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Digital Business APC Requirements


Pre-Admission Requirements

All Advanced Professional Certificate (APC) applicants must have:
  • A bachelor's degree
  • Two years of work experience by the time they start the program
Please review the selection criteria and application components for all APC applicants. 
 

Program Requirements

Students pursuing the Digital Business APC must meet the following requirements:
  • Complete 15 credits within 2 years
  • Take at least 12 credits from the Digital Business APC course offerings:
    • At least 6 of these credits must be taken from the group of Main Digital Business Courses listed below
    • The remaining 6 - 9 credits may be selected from the Main Digital Business Courses or the Additional Digital Business Courses listed below
  • Take no more than 3 credits of Stern MBA-level coursework outside of the Digital Business area
  • Take no more than 9 credits per semester and no more than 15 credits total
Some of the Additional Digital Business Courses require a Stern core course as a prerequisite. To take such courses, APC students must first take the required Stern core course (using the ability to take 3 credits outside of the digital business area) or demonstrate proficiency in the subject area in one of three ways:​
  • Undergraduate major/concentration
  • MBA concentration
  • Passing the Stern proficiency exam for a core course, typically available just prior to the start of a semester.

Main Digital Business Courses (at least 6 credits)

IMPORTANT NOTE: These courses are typically available to APC Digital Business students, however space and availability can change from semester to semester.

Digital Strategy (3 credits)

The course explores the role of information technology 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 (1.5 credits)

This course provides an introduction to programming languages and to 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.

Data Visualization (1.5 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. 

Dealing with Data (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. 

Design and Development of Web and Mobile Applications (3 credits)

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 - Managerial (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 and systematically about whether and 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, and 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. 

Data Science for Business Analytics (3 credits)

THIS IS THE MORE TECHNICAL VERSION OF DATA MINING FOR BUSINESS ANALYTICS [ABOVE]. 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 and systematically about whether and 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, and 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.

Globalization, Open Innovation, and Crowdsourcing: New Ways of Organizing (3 credits)

This course explores new ways in which large organizations and start-ups become innovative and efficient in today’s economy by tapping into expertise, ideas, and solutions that exist outside an organization in a new digital and global economy. While neither globalization of work nor open innovation are new phenomena, there is unprecedented growth of these practices in modern organizations enabled by new digital platforms. In this course, we will discuss how to use these practices effectively and how to evaluate their risks and benefits 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; and 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.

Emerging Technology and Business Innovation (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, and 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 intended for students who have already acquired basic knowledge of technical concepts, and want to advance their knowledge of technologies beyond the basics and further develop an understanding of the dynamics of the spaces associated with these technologies.

 

 

Additional Digital Business Courses (up to 9 credits)

IMPORTANT NOTE: These courses are typically available to APC Digital Business students, however space and availability can change from semester to semester.

The Business of Social and Other Networks (3 credits)

This course analyzes the economics of social networks, such as Facebook and Twitter as well as other networks, such as the Internet, banking networks, mobile money transfer networks, and credit card networks. It also covers related industries such as ebooks, app-based taxi cabs, and electric cars filling stations. Starting from an analysis of social networks, we develop a general theory of platform competition, where the platform may be a network such as Facebook but can also be an operating system such as the iOS, Android, or Windows. We examine how networks are formed from the perspective/incentives of users, the network (platform) operator, and the application providers that are complementary to the network. We identify key features of networks including: (i) higher value to users from networks of larger size; (ii) very significant inequalities in market share, profits, and (often) prices; (iii) the extent of incentives for interoperability and interconnection between networks; and (iv) importance of key network nodes that are "central" or "influential" in the creation and stability of networks. 

Robo Advisors & Systematic Trading (1.5 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 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.

Fundamentals of Digital Marketing Technologies (3 credits)

The purpose of the course is to introduce students to the complex world of technology and data enabled marketing and the vast ecosystem that is contributing to its rapid advancement. While the early applications of digital marketing technology may be credited to digital advertising pioneers such as Google, Doublclick, and Yahoo, the scene today reflects even traditional media (television) channels shifting to digital technologies for media planning and buying and as well for audience targeting. To a large degree, the overwhelming success of the internet can be attributed to the network’s intrinsic ability to work with data, thus better understanding the needs, attitudes, and behavior of its users. This in turn leads to tailoring services and products, fostering innovation on behalf of consumers and businesses, and encouraging competition and competitiveness. Probably one of the most important tools that lead to, and continues to aid, this better understanding is marketing’s use of digital technologies and analytics to improve consumer experiences with every iteration or web interaction; marketing technologies are currently being used by virtually all websites and online services, and knowledge of how digital marketing works is essentially a prerequisite for any online business.

