Department of Technology, Operations, and Statistics | Information Systems

Digital Strategy
INFO-GB.2318
3 credits

The course explores the role of information technology (IT) in corporate strategy with specific attention paid to the Internet. Different Internet business models are identified and are used to explain competitive practices. Cases and lectures illustrate how technology is used to gain and sustain a competitive advantage. The course also describes different Internet technology infrastructures and identifies issues in managing a firm's technology as a strategic asset.

Programming in Python and Fundamentals of Software Development
INFO-GB.2335
3 credits

This course provides an introduction to programming languages and to the software design methods. The programming language of choice is Python. However, the course will introduce the students to the fundamental programming concepts appearing in various other programming languages, including Java and C, that go well beyond the specifics of Python. Upon completion of this course, the students will be able to acquire practical programming skills in Python and understand the principles of structured software development. They will also understand the principles of designing large software systems and what it takes to plan, analyze, design, implement and support large Information Systems throughout their entire System Development Lifecycle.

Intro to Data Science for Business Analytics
INFO-GB.2336
3 credits

THIS IS THE MORE TECHNICAL VERSION OF DATA MINING FOR BUSINESS ANALYTICS [SEE INFO-GB 3336].  SOME PROGRAMMING EXPERIENCE REQUIRED.  Businesses, governments, and individuals create massive collections of data as a by-product of their activity. Increasingly, data is analyzed systematically to improve decision-making.  We will examine how data analytics technologies are used to improve decision-making.  We will study the fundamental principles and techniques of mining data, and we will examine real-world examples and cases to place data-mining techniques in context, to improve your data-analytic thinking, and to illustrate that proper application is as much an art as it is a science.  In addition, we will work hands-on mining data using Python and its data science libraries.  After taking this course you should: (1) Approach problems data-analytically. Think carefully & systematically about whether & how data can improve business performance, to make better-informed decisions. (2) Be able to interact competently on business analytics topics.  Know the fundamental principles of data science, that are the basis for analytics processes, algorithms, & systems.  Understand these well enough to work on data science projects and interact with everyone involved.  Envision new opportunities. (3) Have had hands-on experience mining data.  Be prepared to follow up on ideas or opportunities that present themselves. 
 

Tech and the City: Customer-Centric Digital Entrepreneurship
INFO-GB.2345
3 credits

Have you ever wondered what it’s like to run a high-tech startup? This course provides students with immersive experiential learning about digital entrepreneurship through the lens of successful early-stage technology companies. Student teams are each embedded for a semester into different New York City-based startups from the investment portfolios of Union Square Ventures and other leading tech-focused venture capital firms. Over the course of this immersion, students work with founders and investors to understand business models, assess metrics and their connection to growth and funding, and lead a customer-centric assessment of the company’s products. Weekly critical reflection activities that include structured discussions, journal writing and in-class peer presentations coupled with guest sessions from industry experts allow students to deepen their understanding of both their own company as well as the other participating startups. They emerge from the course with an experience-based appreciation of the transformative potential of digital technologies, of the vibrant tech entrepreneurship environment of New York City, and of the risks faced by high-tech startups that underinvest in understanding their customers.
 

Dealing with Data
INFO-GB.2346
3 credits

The volume of data being generated every day continues to grow exponentially. We capture and store data about pretty much every aspect of our lives. Being able to handle and analyze the available data is now a fundamental skill for everyone. The objective of this course is to challenge and teach students how to handle data that come in a variety of forms and sizes. This course guides students through the whole data management process, from initial data acquisition to final data analysis. The (tentative) list of topics that we plan to cover: Unix tools Regular expressions Data formats: XML, JSON, YAML, etc. Accessing data sources: Crawling, parsing HTML, APIs Data modeling and ER model Relational databases and SQL NoSQL databases and MongoDB Data cleaning Crowdsourcing for data management Textual data and natural language processing tools Handling time series, dates, timezones, etc Handling spatial data, maps, ets Handling image/audio/video data using signal processing Handling social media and network data Basic predictive modeling techniques Visualization Big Data: Hadoop, HBase, Pig.

Trading Strategies and Systems
INFO-GB.2350
3 credits
Prior to a major course revision, this course was offered as B20.3350 Financial Information Systems.

