Curriculum & Courses

Applied analytics is about the strategic use of data and analytics to inform decisions within an operating environment. The use of analytics is rapidly becoming ubiquitous across all organizational functions. This course helps students understand how data and analytics are used across different functions to inform decisions that impact the organization. As such, it is the introductory course to the professional practice of applied analytics and the first course in the leadership sequence.

The course focuses on data and analytics within operational functions of different kinds of organizations across a range of industry sectors, and the overall ecosystem within which they operate. Students will also learn about the broader context—economic, technological, social, and demographic, and how these trends are influencing the use of analytics. Students learn how data and analytics are used to understand how an organization is currently performing, and how data and analytics can be used to inform future actions to optimize the performance of an organization. The goal is to introduce students to the professional practice of applied analytics, focusing on how analytics can inform a wide range of operational decisions within an organization.

The world is generating data at an ever faster pace, including through business transactions, online searches, social media activities, and a variety of sensors. The ready availability of this unprecedented amount of data creates opportunities to predict outcomes and explain phenomena across a wide range of domains from medicine to business to even space exploration. Supervised learning techniques are being extensively used to make useful predictions and generate insights to tackle problems. These predictive analysis techniques are the focus of this course.

As the starting point of the two-part Applied Machine Learning sequence, the course guides students through the data-wrangling process, starting with data exploration and other foundational approaches. The course then covers an array of supervised learning techniques including linear regression, decision trees, and support vector machines. Students also have the opportunity to challenge themselves in applying and combining the techniques they have learned through a predictive analytics competition.

Building upon the tools and foundational concepts from Frameworks and Methods I, this course introduces analytic techniques to handle less traditional forms of data, as well as more specialized analytic techniques to help organizations dig more deeply and comprehensively to create value from their data.

This course covers unsupervised learning techniques, including clustering, to examine unlabeled data and also covers natural language processing procedures, such as tokenization, to analyze text data. The course further introduces neural networks and other specialized analytics frameworks. Students learn to integrate the techniques that they learned over Parts I and II of the Applied Machine Learning sequence and have the opportunity to apply these tools to real-world problems across topics or industries based on their areas of interest.

Great managers of analytic projects are more than mere data users; they are key decision makers and strategic owners in the underlying data processes. This course provides students with foundational context for managing data so that it can be leveraged and used with confidence.

Analytic teams work closely with technology partners in managing data. Languages and techniques unique to each team can impede cooperation. To bridge this gap, this course provides a broad overview of data technology concepts including database engines and associated technologies.

Sound policies and procedures are also essentials to ensure high quality of data throughout the analytics lifecycle. But the challenges of putting these measures into practice are significant. There are often legacy repositories and business functions to unravel, as well as social and political barriers to overcome. Data ownership and accountability are hard to implement. Operational disruption and conflicting stakeholder requirements pose additional barriers.

This course will expose students to foundational data principles, governance processes and organizational prerequisites needed to overcome challenges to ensure data quality.

Data does not have meaning without context and interpretation. Being able to effectively present data analytics in a compelling narrative to a particular audience will differentiate you from others in your field. This course takes students through the lifecycle of an analytical project from a communication perspective. Students develop written, verbal, and visual deliverables for three major audiences: data experts (e.g., head of analytics); consumer and presentation experts (e.g., chief marketing officer); and executive leadership (e.g., chief executive officer).

Students get ample practice in strategic interactions in relevant social and professional contexts (e.g., business meetings, team projects, and one-on-one interactions); active listening; strategic storytelling; and creating persuasive professional spoken and written messages, reports, and presentations. Throughout the course, students create and receive feedback on data storytelling while sharpening their ability to communicate complex analytics to technical and nontechnical audiences with clarity, precision, and influence.

This course focuses on the step after insights have been generated from data, and asks the question: what needs to change in an organization's strategy to benefit from those insights? It is the second in the sequence of analytics leadership core courses.

Students will learn how to evaluate the strategic environment, the strategic models that might be useful for their organization, and the implementation of a strategy. The course will also ask students to learn theory and research findings and then apply what they have learned to real situations. This will include an exercise in strategic business “wargaming.”

Having developed an understanding of organizational strategy, special emphasis is then placed on the interplay between analytics and strategic considerations in an organization. The course teaches students about the practical application of analytics to strategic thinking on two levels: that of the organization (how are analytics used to drive the organization’s strategy?) and the analytics team (how is the organization’s strategy driving the activity of the analytics team?).

Availability: Summer and Fall terms only

These courses serve as the capstone for the MSAA degree. The capstone requires a synthesis of program content applied to industry challenges, aligning leadership, strategic management, communication, and analytics coursework with analytics projects. The courses will allow students to apply the skills they have learned to solve analytical and organizational challenges. Information about each option will be sent to students closer to the start of the available terms; students may enroll in only one of the following:

Solving Real World Problems with Analytics - APAN PS5900 - Online

Data Science Consulting - APAN PS5902 - In-Person 

In recent years, data analytics and artificial intelligence (AI) have become essential to business intelligence and informed decision making. But to realize the impact of analytics and AI, effective visual communication of data insights via user interfaces (UI), such as web pages and app dashboards, is equally critical. Building effective UIs requires mastering the user experience (UX) design principles and certain front-end development technologies. Furthermore, the recent rise of multimodal Generative AI offers unprecedented opportunities for simplifying, automating, and scaling UX/UI development. 

This course provides a comprehensive understanding of UX design principles and best practices for developing UIs while emphasizing ethical considerations and inclusivity. Students will learn to create intuitive and visually engaging websites and dashboards that leverage AI-generated insights, also considering data privacy, diversity, and accessibility. Key topics include the design, implementation, and evaluation of UIs, with hands-on experience in web development technologies like HTML, CSS, and JavaScript, as well as related cloud services. Students will apply state-of-the-art AI technologies to create intelligent and interactive UIs, all while critically assessing data sources and AI models for potential biases.

