Leadership for a Digitally Driven World ^TM

This course aims to develop an understanding of firm information assets, the cybersecurity risks that threaten these, and the risk management principles that best protect an entity's information assets against those risks. Students gain an understanding of information security and controls in the minimization of business disruption and financial reporting risks.

This course, the first one in a series of three courses in the new Data Analytics track, aims to build a strong foundation in data and analytics strategies and techniques. This non-technical course focuses on understanding general data and analytics approaches by using a combination of class lecture, case studies, and project work. Students are introduced to a wide range of data and analytics concepts. The extensive use of case examples reflects a focus on data analytics topics and issues that professional service organizations are helping corporate America address.

This course introduces machine learning and its application in finance, investment, and fintech. It covers fundamental statistical and machine learning methods as well as real world financial applications of these methods using Python programming. Topics covered include regression and classification methods, tree-based models, SVM, Neural Network, Advanced time-series models, Natural Language Processing, etc. Some of the applications include business and financial market forecast, risk prediction and fraud detection, sentiment analysis, etc.

The overall goal of this course is to enable students to grasp fundamental understanding of machine learning models and provide a good foundation for the students as machine learning "users" in their real jobs. This course is quantitative oriented, however, the students are not expected to memorize all the equations, code entire models in details, or even develop new algorithms, as those are for the machine learning "scientists and technicians" (think about car drivers vs. car technicians). We emphasize more on the fundamental thinking behind the models and how to apply models in the real word. In another word, we teach you how to "drive" machine learning "car."

This course is an introduction to programming and data analysis for business utilizing the Python programming language. Python is a high-level, general-purpose language that is one of the most popular languages used in FinTech today. It is also hugely popular in data science and machine learning. Practical coding and data analysis are emphasized. The course begins with basic Python syntax and programming. It then moves on to computational problem-solving techniques and data analysis using popular Python packages. The course culminates with machine learning by way of Python application tools. Skills obtained in this course can easily be transferred to other languages. No prior programming knowledge is required.

This course covers applications of machine learning methods, such as supervised learning classifiers and unsupervised algorithms, to create computer-based models to understand and analyze text data, e.g., for text categorization, sentiment analysis, clustering documents based on similarity, etc. Students explore these methods on numerous data sets from the industry.

This course examines business strategies for firms that make digital products and services. These include computer hardware and software, smart phones and apps, network devices, data analytics, mobile communications, Internet-of-things, and infrastructure such as cloud computing. They include a large share of the world's most valuable and innovative companies, and the technologies they create are used by businesses and consumers in nearly every aspect of life. Their economic, political, social and human impacts are tremendous. Love them or hate them, we can't ignore them. For all of these reasons, it is critical for us to understand how and why the people and firms in these industries think and act.

The objectives of this course are to help you understand the distinctive features of digital products and services, the strategies of firms in those industries, and the broader ecosystems that characterize the industries. By the end of the course you should be able to (1) understand basic economics of information and networks, (2) apply the concepts of platform competition (which you have been introduced to in F207) to digital firms, (3) understand how firms innovate and capture value from innovation in digital industries, (4) apply theories from strategy to digital markets, (5) understand and explain the relationship of globalization and government policies to digital strategies.

The insights from this course are applicable not only to digital firms and industries, but also to other industries that apply their innovations for their own products or services, such as healthcare, banking, biotech, transportation, education, finance, retail, manufacturing and energy. If you want to understand the digital innovations that are arriving faster than ever and having ever greater impacts on all industries, you need to understand the firms that are creating and delivering those innovations. This class will help you to do so.

This course provides introductions to students with an architectural understanding of 'why' and 'how' blockchain was created, what problem it solves, and prior efforts to resolve said problem. Additionally, this course gives students a Bitcoin overview, including history, actors, mechanisms, and issues, as well as an overview of altcoins (e.g., Ethereum). Finally, students gain an understanding of the challenges and risks involved with this new blockchain technology and cryptocurrency landscape.

[New AY19-20 course. Awaiting syllabus and course description.]

This course provides students the fundamentals of deep learning which is driving many business applications in image recognition, natural language understanding, language translation, and conversations. Also, it introduces students to Keras and TensorFlow for designing their deep learning projects and many approaches to deep learning – ANN, CNN, and RNN.