Social Media and Digital Marketing (3 credits)

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 is 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 can help tease out correlation from causality such as randomized field experiments. 

Financial Information Systems (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 (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.

Entrepreneurship and the New Economy (1.5 credits)

The course objective is to expose students to the skills and knowledge required to be successful entrepreneurs in the new economy. For this course, the new economy is defined as the market opportunity brought about by changes in information technology and global internet penetration. These changes are catalyzing the emergence of new business types (e.g., ecommerce, social media, virtual goods) and new business models (e.g., crowd sourced content, affiliate referrals, micro-payments). The course will examine the capabilities required to build a new economy business, how entrepreneurs are using technology to create businesses, and the key success factors for building a viable "new-economy"-based business. The course objective will be achieved through four methods: 1) Class lectures on new economy and entrepreneurship subjects such as business models, networking, market evaluation, online marketing, product development, and the use of social media; 2) Homework exercises designed to reinforce the knowledge gained in class by analyzing the applications in real business situations; 3) Guest lecturers who are entrepreneurs and company founders, able to speak from experience about the realities of starting, growing, and managing a business; and 4) Development of a Company Business Plan for a technology-based business.

​Technology Innovation Strategy (1.5 credits)

The purpose of this course is to expose you to the dynamics of industries driven by technological innovation, and to train you to think strategically about technological innovation. In this course, we will tackle such questions as: How and why are dominant standards chosen in "winner-take-all" industries?; How do firms choose among multiple attractive innovation projects?; How do firms decide whether to "go it alone" or collaborate, and how do firms develop an effective collaboration strategy?; and How do firms make the difficult choice between protecting their technologies with patents or copyrights, versus rapidly disseminating them to build installed base and complementary goods?. The course will be lecture, case, and discussion based. Like the industries we will study, the course will be fast-paced, challenging, and exciting. 

Foundations of Technological Entrepreneurship (3 credits)

This course is designed to help students understand and deal successfully with issues typically faced by technology entrepreneurs, or managers who work in a technology-based startup. It may also be of interest to those who are considering a job that involves dealing with technology-based new ventures or technology commercialization processes in a consulting or investment role. Technology entrepreneurship is defined as the entire process of technology-based enterprise creation from ideation and invention through technology transfer and commercialization to growth of high tech firms. Relevant areas of technology innovation include, but are not limited to, computer hardware and software, communication, security, transportation, imaging, chemicals, optics, life sciences, and clean environment technology. In contrast to other entrepreneurship courses offered in the MBA curriculum, this course focuses on learning how to identify and evaluate a good technology commercialization opportunity, how to determine the best business approach for commercialization, and how to work with technology inventors and scientists to develop a workable business concept. Other learning objectives include: how to attract and deal with potential investors, how to select and properly award key talent, how to manage organizational transition, and how to evaluate exit options. As part of the learning process of this course, students will be required to work in teams to develop a first stage assessment of the potential commercial viability of a new technology that that they will select. To that end, this course provides a unique opportunity for MBA students to work with the inventors of a new technology that solves an important problem in a way that can potentially be commercialized into a profitable business.

​Decision Models and Analytics (3 credits)

This course introduces the basic principles and techniques of applied mathematical modeling for managerial decision making. Students learn to use some of the more important analytic methods (e.g., spreadsheet modeling, optimization, Monte Carlo simulation) to recognize their assumptions and limitations and to employ them in decision making. Students learn to: develop mathematical models that can be used to improve decision making within an organization, sharpen their ability to structure problems and to perform logical analyses, translate descriptions of decision problems into formal models and investigate those models in an organized fashion, identify settings in which models can be used effectively, and apply modeling concepts in practical situations. Students also strengthen their computer skills, focusing on how to use the computer to support decision making. The emphasis is on model formulation and interpretation of results, not on mathematical theory. This course is aimed at students with little prior exposure to modeling and quantitative analysis, but it is appropriate for all students who wish to strengthen their quantitative skills. The emphasis is on models that are widely used in diverse industries and functional areas, including finance, operations, and marketing. 

​Digital Marketing (3 credits)

This course 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 are 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.