Prerequisites
APAN PS5200 Applied Analytics Frameworks and Methods I, APAN PS5800 Storytelling with Data

Corequisites
APAN PS5400 Managing Data

With the growth of the Internet in recent decades, there has been an exponential increase in unstructured textual data available from news and social media. This data is invaluable for extracting actionable insights that enhance the scale and the quality of business analytics. The enormous volume of domain text corpora makes the extraction of meaningful information possible only through the use of advanced natural language processing (NLP) and machine learning techniques. Also, jobs in the data analysis field increasingly require the use of extracting and analyzing information from diverse sources, structured as well as unstructured. This course will train students in a technology that is seen as an essential part of a data analyst's toolkit.

The course will focus on advanced methods and systems that enable named entity recognition and disambiguation, topic modeling, sentiment analysis, word vector embeddings, abstractive summarization, meaning extraction, and deep learning for NLP. Weekly course lectures will offer a blend of theoretical material and hands-on class exercises, which will be put into practice through weekly assignments. Students who complete the course will be able to practice the gained knowledge as applied NLP data scientists in various business domains, including sales and marketing, financial modeling, credit risk analysis, legal trust and compliance, intellectual property and contracts management.

Data analytics has become an essential component of business intelligence and informed decision making. Sophisticated statistical and algorithmic methodologies, generally known as data science, are now of predominant interest and focus. Yet, the underlying cloud computing platform is fundamental to the enablement of data management and analytics.

This course introduces students to cloud computing concepts and practices ranging from infrastructure and administration to services and applications. The course is primarily focused on the development of practical skills in utilizing cloud services to build distributed and scalable analytics applications. Students will have hands-on exposure to VMs (Virtual Machines), databases, storage, microservices, and AI/ML (Artificial Intelligence and Machine Learning) services through Google Cloud Platform, etc. Cost and performance characteristics of alternative approaches will also be studied. Topics include: overview of cloud computing, cloud systems, parallel processing in the cloud, distributed storage systems, virtualization, security in the cloud, and multicore operating systems. Throughout, students will study state-of-the-art solutions for cloud computing developed by Google, Amazon, Microsoft, and IBM.

The course modules provide a blend of lecture and reading materials along with class exercises and programming assignments. While extensive programming experience is not required, students taking the course are expected to possess basic Python 3 programming skills.

The desired outcome of the course is the student’s ability to put conceptual knowledge to practical use. Whether you are taking this course for future academic research, for work in industry, or for an innovative startup idea, this course should help you master the fundamentals of cloud computing.

Prerequisites

Required
APAN 5200 Applied Analytics Frameworks and Methods I
APAN 5100 Applied Analytics in the Organizational Context
APAN 5800 Storytelling with Data

Recommended
APAN 5400 Managing Data

This course offers a comprehensive introduction to a branch of machine learning called generative modeling, focusing on the underlying concepts, theoretical techniques, and practical applications. The defining property of Generative AI models is their ability to generate new data similar to a given dataset. In recent years, Generative AI has seen rapid advancement, revolutionizing various industries by enabling machines to create realistic and novel content, ranging from images, videos, and music to text and complex simulations.

Students will learn to use, fine-tune, and programmatically interface with high-level APIs and open-source foundational models, allowing them to leverage state-of-the-art tools in Generative AI. Additionally, the course delves into the theory and practice of low-level implementations, empowering students to train their own models on their own data and understand these models from first principles. The course covers various types of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers with their applications to text, image, audio, and video generation.

By combining these approaches, this course provides a robust foundation in both the practical application and deep theoretical knowledge required to develop innovative AI solutions.

Over the past decade, the Internet of Things (IoT) has transformed industries by enabling real-time data collection, analysis, and automated control through interconnected devices. Advances in networking, cloud computing, and robotics have expedited IoT adoption, impacting a wide range of fields from home safety and industrial automation to healthcare and autonomous driving. Additionally, the rise of artificial intelligence (AI) led to the emergence of AIoT (Artificial Intelligence of Things), which combines IoT connectivity with AI-driven decision-making to enhance smart systems.

This course provides a comprehensive understanding of IoT technologies and their integration with AI and robotic systems. Students will explore IoT architecture, key components, and communication protocols, while gaining hands-on experience with IoT platforms, sensors, and data acquisition devices. The curriculum emphasizes practical AIoT applications for real-time decision-making in manufacturing, public safety, smart cities, healthcare, etc., and addresses the ethical considerations of these technologies.

Combining conceptual learning with practical assignments, the course features weekly lectures and readings on IoT fundamentals and applications, with biweekly quizzes to assess conceptual understanding. Students will further apply their learning through individual assignments and a group term project, ensuring a robust foundation in IoT analytics and AI-powered robotic automation.

Blockchains have created a new paradigm in secure yet decentralized information management among various entities without requiring trusted intermediaries. Applications to various fields abound including cryptocurrencies (e.g., Bitcoin, Ethereum), banking (Ripple), insurance, and logistics.

This is an introductory course on blockchains and crypt-currencies. The course introduces the concepts of blockchains using Bitcoin as the main example. It then goes into the details related to underlying fundamentals including cryptographic protocols, hash, digital signatures, chaining of blocks of transactions, decentralization using mining based on proof of work, and smart contracts. The course also covers data mining of transactions using machine learning and social network methods. It helps students understand blockchain and its applications as a key peer-to-peer technology and its uses in smart contracts.

This course equips students with essential mathematical foundations for understanding and working with artificial intelligence (AI) algorithms. After a brief introduction to the historical and social context that numbers arise in, students will learn about:

• Linear Algebra: Matrices, matrix-vector multiplication, linear models, change of basis, dimensionality, spectral decomposition, and principal component analysis (PCA).
• Calculus: Rates of change, derivatives, optimization techniques like gradient descent, with a brief touch upon linear approximation.
• Probability and Statistics: Mathematically deriving complex probability distributions out of simpler ones, mathematically deriving statistical testing methods
• Graph Theory: How graphs are used to find relationships among data as well as being a setting for AI-driven problem solving.
• Problem Solving and Algorithms: Applying mathematical concepts to find problem solutions. Students will learn about search methods like uninformed search, informed search with the A* algorithm, and greedy algorithms.
• Computational Theory and Automata: Answering questions about what is computable, what is needed in order to compute something, and using this framework to state how much “information” is contained in a mathematical object.

By the end of this course, students will possess a strong mathematical toolkit to confidently tackle the complexities of modern AI algorithms.