The course is designed at four levels of depth. The first level introduces students to deep learning as end-user in our day-to-day activities. At this level, students understand how the performance of these models is measured and how they are deployed. The second level explores how a pre-built deep learning model can be customized. At this level, students focus on customization tooling and supervision options. The third level expands on how students can build a deep-learning model from scratch using standard components. This level exposes students to additional capabilities, such as hyper-parameters and how they can be tuned. Finally, the fourth level shows students how researchers are developing new deep-learning models using mathematical and statistical tooling, and exposes to some advanced topic in early research.

[New AY19-20 course. Awaiting syllabus and course description.]

As businesses increasingly operate like tech companies, future executives must possess an understanding of the role and impact of software in business: what software makes possible, what problems it can solve, and how software technologies will advance in the future. To achieve this goal, this class discusses key developments and advances in software technologies, highlight how the different components of software (ranging from those that interact closely with the machine to those that interact with users) work together, teach systems thinking and algorithmic approaches to problem solving, and provide introductory programming skills.

Digital customer channels are maturing in providing personalized customer-facing functions, such as advertising, sales, ordering, and product use. Social engagement is now a predominant way for influencing customers and creating buzz. Cognitive techniques are providing a more human touch, even when dealing with software agents. The success of new customer engagement is heavily due to advanced analytics capabilities to drive personalized communication to each customer or micro segment. There is a tremendous need in organizations to establish programs for analytics-driven customer engagement. In this course, students learn elements of analytics and personalized engagement. Students also work on a project using real-life business problem and data to get hands-on understanding.

This course offers an in-depth hands-on exploration of various cutting-edge information technologies used for big data analytics. The first half of the course focuses on using Hadoop – Sqoop, Pig, and Hive – for ETL (extract-transform-load) operations. The second half of the course focuses on understanding the MapReduce algorithm and using Apache Mahout for data mining algorithms, including classification, clustering, and collaborative filtering. Towards the end of the course, students are introduced to Apache Spark. Students become familiar with the Unix operating system, as well as with programming in Hadoop and Scala. This course is extremely hands-on as students spend significant time working with data. Students are expected to have taken at least one course in data modeling and one course in data mining (see prerequisites) or have significant related work experience.

Edge is the Merage School's integrative MBA course that explores the crucial roles of global, market, and technology forces in the business landscape as disruptive and transformative catalysts for change – opening new markets, erasing boundaries, and reinventing industries. In a world that is being redefined by these catalysts – an evolving geopolitical order, a demand for sustainability, shifting demographics, changing consumer preferences, digital technologies, ongoing economic uncertainty, and relentless competitive pressures – the imperative for companies to pursue business reinvention and new leadership strategies is unprecedented. This course prepares future business leaders to proactively identify the opportunities and challenges presented by these catalysts, provide valuable frameworks to critically evaluate their implications, and develop the key strategies, insights and approaches needed to innovate, compete and win at the Edge.

The course covers relational and non-relational databases, cloud data management solutions, as well as the underlying storage and security properties of these systems. Students broadly study big-data management frameworks such as Hadoop and Spark and also participate in a hands-on project to conduct data analytics, based on real data, drawn from public sources such as the web or social networks.

This course examines impacts of technology on marketing, emphasizing strategic and tactical implications for marketing managers. Topics cover technology and consumers, marketing strategy, branding, advertising, omnichannel, search engine marketing, email and mobile marketing, conversion optimization, web analytics, and more.

Since its commercial inception in the early 1990s, the internet has dramatically transformed marketing practice, resulting in spectacular successes as well as dismal failures within the ever-evolving space. Gaining the strategic know-how necessary for a chance at success in such a highly competitive and challenging digital environment requires knowledge of the historical, technological, social and commercial developments that have helped to shape the internet of today. Marketing on the Internet begins by laying the foundation for the type of insight necessary to intelligently and strategically engage the environment while avoiding its many pitfalls. The course covers online marketing concepts from pre-launch strategy to post-launch analytics (and numerous topics in between), referencing real-world examples to illustrate key points while encouraging participation, discussion and a shared learning experience. Marketing on the Internet merges theoretical concepts with strategic and practical solutions. Through a combination of readings, industry articles, case studies and online references (including blog posts, tweets and videos), students are exposed to the latest developments in internet marketing. Students work in groups, applying their understanding of key topics to develop their own internet marketing strategy and plan for a new (or improvement upon an existing) product, service or campaign. The course culminates with each group handing in a 10-page paper and presenting their internet marketing strategy and plan in a 7-10-minute video produced by each group.

[New AY19-20 course. Awaiting syllabus and course description.]

Micromarketing with Digital Footprints focuses on how businesses use digital footprints from household data and point-of-purchase data to customize product offerings and delivery, store locations, advertising, and promotions to households and neighborhoods with the highest market potential.