As financial markets become more electronic and more liquid, a higher degree of knowledge about systems and analytics is required in order to compete. This course teaches students how to use the information emanating from the markets for decision making and building and implementing systematic computer-based models for trading. The course begins with a description of the financial markets, specifically, equity, currency, fixed income, and commodities, and the systems that enable them. We consider exchanges, ECNs, and other dealer markets and the information that emanates from them. This provides the backdrop for the bulk of the course which covers the design, evaluation and execution of trading strategies that are commonly used by professionals in the various markets. There is increasing interest in particular, on /systematic/ trading strategies and execution systems because of their scalability and transparency. The course should be of interest to students across the financial services industry. It will not transform you into a trading expert, which takes considerable effort, time, and pain. It will, however, bring the concepts of risk and return alive by working with real data and exercises, and through industry experts describing their approach to fund management and administration. More generally, the course should give you a clearer appreciation on the fact that understanding markets is a theory building exercise, where professionals spend a lot of time in understanding emerging market phenomena with the objective of translating their insights into profitable strategies. These concepts are useful regardless of your specific interest in the financial industry, i.e., whether you intend to be a trader, risk manager, controller, salesperson, or analyst.

Data Visualization
INFO-GB.3306
3 credits

This course is an introduction to the principles and techniques for data visualization. Visualizations are graphical depictions of data that can improve comprehension, communication, and decision making. In this course, students will learn visual representation methods and techniques that increase the understanding of complex data and models. Emphasis will be placed on the identification of patterns, trends and differences from data sets across categories, space, and time. Throughout the course, several questions will drive the design of data visualizations some of which include: Who’s the audience? What’s the data? What’s the Task? This is a hand-on course. Students will use several tools to refine their data and create visualizations. These may include: R, Python, ManyEyes, HTML/CSS, JavaScript (D3 Framework), Google Fusion tables, Google Refine, Google Charts, Adobe Illustrator, and Excel. To learn more watch the course preview: http://www.youtube.com/watch?v=frwl-YVtmrs

Social Media and Digital Marketing Analytics
INFO-GB.3310
3 credits
Prerequisites: COR1-GB.1305

The emergence of the Internet has drastically changed various aspects of a firm’s operations. Some traditional marketing strategies are now completely outdated, others have been deeply transformed, and new digital marketing strategies are continuously emerging based on the unprecedented access to vast amounts of information about products, firms, and consumer behavior. From Twitter to Facebook to Google to Amazon to Apple, the shared infrastructure of IT-enabled platforms are playing a transformational role in today’s digital age. The Internet is now encroaching core business activities such as new product design, advertising, marketing and sales, creation of word-of-mouth and customer service. It is fostering newer kinds of community-based business models. Traditional marketing has always been about the 4Ps: Product, Price, Place, and Promotion. This course will examine how the digital revolution has transformed all of the above, and augmented them with the 5th P of Participation (by consumers). While there will be sufficient attention given to top level strategy used by companies adopting social media and digital marketing, the focus of the course is also on analytics: how to make firms more intelligent in how they conduct business in the digital age. Measurement plays a big role in this space. The course is complemented by cutting-edge projects and various business consulting assignments that the Professor has been involved in with various companies over the last few years. We will learn about statistical issues in data analyses, assessing the predictive power of a regression, various econometrics-based tools such as simple and multivariate regressions, linear and non-linear probability models (Logit and Probit), estimating discrete and continuous dependent variables, count data models (Poisson and Negative Binomial), cross-sectional models vs. panel data models (Fixed Effects and Random Effects) and various experimental techniques that help can tease out correlation from causality such as randomized field experiments. 

Design and Development of Web and Mobile Apps
INFO-GB.3322
3 credits
Prerequisites: B20.2317 or equivalent background as well as the ability to program in some programming language.

The World Wide Web and the new technologies and standards surrounding it have dramatically changed the way systems are developed and used in organizations and markets. This course covers the issues and concepts in developing data-driven Web sites. Students evaluate a variety of different Web development approaches and architectures, including the common gateway interface model, Java, Active Server Pages, Dot Net, and Web Services. A variety of alternative development approaches are compared, looking at issues such as the development environment and the security, performance, scalability, and maintainability of systems developed with the different approaches. The class is divided into student teams. Each team implements a small system using one of the supported technologies and evaluates their experience. Students should have the ability to build a simple Web page and be proficient with common Microsoft office business applications, especially ACCESS. There is light programming, which is used as an example of how to build dynamic Web pages for B2C and B2B sites. Assignments include both Active Server Pages as well as J2EE. Unix, Windows 2000, and Linux platforms are available to host projects.