Operations Management (OM) is responsible for the efficient production and delivery of goods and services, serving as a cornerstone of successful organizations. This course emphasizes how analytical techniques, such as forecasting, queuing theory, and linear programming, provide critical tools for optimizing operational decision-making, improving efficiency, and addressing real-world challenges in operations management. In this course, you will gain essential skills to optimize processes, manage resources, and enhance productivity across various industries. The course will be delivered through a combination of interactive lectures, case studies, and hands-on coding exercises to ensure a balance between conceptual learning and practical application.

Through lectures, you will gain a solid foundation in OM principles and analytical techniques. Case studies will help illustrate real-world applications of OM in industries such as manufacturing, healthcare, retail, and logistics, allowing you to see how the concepts are applied in diverse contexts. This course will integrate the principles of OM with hands-on analytical techniques using Python, allowing you to model and solve real-world OM problems. You will learn to run simulations, perform optimizations, and analyze data to make data-driven decisions that enhance efficiency and overall performance.

OM practices are tailored to meet the specific needs of various sectors. In manufacturing, OM helps streamline production lines and minimize waste; in healthcare, it enhances patient flow and optimizes resource allocation; in retail, it improves inventory management and supply chain operations; and in logistics, it ensures timely deliveries while reducing transportation costs. This course will equip you with the skills to apply OM practices effectively in different industries.

In today’s hyper-connected world, cybersecurity has become a critical priority for organizations, governments, and individuals alike. With the ever-growing volume and sophistication of cyber threats, data analytics plays a pivotal role in identifying, managing, and mitigating security risks. This course offers a comprehensive introduction to core cybersecurity principles—encompassing network security, threat detection, and vulnerability management—while highlighting how emerging techniques in AI and machine learning can transform modern defense strategies. Students will explore key frameworks, advanced tools, and real-world case studies, gaining insight into how analytics underpins effective threat intelligence, incident response, and regulatory compliance.

Through hands-on exercises, interactive assignments, and a culminating project focused on automating vulnerability analysis, learners will develop practical skills in data-driven security. They will practice essential tasks—such as parsing logs, classifying vulnerabilities, creating visual dashboards, and applying AI-driven anomaly detection—while also examining ethical, legal, and regulatory considerations in cybersecurity. By the end of the course, students will be equipped to design proactive defense measures, critically assess diverse threat landscapes, and responsibly leverage AI and analytics to fortify digital ecosystems against evolving cyber risks.

Data analytics has become an essential component of business intelligence and informed decision making. Sophisticated statistical and algorithmic methodologies, generally known as data science, are now of predominant interest and focus. Yet, the underlying cloud computing platform is fundamental to the enablement of data management and analytics.

This course introduces students to cloud computing concepts and practices ranging from infrastructure and administration to services and applications. The course is primarily focused on the development of practical skills in utilizing cloud services to build distributed and scalable analytics applications. Students will have hands-on exposure to VMs (Virtual Machines), databases, storage, microservices, and AI/ML (Artificial Intelligence and Machine Learning) services through Google Cloud Platform, etc. Cost and performance characteristics of alternative approaches will also be studied. Topics include: overview of cloud computing, cloud systems, parallel processing in the cloud, distributed storage systems, virtualization, security in the cloud, and multicore operating systems. Throughout, students will study state-of-the-art solutions for cloud computing developed by Google, Amazon, Microsoft, and IBM.

The course modules provide a blend of lecture and reading materials along with class exercises and programming assignments. While extensive programming experience is not required, students taking the course are expected to possess basic Python 3 programming skills.

The desired outcome of the course is the student’s ability to put conceptual knowledge to practical use. Whether you are taking this course for future academic research, for work in industry, or for an innovative startup idea, this course should help you master the fundamentals of cloud computing.

Prerequisites

Required
APAN 5200 Applied Analytics Frameworks and Methods I
APAN 5100 Applied Analytics in the Organizational Context
APAN 5800 Storytelling with Data

Recommended
APAN 5400 Managing Data

From strategic considerations to technical implementations, Applied Analytics is best practiced through the development of innovative solutions to organizational challenges. Your ability to think critically about these issues, assess the capabilities of the available information, and creatively generate new approaches to solving problems can make a large impact. Relative to a traditional job within a single company, a consultant may simultaneously pursue multiple projects across industries and domains.

The course provides an immersive experience akin to working as a data scientist in a consultative setting. The course will create a range of diverse experiences across multiple industries with projects of increasing complexity and intertwining ties. As such, the course is designed to convey many of the practical lessons of data science and consulting that are not traditionally conveyed in an academic curriculum. The content of the class will emphasize rapidly learning new analytical paradigms, ranging from technical skills and data structures to business priorities. In addition to solving problems, you will also reevaluate how to structure your own efforts in a way that can improve your productivity and the reliability of your work. The course will also include advanced training in R programming that incorporates the best practices in software design.

Data Science Consulting will apply the skills that you have learned to solve numerous analytical and organizational challenges, integrating the concepts from the technical and managerial cores to help you develop the skills to be successful consultants. In preparation for a consulting role, each student will take on both individual and team-based projects.

Financial securities analysis and portfolio management is the study of analyzing information to evaluate financial securities and design investment strategies. Studying the subject can provide a foundation for students entering the fields of investment analysis or portfolio management. This course provides an intensive introduction to major topics in investments. Part I of the course lays the theoretical foundation by introducing the Portfolio Theory and Equilibrium Asset Pricing models. Part II covers the valuation models and analysis of major asset classes: equity, fixed-income, and derivatives. Topics include bond valuation and interest rate models, equity valuation and financial statement analysis, options valuation, other derivatives, and risk management. Part III of the course focuses on the practice of active portfolio management.

Organizations need quantitative analysis to solve complex problems and make consequential choices. Research design provides both a coherent framework for collecting relevant evidence and strategies for evaluating that evidence. Knowledge of research design enables organizations to make adaptive and effective use of quantitative analysis in solving problems and making choices. This course serves as a foundational course in the Applied Analytics program.

In this course, you will approach problems as methodological thinkers: you will assess whether the organization is asking the right questions, choosing a relevant design, gathering appropriate and meaningful evidence, and using the appropriate statistical analysis to answer those questions.

