam, students will form into project teams to design, develop, present and gain buy-in for a specific digital strategy.

This course will be a one week intensive held in Silicon Valley. The course is designed to achieve two objectives. First, to develop an appreciation for the role of Silicon Valley in Digital Innovation and, secondly, to examine how digital innovations are impacting key shifts in specific sectors. Students will be placed into teams and be expected to develop specific insights as the basis to engage in interactions with corporate executives, alumni and follow classmates. Teams will also visit leading companies involved in each sector and develop and present their team's perspective on key digital trends and leadership challenges for their sector.

This course will introduce concepts, methods, and processes of data mining and machine learning within projects that have been sponsored by partner companies. Through practice in this live setting, we will develop our analytical problem solving skills, and understand how to organize and manage agile analytical projects in the most realistic possible situation. We will learn how to collect, wrangle, and analyze both primary and secondary data sources in multiple business contexts and apply this knowledge to the client data.

Most organizations today -- of all sizes and stages of maturity -- are undertaking internally and externally focused digital initiatives. The success of these programs varies widely and depends on numerous strategic, tactical and technical factors. Foremost among them are how individuals with skills across strategy, design, product/project management, technology, and data science think individually and how they work together collectively. In this class, students will learn and apply leading thinking, practices, and tools used by top digital professionals to design and build digital products. The first half of the course will focus on cloud technology and its effect on organizational structures and product development lifecycles. In the second half of the semester, students will organize into teams to begin work on a digital experience they will design and iteratively realize the following semester in IS890, applying skills they have learned throughout the MSDT as well as new methods including exploratory ethnography, service design, agile/scrum, and data-driven experimentation.

The ability to collect, organize, access, analyze and harness data is a source of competitive advantage for some and a competitive necessity for others. Getting an organization to the point where it has a data asset it can leverage is a non-trivial task. Many firms have been shocked at the amount of work and complexity that is required to pull together an infrastructure that integrates its diverse data sources and empowers its managers. This course will provide an introduction to the concepts and technologies that are involved in managing and supporting the data assets of your organization. We will cover data modeling, relational databases, including SQL, data warehousing and business intelligence.

This course describes how digital transformation increases the reach and speed of experimentation, and how this in turn both improves execution and supports innovation. The course will examine how successful firms integrate modeling, data gathering, analysis, knowledge synthesis, with both their planning and operations. Managing for such an approach requires not just an agile, but a scaled-agile approach, with multiple parts of the firm co-evolving in a mutually supportive way. Working in small teams, students will apply the concepts to term-length projects. The class will run as an applied studio, with teams providing constructive feedback to each other throughout the term.

Graduate-level directed study in Management Information Systems. 1, 2, or 3 cr. Application available on the Graduate Center website.

Graduate-level directed study in Management Information Systems. 1, 2, or 3 cr. Application available on the Graduate Center website.

This class will cover seminal works in the economics of information including the Nobel Prize winning ideas of Akerlof, Arrow, Spence, Stiglitz, and von Hayek. It will proceed through (i) concepts of information, its value and measurement (ii) search and choice under uncertainty (iii) signaling, screening, and how rational actors use information for private advantage (iii) how to price and package information goods (iv) how properties of information cause market failure (v) macroeconomic effects of information (vi) social and legal issues of owning information. Although primarily a theory class, it should be of interest to any student applying information economics in academic, commercial, or government policy contexts. Prerequisites are a graduate course in microeconomics and mathematics at the level of introductory calculus and statistics. Students will produce a major paper suitable for publication or inclusion in a thesis.

This course covers those important Information Systems (IS) theories and topics that are at the organizational level of analysis and below. That is, it focuses on the behaviors of single individuals and small numbers of individuals, such as dyads and teams. This is consistent with an approach to organizational phenomena that distinguishes between micro and macro levels of research, this course being the micro. The focus is on ways that individuals and teams use information technologies to acquire, process, and transfer information, and the effects these technologies have on individual cognition and dyadic and group interactions. It also investigates the design and implementation of information technologies and the impact of these on organizational outcomes. The course is designed to engender students with a broad knowledge of research at the intersection of information technologies and organizations, with an emphasis on theoretical underpinnings and methodological choices.

PhD-level directed study in Management Information Systems. 1, 2, or 3 cr. Application available on the Graduate Center website.

PhD-level directed study in Management Information Systems. 1, 2, or 3 cr. Application available on the Graduate Center website.