In the first part of the course, students receive extensive, hands-on experience using data on household digital footprints, as well as mapping software called Geographic Information System (GIS) software to visualize the data such as household and then consumer segments based on the digital footprints from Experian, Alteryx software to access and analyze the data, mapping software from ESRI called ArcMap, and an address geocoder from TomTom. The second part of the course teaches students to use point-of-sale digital footprint products from retail stores. By using data and software are provided by AC Nielsen, in their business intelligence product called Answers Retail Edition, students learn unit and dollar sales for all stocking units (SKU's) at several retail locations, including sales by competitors. In addition to data reported monthly, which includes sales by brand, manufacturer, product category, price, and price promotion.

The accelerating development of digital technologies has been a dominant force shaping business opportunities (and constraints) since the late 20th century. The exact implications of this trend vary across businesses, markets, and industries and also over time, calling for precise and specific strategic analysis for each situation. Today, firms are still adjusting to the consequences of past digital development, but even as these existing waves of change work their way through the economy, continuing digitalization is creating another, newer wave of developments: blockchain transaction control, true digital manufacturing with tiny modular components, cheap robotics and automation, and the application of artificial neural networks to large data sets, among other emerging capabilities. Strategy for a Digital Age focuses on applying the unchanging basic principles of strategy, economics, and organization – the “eternals” – to these new developments. Through the study of both historical and contemporary competitive situations, using strategic and economic principles and models, we work out key underlying digital mechanisms – the “themes” – that shape competition over time, not just for firms producing hardware, software, and information services but also for firms whose businesses are being transformed by the use of digitalization. And we learn to apply these eternals and themes to contemporary business situations spanning a range of current digital topics.

In a world increasingly defined by the exponential growth in computing power, students learn how to design, build, and operate a competitive intelligence program. A competitive intelligence program assesses a competitor’s strengths and weaknesses across products, advertising, and brand platforms upon which digitally-driven strategies and execution tactics are developed, assessed, and modified.

Through a combination of case studies, guest speakers and live working examples of how companies are increasingly using this process, students learn to utilize tools and practical examples to define, gather, analyze, and distribute actionable intelligence about products, customers, competitors, and any aspect of the environment needed to support executives and managers in strategic and tactical decision making for an organization. With these tools, students will be able to assess a company’s external environment, including the industry and relevant competitors, using traditional as well as the technically advanced AI/ML tools to discern elements key to establishing trends and appropriate responses for companies in a variety of industries. The class draws on the students’ experiences as well as information from strategy and related digitally driven disciplines. The class concludes with the schools globally renowned “War Game.”

Media and entertainment companies are experiencing massive disruption, driven by technological advancements and the impact of digital, changes in consumer behavior and growing international markets. This interdisciplinary course provides an understanding of the overall ecosystem, the path to monetization, and the impact of digital disruption. Media content includes music, movies, TV, streaming, sports, gaming, and virtual reality. Music includes new trends in distribution and monetization. Publishing explores the impacts on digital and how companies are reacting. Film covers trends in development, distribution, and marketing, driven by digitization and big data. TV includes changes in broadcast, cable, and premium TV, driven by new content, channels, and devices. Internet covers new multi-channel networks, user-generated content, and globalization. Sports addresses how leagues are reacting to the impacts of digital. Gaming includes the impact of mobile and cloud-based services on traditional models. Virtual reality covers how Hollywood is experimenting with virtual reality content. Articles and notes provide background and explore emerging trends. Case studies and class discussions bring to life elements of all these areas, and prepare students to understand, analyze, and present recommendations.

As computing and communication have grown in power and pervasiveness, leading up to today’s Internet-connected world, digital intermediation of multi-sided transactions has become the key context for strategic decisions about how to compete and cooperate in order to flourish. The famous stars of today – Google, Facebook, Apple, Amazon – and their many would-be imitators and successors, as well as the companies adapting in their shadow, all must cope with the realities of a world in which controlling interfaces and interactions may be more valuable than any specific product or service that gets transacted. Or perhaps a new age of decentralized, common interaction protocols will replace today’s proprietary interaction hubs.

This course provides a rigorous, economics-based and real-world-grounded development of the theory of network effects and standards competition, platform economics and tactics, ecosystems management, and regulatory impacts. Students learn models and tools for analyzing and synthesizing strategies in these networks, platform, and ecosystem contexts, including relevant parts of economics, game theory, network theory, and political science. They apply these tools to contemporary and historical situations via class discussions, problems, and write-ups.