To varying degrees and in different organizational contexts, we will work to answer some of the following key questions:

• What are the key questions that strategic decision makers need to formulate and answer in order to inform their decisions?
• What data are available (and unavailable) that might be used to inform the important strategic decisions?
• What research questions are implied by the needs of strategic decision makers?
• What data do we need to measure those variables? Are we currently collecting that data? Why or why not?
• Which analytical methods might be helpful in answering the research question?
• What might challenge the validity of results and how can research results be communicated in a way that mitigates the risks associated with these challenges?
• What are the business factors that influence decisions about how research is undertaken?

The Internship in Industry course offers students the preparation to excel in the marketplace with hands-on experience within an organization. The ideal internship will provide students an opportunity to gain tangible and practical knowledge in their chosen field by taking on a position that is closely aligned with their coursework and professional interests.

This course is structured around the internship experience. In the first assignment, students will author learning objectives to complete in their internship and review these learning objectives with their site supervisor. Students should also expect that after completing this course they will be able to:

• Discuss the application of program content and theory in a professional context (LO1)
• Define a plan for assessing and building their professional competencies (LO2)
• Describe an organization’s culture and assess their cultural “fit” (LO3)
• Make recommendations for the types of behaviors, structure, and culture they would want to see in a future workplace setting (LO4)

Before registering for this course, students must secure an appropriate graduate-level internship, complete the Internship Application Form and receive approval from the academic program. It is highly recommended that domestic students complete at least 12 points (credits) prior to completing an internship. International students must have completed at least two terms before completing an internship and apply for & receive CPT approval through the ISSO Office unless they completed their undergraduate degree in the U.S. and enrolled in graduate school immediately after obtaining their undergraduate degree.

To receive approval, the internship must:

• Provide an appropriate opportunity for students to apply course concepts
• Fit into the planned future program-related career path of the student
• Provide a minimum of 210 hours over the semester
• Internship dates must coincide with the start and end of the term you are enrolling in the course. You may not complete this course for a previous internship or for an internship you plan to take in the future. The internship and course must be done at the same time. 

In recent years, data analytics and artificial intelligence (AI) have become essential to business intelligence and informed decision making. But to realize the impact of analytics and AI, effective visual communication of data insights via user interfaces (UI), such as web pages and app dashboards, is equally critical. Building effective UIs requires mastering the user experience (UX) design principles and certain front-end development technologies. Furthermore, the recent rise of multimodal Generative AI offers unprecedented opportunities for simplifying, automating, and scaling UX/UI development. 

This course provides a comprehensive understanding of UX design principles and best practices for developing UIs while emphasizing ethical considerations and inclusivity. Students will learn to create intuitive and visually engaging websites and dashboards that leverage AI-generated insights, also considering data privacy, diversity, and accessibility. Key topics include the design, implementation, and evaluation of UIs, with hands-on experience in web development technologies like HTML, CSS, and JavaScript, as well as related cloud services. Students will apply state-of-the-art AI technologies to create intelligent and interactive UIs, all while critically assessing data sources and AI models for potential biases.

Prerequisites
APAN PS5200 Applied Analytics Frameworks and Methods I, APAN PS5800 Storytelling with Data

Corequisites
APAN PS5400 Managing Data

The successful implementation of analytics depends not only on developing good insights and good strategy, but is also an exercise in managing the necessary changes. The inspiring stories about the importance of analytics today are about how what was learned through analytics was actually implemented to enable an organization to improve its operations, effectiveness, or return on investment.

This course--the third in the sequence of analytics leadership core courses—is about changing the behavior and the culture of organizations, with particular emphasis on how to successfully introduce the methods and results of analytics. Students explore the motivations, obstacles and interventions of change, and learn to build alliances, facilitate difficult meetings and develop a transformation plan. The course focuses on practical skills as they are being developed at organizations with pioneering analytics capabilities today.

Students will review some of the most important academic research and business publications on change management and the implementation of analytics. However, the course is also intended to enhance practical skills, so students will engage in some real-world practice and role-playing with classmates. As they master each module, students will incrementally develop a plan to introduce analytics into the organization where you currently work, or have worked, or hope to work.

Operations Management (OM) is responsible for the efficient production and delivery of goods and services, serving as a cornerstone of successful organizations. This course emphasizes how analytical techniques, such as forecasting, queuing theory, and linear programming, provide critical tools for optimizing operational decision-making, improving efficiency, and addressing real-world challenges in operations management. In this course, you will gain essential skills to optimize processes, manage resources, and enhance productivity across various industries. The course will be delivered through a combination of interactive lectures, case studies, and hands-on coding exercises to ensure a balance between conceptual learning and practical application.

Through lectures, you will gain a solid foundation in OM principles and analytical techniques. Case studies will help illustrate real-world applications of OM in industries such as manufacturing, healthcare, retail, and logistics, allowing you to see how the concepts are applied in diverse contexts. This course will integrate the principles of OM with hands-on analytical techniques using Python, allowing you to model and solve real-world OM problems. You will learn to run simulations, perform optimizations, and analyze data to make data-driven decisions that enhance efficiency and overall performance.

OM practices are tailored to meet the specific needs of various sectors. In manufacturing, OM helps streamline production lines and minimize waste; in healthcare, it enhances patient flow and optimizes resource allocation; in retail, it improves inventory management and supply chain operations; and in logistics, it ensures timely deliveries while reducing transportation costs. This course will equip you with the skills to apply OM practices effectively in different industries.

In today’s hyper-connected world, cybersecurity has become a critical priority for organizations, governments, and individuals alike. With the ever-growing volume and sophistication of cyber threats, data analytics plays a pivotal role in identifying, managing, and mitigating security risks. This course offers a comprehensive introduction to core cybersecurity principles—encompassing network security, threat detection, and vulnerability management—while highlighting how emerging techniques in AI and machine learning can transform modern defense strategies. Students will explore key frameworks, advanced tools, and real-world case studies, gaining insight into how analytics underpins effective threat intelligence, incident response, and regulatory compliance.

Through hands-on exercises, interactive assignments, and a culminating project focused on automating vulnerability analysis, learners will develop practical skills in data-driven security. They will practice essential tasks—such as parsing logs, classifying vulnerabilities, creating visual dashboards, and applying AI-driven anomaly detection—while also examining ethical, legal, and regulatory considerations in cybersecurity. By the end of the course, students will be equipped to design proactive defense measures, critically assess diverse threat landscapes, and responsibly leverage AI and analytics to fortify digital ecosystems against evolving cyber risks.