Pre-requisite: Marketing Core Course

Tech Product Management (1.5 credits)

This course is designed to provide you with a framework for understanding product management for technology products within a range of organizations large and small. The course covers tangible tools, techniques, best practices, and real world simulation of what a product manager faces in trying to deliver against product, company, and user objectives.

Pre-requisite: Marketing Core Course
 

Entertainment and Media Industries (1.5 credits)

This course serves as a foundation for those interested in Stern's Entertainment, Media, and Technology program. It provides a framework for understanding the key marketing, economic, and strategic issues facing organizations in the entertainment industry. It covers key sectors of the entertainment industry, focusing on film, television, home video, cable, music, publishing, sports, and new media. The course utilizes lectures and cases studies.

Pre-requisite: Marketing Core Course

Social Media (1.5 credits)

This course is designed to provide managers with a framework for understanding and succeeding in the social media space. The course covers trends in the industry and foundational pieces, including but not limited to: social business, social features, analytics, and sustainability. In this course you will learn the basic concepts, terms, and principles that apply to the social media industry, analyze the activities of the leading social media companies and applications through articles, case studies, and lectures, become familiar with key strategic issues across all the social media sectors, and gain an understanding of and an appreciation for the challenges involved in managing social media products. The final project is designed to give you an opportunity to use multiple perspectives to improve a company's social media strategy or social business culture.

Pre-requisite: Marketing Core Course

New Media in Marketing (1.5 credits)

This course will look to provide a framework for understanding the various technologies impacting the media in the marketplace today – using subjects both ripped from the headlines and grounded in near-term history – as well as provide a structure for assessing the opportunities and challenges of innovations in the 3-5 year time horizon. It is designed to help students become effective marketers in the 21st century. Topics covered will include the digital home, web 2.0, social media, online video, digital advertising, video-on-demand, mobile applications, gaming, sports technologies, and interactive TV.

Pre-requisite: Marketing Core Course

​Digital Media Innovation (3 credits)

This course is designed to provide an orientation to the best current digital marketing practices. We will examine the inner workings of some of the most interesting and fastest growing digital companies, and meet some of the leaders of these companies for a first hand view of how digital marketing is evolving and progressing. Course objectives are: 1) To understand the most practiced forms of digital marketing (e.g., social marketing, local marketing, search engine marketing, brand marketing) and how the venture capital world views these tools and approaches; 2) To learn how advertisers and publishers are working together in the digital world to entice consumers with “authentic” marketing practices; 3) To become familiar with some of the best practices in digital marketing; and 4) To learn how to create and present a new business idea in the digital marketing space to the venture capital community.

Pre-requisite: Marketing Core Course

Data Driven Decision Making: Managerial (3 credits)

Regardless of your chosen field or major, it is virtually impossible to survive in the professional world without a working knowledge of basic data analysis and use of some statistical software. The course is designed to expose and train you in a wide spectrum of problems that you are likely to encounter in your workplace. Extracting useful insights from the vast amount of information involves a combination of analytical skills and intuition. It is both art and science. The pedagogic philosophy in this course embraces the principle of learning-by-doing. Each concept that we cover has a software implementation and a problem/case whose resolution can be enhanced through the use of data. Statistical tools covered in the class will range from simple data analysis and visualization, to advanced methods such as non-linear regressions, multivariate statistics, and mining of ‘unstructured’ data. Our emphasis will be on applications and interpretation of the results for making business/policy decisions. Beyond what is necessary, we will focus less on the mathematical and statistical properties of the techniques used to produce these results. 

​Pre-requisite: Statistics and Data Analysis Core Course

Data-Driven Decision Making: Technical (3 credits)

The specific objectives of this course are to: 1) Help you understand how analytical techniques and statistical models can help enhance decision making by converting data to information and insights; 2) Provide intuition for data driven decision making by using practical examples from a wide spectrum of fields; 3) Provide insight into how to choose and use the most effective statistical tool based on the problem at hand; 4) Provide you with a software tool kit that will enable you to apply statistical models to real decision problems; and 5) Most importantly, remove any fear of data analysis and increase your comfort level with analyzing databases most commonly used in the business world. 