Data Mining for Business Analytics
INFO-GB.3336
3 credits

Businesses, governments, and individuals create massive collections of data as a by-product of their activity. Increasingly, data is analyzed systematically to improve decision-making.  In many cases automating analytical processes is necessary because of the volume of data and the speed with which data are generated.  We will examine how data analytics technologies are used to improve decision-making.  We will study the fundamental principles and techniques of mining data, and we will examine real-world examples and cases to place data-mining techniques in context, to improve your data-analytic thinking, and to illustrate that proper application is as much an art as it is a science.  In addition, we will work hands-on with data mining software.  After taking this course you should: (1) Approach business problems data-analytically. Think carefully & systematically about whether & how data can improve business performance, to make better-informed decisions. (2) Be able to interact competently on business analytics topics. Know the fundamental principles of data science, that are the basis for analytics processes, algorithms, & systems.  Understand these well enough to work on data science projects and interact with everyone involved.  Envision new opportunities. (3) Have had hands-on experience mining data.  Be prepared to follow up on ideas or opportunities that present themselves, e.g., by performing pilot studies. 

Financial Information Systems
INFO-GB.3350
3 credits

As financial markets become more electronic and more liquid, a higher degree of knowledge about systems and analytics is required in order to compete. This course teaches students how modern financial markets function as a network of systems and information flows, and how to use information technology for decision making in trading and managing customer relationships. Information systems serve two purposes in the financial industry. First, they facilitate markets and their supporting services such as payment, settlement, authentication, and representation. Second, they facilitate or engage in making decisions such as when and how much to invest in various instruments and markets. The first part of the course describes how systems facilitate various kinds of payment and settlement mechanisms, enable financial markets such as exchanges and ECNs, and support inter-institution communication. The second part of the course describes how traders, analysts, and risk managers use systems to cope with the vast amounts of data on the economy, markets, and customers that flow into their systems each day. It covers automated trading systems and other types of customer-oriented analytic systems that are becoming increasingly intelligent in how they make or support decisions. The course features a mix of case studies, Excel-based illustrations and assignments, and the latest industry tools. It is particularly suited for finance and marketing students interested in understanding information technologies in financial services from a practical career standpoint.

Risk Management Systems
INFO-GB.3351
3 credits

In today's world of complex financial engineering, rising volatility, and regulatory oversight, prudent management increasingly requires understanding, measuring, and managing risk. Banks, securities dealers, asset managers, insurance companies, and firms with significant financing operations all require real-time, enterprise-wide risk management systems for handling market, credit, and operational risk. Such systems establish standards for aggregating disparate information, including positions and market data and operational risk, calculating consistent risk measures, and creating timely reporting tools. This course is directed toward both finance and technology oriented students who are interested in understanding how large-scale risk systems need to be evaluated, acquired, architected, and managed. It identifies the business and technical issues, regulatory requirements, and techniques to measure and report risk across an organization or market.

Globalization, Open Innovation, and Crowdsourcing: New Ways of Organizing
INFO-GB.3355
3 credits

This course explores new ways in which organizations become innovative and efficient in today’s economy by tapping into expertise that exists outside firm’s boundaries and its major geographical locations. While neither globalization of work nor involving other firms or customers into a firm’s innovation processes is new per se, there is unprecedented growth of these practices in modern organizations enabled by new technological platforms. Yet, the practices of opening up the enterprise through offshoring, outsourcing, and crowdsourcing knowledge work come with certain costs and risks of failure. In this course, we will discuss how to evaluate risks and benefits of such practices by doing qualitative analysis of cases, discussing strategic theories, learning decision making tools, and engaging in real-time crowdsourcing projects. Specific topics covered include: 1) strategic considerations of whether an activity should stay within or outside the firm boundaries; 2) strategic evaluation of geographical locations for a particular type of knowledge work; 3) vendor competencies: how to grow them as a provider and how to evaluate them as a client; 4) when and how to partner for product innovation; 5) how to organize a crowd of customers or experts; 6) contracting with and governing of strategic vendors; 7) enabling innovation in distributed teams. This course is designed to give students a truly multidisciplinary perspective on these issues drawing on theories and practices from international business, strategy, and innovation management.
 