Anomaly detection helps in the early detection of critical outliers in a system. Based on the context, these outliers can be detrimental and result in loss of resources, and time through errors, fraud, manipulation of stocks, and other such malicious activities. Outliers can also be beneficial for example in investing, and arbitrage. Business decisions that leverage anomaly detection, which used to require intense human resource and capacity can now be completed in a short time through versatile models and automation.

In this course, students will learn how to find these unusual occurrences in the data. Students will be provided hands-on experience in multiple contexts with complex datasets that they must further manipulate through industry-specific data engineering. This course will enable students to build advanced supervised and unsupervised machine learning models to find these anomalies. Data engineering in this course will challenge students to engage in techniques of data manipulations with datasets that are NOT perfect.

This course offers a comprehensive introduction to a branch of machine learning called generative modeling, focusing on the underlying concepts, theoretical techniques, and practical applications. The defining property of Generative AI models is their ability to generate new data similar to a given dataset. In recent years, Generative AI has seen rapid advancement, revolutionizing various industries by enabling machines to create realistic and novel content, ranging from images, videos, and music to text and complex simulations.

Students will learn to use, fine-tune, and programmatically interface with high-level APIs and open-source foundational models, allowing them to leverage state-of-the-art tools in Generative AI. Additionally, the course delves into the theory and practice of low-level implementations, empowering students to train their own models on their own data and understand these models from first principles. The course covers various types of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers with their applications to text, image, audio, and video generation.

By combining these approaches, this course provides a robust foundation in both the practical application and deep theoretical knowledge required to develop innovative AI solutions.

With the growth of the Internet in recent decades, there has been an exponential increase in unstructured textual data available from news and social media. This data is invaluable for extracting actionable insights that enhance the scale and the quality of business analytics. The enormous volume of domain text corpora makes the extraction of meaningful information possible only through the use of advanced natural language processing (NLP) and machine learning techniques. Also, jobs in the data analysis field increasingly require the use of extracting and analyzing information from diverse sources, structured as well as unstructured. This course will train students in a technology that is seen as an essential part of a data analyst's toolkit.

The course will focus on advanced methods and systems that enable named entity recognition and disambiguation, topic modeling, sentiment analysis, word vector embeddings, abstractive summarization, meaning extraction, and deep learning for NLP. Weekly course lectures will offer a blend of theoretical material and hands-on class exercises, which will be put into practice through weekly assignments. Students who complete the course will be able to practice the gained knowledge as applied NLP data scientists in various business domains, including sales and marketing, financial modeling, credit risk analysis, legal trust and compliance, intellectual property and contracts management.

Over the past decade, the Internet of Things (IoT) has transformed industries by enabling real-time data collection, analysis, and automated control through interconnected devices. Advances in networking, cloud computing, and robotics have expedited IoT adoption, impacting a wide range of fields from home safety and industrial automation to healthcare and autonomous driving. Additionally, the rise of artificial intelligence (AI) led to the emergence of AIoT (Artificial Intelligence of Things), which combines IoT connectivity with AI-driven decision-making to enhance smart systems.

This course provides a comprehensive understanding of IoT technologies and their integration with AI and robotic systems. Students will explore IoT architecture, key components, and communication protocols, while gaining hands-on experience with IoT platforms, sensors, and data acquisition devices. The curriculum emphasizes practical AIoT applications for real-time decision-making in manufacturing, public safety, smart cities, healthcare, etc., and addresses the ethical considerations of these technologies.

Combining conceptual learning with practical assignments, the course features weekly lectures and readings on IoT fundamentals and applications, with biweekly quizzes to assess conceptual understanding. Students will further apply their learning through individual assignments and a group term project, ensuring a robust foundation in IoT analytics and AI-powered robotic automation.

Data analytics has become an essential component of business intelligence and informed decision making. Sophisticated statistical and algorithmic methodologies, generally known as data science, are now of predominant interest and focus. Yet, the underlying cloud computing platform is fundamental to the enablement of data management and analytics.

This course introduces students to cloud computing concepts and practices ranging from infrastructure and administration to services and applications. The course is primarily focused on the development of practical skills in utilizing cloud services to build distributed and scalable analytics applications. Students will have hands-on exposure to VMs (Virtual Machines), databases, storage, microservices, and AI/ML (Artificial Intelligence and Machine Learning) services through Google Cloud Platform, etc. Cost and performance characteristics of alternative approaches will also be studied. Topics include: overview of cloud computing, cloud systems, parallel processing in the cloud, distributed storage systems, virtualization, security in the cloud, and multicore operating systems. Throughout, students will study state-of-the-art solutions for cloud computing developed by Google, Amazon, Microsoft, and IBM.

The course modules provide a blend of lecture and reading materials along with class exercises and programming assignments. While extensive programming experience is not required, students taking the course are expected to possess basic Python 3 programming skills.

The desired outcome of the course is the student’s ability to put conceptual knowledge to practical use. Whether you are taking this course for future academic research, for work in industry, or for an innovative startup idea, this course should help you master the fundamentals of cloud computing.

Prerequisites

Required
APAN 5200 Applied Analytics Frameworks and Methods I
APAN 5100 Applied Analytics in the Organizational Context
APAN 5800 Storytelling with Data

Recommended
APAN 5400 Managing Data

The role of databases in data analytics cannot be overstated; databases facilitate efficient, secure and accurate information storage and retrieval across multiple users and platforms. As a result, proficiency in database design and knowledge of SQL programming are essential skills for the modern analyst and data scientist. This course is designed to help students develop these skills.

Data hardly ever comes ready to be analyzed. In fact, in many analytics projects, –the preparation of data (be it collecting, loading, organizing, filtering, etc.) can take more than 80% of the team’s time and resources, often forcing them to rush through the analyses in order to produce results. This course will demonstrate how relational database design coupled with efficient programming can alleviate the burden of handling messy data, allowing analysts and data scientists to focus on delivering accurate, reliable and reproducible results.

While the Structured Query Language (SQL) has not changed much in the past decade, database systems and the tools that interact with them have continued to evolve. Students will be introduced to the latest programs and database connectors that allow for tight integration with Python and R as well as interactive visualization in Power BI and Tableau.