Pre-requisite: Statistics and Data Analysis Core Course

​Regression and Multivariate Data Analysis (3 credits)

This is a data-driven, applied statistics course focusing on the analysis of data using regression models. It emphasizes applications to the analysis of business and other data, and makes extensive use of computer statistical packages. Topics include simple and multiple linear regression, residual analysis and other regression diagnostics, multicollinearity and model selection, autoregression, heteroscedasticity, regression models using categorical predictors, and logistic regression. All topics are illustrated on real data sets obtained from financial markets, market research studies, and other scientific inquiries. 

Pre-requisite: Statistics and Data Analysis Core Course

Forecasting Time Series Data (3 credits)

Presented in this course are practical time series forecasting techniques with emphasis on the Box-Jenkins ARIMA (autoregressive integrated moving average) method, conditional volatility ARCH (autoregressive conditional heterogeneity), and GARCH (generalized autoregressive conditional heterogeneity) models. The course gives a mix of practical data analysis along with an introduction to the relevant theory. The ARIMA models are used to forecast series like interest spreads, while ARCH models are used in estimating and forecasting the volatility of series like stock returns and exchange rate returns. Students analyze data sets of their own choice in projects. Additional topics of interest covered in the course are methods of testing for nonstationary (Dickey-Fuller tests) as well as models for capturing seasonality as seen, for example, in series of monthly sales figures. The low-cost forecasting method of exponential smoothing is discussed, and its connection to the RiskMetricsTM methods of J. P. Morgan and GARCH models is explored. If time permits, we also study methods of forecasting multivariate time series, where information from several series is pooled to forecast a single series. The concept of co-integration or co-movement of multivariate series is discussed (interest rates being a prime example), along with their implications for forecasts. Other potential topics in the course include the use of ARCH models in value at risk (VAR) analysis and in option pricing.

Pre-requisite: Statistics and Data Analysis Core Course

Applied Stochastic Processes for Financial Models (3 credits)

In this class we study stochastic models for the financial markets mostly in a discrete time setting. We discuss the concept of martingales and risk-neutral probability measures, and derive the general pricing formula for contingent claims. We study the binomial model and derive the price of a European call option on this model, called the binomial Black-Scholes (BS) formula. We study put options using the put-call parity. We compare the binomial BS formula to the continuous time BS formula, and analyze the latter via the “Greeks”. We also look at exotic options such as the lookback and the knockout option. Additionally, American options, forward and future contracts, and fixed income models will be included.

Pre-requisite: Statistics and Data Analysis Core Course

Mathematics of Investment (3 credits)

The course discusses mathematical and technical aspects of investments. Topics include measurement of interest and discount rates, accumulated value and present value, annuities, sinking funds, amortization of debt, and determination of yield rates on securities. Applications include bond evaluation, mortgages, capital budgeting, depreciation methods, and insurance.

Pre-requisite: Statistics and Data Analysis Core Course

​Introduction to the Theory of Probability (3 credits)

This course covers the basic concepts of probability. Topics include the axiomatic definition of probability; combinatorial theorems; conditional probability and independent events; random variables and probability distributions; expectation of functions of random variables; special discrete and continuous distributions, including the chi-square, t, and F distributions; joint distributions with emphasis on the bivariate normal distribution; law of large numbers; central limit theorem; and moment generating functions. The theory of statistical estimation is introduced with a discussion on maximum likelihood estimation.

Pre-requisite: Statistics and Data Analysis Core Course

Statistical Inference and Regression Analysis (3 credits)

The course has two distinct components: statistical inference and regression analysis. Topics included in statistical inference are principles of statistical estimation and inference, Neyman-Pearson Lemma, testing of means, variances, tests of independence, and nonparametric methods. Regression analysis focuses on the general linear regression model, least squares estimation, departures from standard assumptions, autocorrelation, multicollinearity, analysis of residuals, choice of variables, and nonlinear models.

Pre-requisites: Statistics and Data Analysis Core Course & Introduction to the Theory of Probability
 

Introduction to Stochastic Processes (3 credits)

This is an introductory course in stochastic processes. The course places emphasis on probabilistic thinking and on learning how to model the real-life phenomena, which evolve over time. It presents classes of stochastic processes which are widely used as modeling tools in diverse fields of applications including finance, economics, accounting, marketing, and actuarial science. It covers basic theory and applications of discrete and continuous- time Markov chains; discrete and continuous time martingales; and Brownian motion and its generalizations. An introduction to stochastic calculus is presented with a view towards financial applications. The course also discusses some statistical aspects of considered processes.

Pre-requisite: Statistics and Data Analysis Core Course