Practical Data Science
INFO-GB.3359
3 credits

This class is an introduction to the practice of data science. The student will leave the class with a broad set of practical data analytic skills based on building real analytic applications on real data. These skills include accessing and transferring data, applying various analytical frameworks, applying methods from machine learning and data mining, conducting large-scale rigorous evaluations with business goals in mind, and the understanding, visualization, and presentation of results. The student will have experience processing "big data," the latest buzz concept in a field awash with buzz. Specifically, the student will be able to analyze data that are too big to fit in the computer's memory, and therefore thwart many standard analytical tools. The student will have experience with unstructured data, for example processing text for applications such as sentiment analysis of user-generated content on the web.

Emerging Technology and Business Innovation
INFO-GB.3362
3 credits

This course provides a thorough examination of several key technologies that enable major advances in e-business and other high-tech industries, and explores the new business opportunities that these technologies create. For each of these technologies, it provides an overview of the "space" corresponding to this class, examines who the major players are, and how they use these technologies. Students then study the underlying technologies; examine the business problems to which they can be applied; and discuss how these problems are solved. Key companies in the "spaces" created by these technologies are also studied: what these companies do; which technologies they use; how these technologies support their critical applications; and how these companies compete and collaborate among themselves. Moreover, the course examines possible future directions and trends for the technologies being studied; novel applications that they enable; and how high-tech companies can leverage applications of these technologies. This is an advanced course, and it is intended for the students who have already acquired basic knowledge of technical concepts and who want to advance their knowledge of technologies beyond the basics and to further develop an understanding of the dynamics of the "spaces" associated with these technologies.

Networks, Crowds, and Markets: Reasoning about a Highly Connected World
INFO-GB.3383
3 credits

Professor Foster Provost with Professors Sinan Aral, Yannis Bakos, Panos Ipeirotis, Arun Sundararajan and other guests (e.g., hopefully Duncan Watts and others).

This is a course on how the social, technological, and natural worlds are connected, and how the study of networks sheds light on these connections. Topics include: social network structure and its effect on business, culture, and the propagation of information, fads, disease, etc.; the technology, economics, and politics of social networks, Web information, and on-line communities; small worlds, network effects, and "rich-get-richer" phenomena. The power of networks for prediction, including topics like social-network advertising, the power of the network for web search, and the melding of economics, modeling, and technology into "prediction markets."

The class will be a combination of lectures based on the textbook, guest lectures from our faculty who are well-known experts on this topic, and guest lectures from outside speakers.

Textbook: Networks, Crowds, and Markets: Reasoning About a Highly Connected World, by David Easley and Jon Kleinberg. http://www.cs.cornell.edu/home/kleinber/networks-book/networks-book.pdf.

We won't follow the book chapter-by-chapter, but will try to cover broadly the topics covered throughout the book, as we can fit into one semester.

Digital Marketing
INTA-GB.3340
3 credits

This core course of the Digital Marketing specialization at Stern addresses a fundamental business question of the digital age: how to increase shareholder value through digital media. This is a question that all firms are currently struggling to answer in an era where they can, for the first time, truly engage in rapid two-way conversations with potential and current customers. If firms ask themselves the question “how do we attract and retain customers?” chances that the answer to this looks very different from what it was a decade ago when the Internet was still in its infancy. At the current time, reputations can be made or destroyed within minutes, presenting great opportunity as well as a high degree of risk.

The focus of the course is on how to make firms more intelligent in how they conduct business in the digital age. This requires a fundamental understanding of the technologies and platforms that form the backbone of electronic commerce, the ability to govern and leverage large amounts of data that are generated as a by-product of electronic interactions, and sociological norms and individual preferences. Measurement plays a big role in this space. As a modern-day famously remarked “In God we believe, everyone else please bring data.”

The course will feature (at least) two instructors who will provide complementary perspectives on branding, analytics, social media, and strategy. There will be several (roughly 6) senior executives from companies providing a detailed look at what their companies are doing in the digital space. There will be several assignments and a term project for this course. The project, done in teams, will involve the assessment of the "Digital IQ" of a firm of your choice and a set of actionable recommendations for the firm based on your audit. Considering the nature of the material there is no textbook for this course. Materials will consist of readings, links to websites, and datasets.