Additionally, students will be exposed to NoSQL database systems optimized for big data analytics and the techniques necessary for interacting with massive amounts of data.

This course features a final project in which students will leverage their newfound skills to tackle real-life data management scenarios by designing appropriate database schemas and demonstrating how raw data can be transformed into actionable insights.

Financial securities analysis and portfolio management is the study of analyzing information to evaluate financial securities and design investment strategies. Studying the subject can provide a foundation for students entering the fields of investment analysis or portfolio management. This course provides an intensive introduction to major topics in investments. Part I of the course lays the theoretical foundation by introducing the Portfolio Theory and Equilibrium Asset Pricing models. Part II covers the valuation models and analysis of major asset classes: equity, fixed-income, and derivatives. Topics include bond valuation and interest rate models, equity valuation and financial statement analysis, options valuation, other derivatives, and risk management. Part III of the course focuses on the practice of active portfolio management.

Blockchains have created a new paradigm in secure yet decentralized information management among various entities without requiring trusted intermediaries. Applications to various fields abound including cryptocurrencies (e.g., Bitcoin, Ethereum), banking (Ripple), insurance, and logistics.

This is an introductory course on blockchains and crypt-currencies. The course introduces the concepts of blockchains using Bitcoin as the main example. It then goes into the details related to underlying fundamentals including cryptographic protocols, hash, digital signatures, chaining of blocks of transactions, decentralization using mining based on proof of work, and smart contracts. The course also covers data mining of transactions using machine learning and social network methods. It helps students understand blockchain and its applications as a key peer-to-peer technology and its uses in smart contracts.

In recent years, machine learning techniques have made significant impact in a wide range of application areas in various industries. This course provides an introduction to machine learning concepts and algorithms, as well as the application areas. Topics will include supervised and unsupervised learning, learning theory etc.

Market research is the way that companies identify, understand and develop the target market for their products.  It is an important component of business strategy, and it draws on the research and analytics skills you have learned thus far in the program. Often market research consists of generating your own data, through quantitative and qualitative methodologies, in pursuit of the market research question. 

This course is an elective that will expand on quantitative and qualitative methodologies that have been introduced previously, provide an introduction to other methodologies that are more specific to market research, and provide hands-on practice in defining a market research plan from start to finish. Students will also learn about particular types of market research studies and when and how they should be deployed. Students will generate and test their own research instruments. Through the use of case studies and simulations, students will learn how market research fits into an overarching marketing plan for a company. 

Students will leave this class understanding the essential aspects of market research, when and how they should be deployed, and the role they could play in small and large companies directing and executing on market research opportunities.

Prerequisites

Required
APAN 5300 Research Design

Recommended
APAN 5600 Strategy and Analytics

This course equips students with essential mathematical foundations for understanding and working with artificial intelligence (AI) algorithms. After a brief introduction to the historical and social context that numbers arise in, students will learn about:

• Linear Algebra: Matrices, matrix-vector multiplication, linear models, change of basis, dimensionality, spectral decomposition, and principal component analysis (PCA).
• Calculus: Rates of change, derivatives, optimization techniques like gradient descent, with a brief touch upon linear approximation.
• Probability and Statistics: Mathematically deriving complex probability distributions out of simpler ones, mathematically deriving statistical testing methods
• Graph Theory: How graphs are used to find relationships among data as well as being a setting for AI-driven problem solving.
• Problem Solving and Algorithms: Applying mathematical concepts to find problem solutions. Students will learn about search methods like uninformed search, informed search with the A* algorithm, and greedy algorithms.
• Computational Theory and Automata: Answering questions about what is computable, what is needed in order to compute something, and using this framework to state how much “information” is contained in a mathematical object.

By the end of this course, students will possess a strong mathematical toolkit to confidently tackle the complexities of modern AI algorithms.

Python is one of the leading open source programming languages for data analysis.

This is an elective course that explores Python programming languages for data science tasks. The students in this course will learn to examine raw data with the purpose of deriving insights and drawing conclusions. Together, we will manipulate large size data sets to extract meaning and generate visualizations.

The course assumes no prior programming experience with Python. We will start by learning the fundamentals of data storage, input and output, control structures, functions, sequence and lists, file I/O, and standard library classes. We will then move on to learning Object-Oriented Programming with Python: encapsulation, inheritance and polymorphism.

To explore the Python data analysis platform, we will focus on IPython (Interactive Python) and Jupyter Notebook. IPython is an enhanced interactive Python terminal specifically designed for scientific computing and data analysis; Jupyter Notebook is a graphical interface that combines code, text, equations, and plots in a unified interactive environment.

Students will learn to work with widely-used libraries, such as pandas for data analysis and statistics; NumPy for its practical multi-dimensional array object; and MatPlotLib for graphical plotting. We will use these libraries to load, explore and visualize real-world datasets.

Organizations need quantitative analysis to solve complex problems and make consequential choices. Research design provides both a coherent framework for collecting relevant evidence and strategies for evaluating that evidence. Knowledge of research design enables organizations to make adaptive and effective use of quantitative analysis in solving problems and making choices. This course serves as a foundational course in the Applied Analytics program.

In this course, you will approach problems as methodological thinkers: you will assess whether the organization is asking the right questions, choosing a relevant design, gathering appropriate and meaningful evidence, and using the appropriate statistical analysis to answer those questions.

To varying degrees and in different organizational contexts, we will work to answer some of the following key questions:

• What are the key questions that strategic decision makers need to formulate and answer in order to inform their decisions?
• What data are available (and unavailable) that might be used to inform the important strategic decisions?
• What research questions are implied by the needs of strategic decision makers?
• What data do we need to measure those variables? Are we currently collecting that data? Why or why not?
• Which analytical methods might be helpful in answering the research question?
• What might challenge the validity of results and how can research results be communicated in a way that mitigates the risks associated with these challenges?
• What are the business factors that influence decisions about how research is undertaken?

In this course, students will learn concepts that are critical to corporate finance, including: financial statement analysis; performance metrics; valuation of stocks and bonds; project and firm valuation; cost of capital; capital investment strategies and sources of capital, and firm growth strategies. Students will work as individuals and in groups to apply the tools of corporate finance to assigned cases. By the end of this course students will understand:

• How to apply fundamental corporate finance tools to analysis of firms’ strategic financial decisions.

• Evaluate the value impact of corporate decisions.

• Explain the rationale for decisions related to mergers & acquisitions or other corporate transactions and allocations of capital.

• Apply the Four Cornerstones of Corporate Finance in your evaluation of whether a firm has effectively created value.

In this course, students will learn fundamental marketing concepts and their application. Students will work extensively with case study projects. By the end of this class students will understand:

• The essential elements of a market and large-scale company strategy

• How to identify customers and competition

• The fundamental elements of the marketing mix (product, price, placement and promotion) 

• How to research consumer behavior and pricing strategies.

This course explores key knowledge management and organizational learning concepts and techniques that are critical to business, individual, and organizational performance. As technology and the network economy drive businesses to compete under continuously accelerating rates of change in technology, business leaders must incorporate knowledge management and learning into their organization’s activities in ways that support and propel their business goals. They must also be proactive in recognizing and responding to the influence of technology on these goals and environment(s) in which they are accomplished. Class sessions encompass a set of topics including purpose, planning, success measurement, and implementation of knowledge management initiatives and organizational learning techniques. Through lectures and individual and collaborative work, students explore how they can use these techniques to improve business performance and strengthen their leadership and management capabilities.

In this course, students will gain an overview of major concepts of management and organization theory, concentrating on understanding human behavior in organizational contexts, with a heavy emphasis on the application of concepts to solve managerial problems. Students will work in a combination of conceptual and experiential activities, including case studies, discussions, lectures, simulations, videos, and small group exercises.

By the end of this course students will:

• Develop the skills to motivate employees
• Establish professional interpersonal relationships
• Take a leadership role
• Conduct performance appraisals

Availability: 

On Campus: Every term
Online: Every term 

In this course, students will develop analytical skills used to formulate and implement marketing-driven strategies for an organization. Students will work on case studies in both individual and team-based projects. By the end of this course, students will: 

• Develop a marketing strategy based on market assessments and company needs

• Develop a deeper understanding of marketing strategies

• Learn how to implement tactics to achieve desired goals

Data modeling is about understanding the data used within our operational and analytics processes, documenting this knowledge in a precise form called the “data model”, and then validating this knowledge through communications with both business and IT stakeholders. Underlying all successful applications is a robust and precise data model, and similarly, most software development failures are due to a lack of understanding of the data or data requirements.

A data model is therefore an essential part of applications development including forward engineering, reverse engineering, and integration efforts. Forward engineering means focusing on business requirements, whereas reverse engineering means modeling existing systems to drive the support, replacement, or customization of applications. Integration projects such as business intelligence efforts, data lakes, and master data initiatives, require a consistent holistic view of concepts such as Customer, Account, and Product.

This course helps students to master data modeling and build data models. Students have the opportunity to explore and create conceptual, logical, and physical data models. Students also learn to work with relational, dimensional, and NoSQL data models. After learning the styles and steps in capturing and modeling requirements, students have the opportunity to apply a best practices approach to building and validating data models through the Data Model Scorecard.

This elective course provides students with a strategic understanding of the healthcare ecosystem, knowledge of how different stakeholder groups use data and analytics to inform scientific, clinical and operational decisions, and how state of the art analytics are transforming every aspect of healthcare from how drugs are discovered and developed, to how patients receive treatment, and how population and individual health outcomes are optimized.   

The adoption of machine learning and AI in healthcare has been slower than in other industry sectors. This may be due in part to the high stakes of healthcare decisions, i.e., patient health outcomes. It is also due in part to the importance of understanding the context of healthcare in the development of analytic solutions, lessons that were learned painfully during the COVID pandemic.  Students will learn how to communicate data and analytics insights to drive trial and adoption by healthcare providers and patients. The course will help students develop the analytics leadership skills that are critical to the realization of the potential of data and analytics to transform healthcare.  Topics include digital health, digital therapeutics, precision medicine, population health, clinical and operational uses of machine learning and AI, and pharmaceutical R&D.  

Exponential growth of information and data—combined with software that can understand and learn from analytic experience—provides entrepreneurs with tremendous opportunities to bring innovative customer-focused solutions to market. While there are no direct paths to bring a new product idea to market, there are easily identifiable milestones that can guide the way from idea generation to product profitability. This course will explore the process of early stage development of knowledge-driven, data intensive digital products like Pandora, Netflix, Watson and Trip Advisor. The goal is to create an entrepreneurial experience at its most elemental and visceral level—ideation, brainstorming, interacting with customers, building a founding team, developing a business model, managing risk, investigating competitors, and pitching the business to potential investors. Students will be exposed to all the pressures and demands of real world start-ups by participating on teams tasked with creating deliverables required to launch a new business.

This course will provide an overview of life insurance company structure, life insurance products, product development and pricing considerations, investments and the regulations and liabilities that drive life insurance company decisions. All life insurance actuaries must master the concepts of financial mathematics and how to apply those concepts to calculate projected present values and accumulated cash flows. You will study these concepts and apply them to calculate basic reserves, new business pricing, and profitability metrics. This will include exploring various types of product designs. Actuaries play an important part in development and interpretation of the various financial statements that insurance companies are required to provide to the public. The course will illustrate the content of the most important statements, exhibits, and schedules and provide a description of their purpose. Insurance cash flows are unique in that there are many uncertainties and those cash flows stretch out into the future over a considerable amount of time. This leads to another important aspect of actuarial science, which is risk analysis and risk management. This course will study how companies map risks and set aside capital to provide for the uncertainties above and beyond those provided for by standard reserves, including an introduction to evolving uses of predictive analytics and enterprise risk management by insurance companies.

Finally, the course will cover current evolving trends, e.g., the growth of online life insurance products and services.

The goal of this elective course is to provide you with a broad understanding of fixed income securities and how they are used for asset liability management (ALM) in financial institutes. This course is designed for individuals who currently work or plan to work as insurance and financial professionals such as actuaries, traders, and quants. The course builds on concepts introduced in several of the program’s core courses and emphasizes the application of theories. The course covers content adapted from the SOA syllabus for fellowship exams and is split into four parts: interest rate risk measurements, interest rate management—ALM strategy, ALM decision-based asset allocation, and value-based management. In this course, you will learn several ALM techniques related to mitigating interest rate risks, managing risk and return trade-offs, and setting strategic asset allocation (SAA) to achieve an optimized risk/return portfolio. Additionally, you will be introduced to the concepts of value-based management and economic value of liabilities. Completing this course will give you a fundamental basis for understanding ALM in financial organizations and further prepare you to apply these concepts in real-life situations under both generally accepted accounting principles (GAAP) and market consistent approaches.

This course is offered both in an online (synchronous) format as well as in an in-person format. 

This course is about leading boundary-spanning coalitions. An old African proverb tells us that, "If you want to go fast, go alone. If you want to go far, go together." While this advice is especially relevant in our interconnected 21st-century world, we have learned that working together is not always easy to do well.

“Collaboration at Scale: Leading Boundary-Spanning Coalitions” takes the study of collaboration into an even wider realm by examining the potential and complexity of large-scale, cross-organizational collaboration, and how to lead it.

The concept of scalability is common in the business world and this course demonstrates what it takes to make collaboration scalable and suitable for a variety of challenging contexts larger than a single organization. Inherent in the concept of scalability are the notions of "appropriate scale" and also "at scale." Both of these notions raise valid questions that we will address in this course. (Though our interpretations of scale have evolved with the advent of social media, specific technology selection is not the focus of the course.)

Students will learn the characteristics, conditions and dynamics of various large-scale collaborations, as well as how to design and lead them effectively. Course materials will be drawn from the for-profit and nonprofit worlds. Using a balance of practice and theory of networks and large system facilitation, students will demonstrate their mastery of course materials through an assignment in which they diagnose and (re)design a “collaboration at scale.” This could be in the business, scientific, religious, political, or humanitarian domains.

Project management has been important to many types of missions, projects, and activities for many years; however, it has been especially critical to the success of large complex projects across decades and centuries.  Large complex projects span the globe across all industries and sectors. They also span concepts, product design, development, manufacturing, operations, and logistics, etc. Products may include hardware, software, services, product support, systems, and systems of systems, etc.

The primary focus of this course will be around project leadership as projects are planned and executed (project management).  The course will start by recognizing the need and benefits of project management for large complex global projects, explore characteristics of project managers, and study the commonality and differences in types of projects.  The course will continue with understanding the essential capabilities of project management, and analyze the variations in project lifecycles. The course will address managing risk throughout the project lifecycle, controls, and performance measurement, and maximizing the use of knowledge.  Lastly, the course will visualize the future of projects and project management structure and core capabilities.

Our fundamental goal is to better prepare leaders for large complex global projects.  This will be gained via readings; real-world case studies; and study, research, analysis, and exploration by the students.  Therefore, the course will require students to engage in reflection, discussion, activities, and assignments aimed at personal unlearning and learning.  The assignment and class discussions will be quite provocative to drive maximum learning.

This course is a workshop in ERISA and Taxation Rules for Actuaries. Actuarial science can be applied and cover a number of welfare benefit arrangements (such as life insurance, medical, disability, severance etc.), qualified plans and nonqualified deferred compensation plans. The services and products that are developed in the actuarial field may be governed by certain federal laws. In the U.S., these arrangements are governed by the Employee Retirement Income Security Act ("ERISA"). In addition, certain federal taxation and reporting rules may apply. To be successful in the field will require an understanding of these rules, reporting requirements, taxation rules and the government agencies (Internal Revenue Service, Department of Labor and Pension Benefit Guarantee Corporation) responsible for oversight of such arrangements. Other topics covered will include SEPs, Simple Plans, 403(b) plans, 457 plans and Nonqualified Deferred Compensation Plans.

This course introduces general principles of ratemaking and reserving as they relate to P&C insurance products. Students will learn actuarial conventions and terminology and structure insurance data accordingly. We will discuss techniques needed to restate historical premium and loss information at current levels and derive consistent profitability metrics. Advantages and disadvantages of various traditional pricing and reserving techniques will be discussed as well as classification of insureds and other important topics.

As the pace of technological change accelerates, and market and social disruptors lurk around the corner, organizations and policy makers find that traditional hierarchies pose a huge disadvantage. Decision-making is often layered and ponderous, insular cultures block new ideas, and information moves inefficiently. Increasingly, managers find that, to compete, they need novel operating models. Organizations and institutions need to readily access resources and markets. At the same time, they need diverse intelligence, large multidisciplinary data sets, and novel product ideas. The answer lies in the network, an organizational construct that involves people engaging across boundaries, organizations, and/or geographies with shared knowledge-creation goals. 

For-profit and nonprofit organizations, alike are embracing networks to share insights and data, act as a voting block, serve customers, and innovate. For example Proctor & Gamble, World Health Organization, the World Bank, and German Aerospace and Technology Center are all leveraging networks. The ideas of “open” and “contagion” are no longer seen as a rarified university experiment. Now these present a viable means for a growing number of purposes: get to market faster, thwart climate change, clean the oceans, and find cures to intractable diseases. 

“The Science of Communities and Networks” presents the quantitative structure, impacts, and practical work of networks. There are many different forms of network, varying in size, shape and purpose. Yet there are some common practices and behavior patterns and models that trace their origins back to the science of the human brain, mathematics and social and behavioral psychology. After computing and interpreting the metrics of social network structure, we will use the Knowledge Network Effectiveness Framework, a logic model flowing backwards from outcomes, to individual and social behavior, to dynamics, to design. We will also use other scholarly research, along with practical cases, to study different network forms: communities of practice, knowledge-networks, crowds, open source, open data, and open innovation. Students will envision, diagnose and design networks for “cooperative advantage.” We will do that while considering that networks operate in the context of human bias, complex contagion, common-pool resource dilemmas, and technology advancement.   

IKNS and other SPS students will find that the course incorporates both social science and data science on the future of work, in which operations and innovation come increasingly from parties outside the organization or department. The course relates to three main themes of the IKNS curriculum, digital transformation, future of work, and collaboration.

This course teaches cutting-edge tools and methods that drive investment decisions at quantitative trading firms, and, more generally, firms applying machine learning to big data. The course will combine presentations of theory, immediately followed by in-class Python programming examples using real financial data. The course will develop a general approach to building models of economic and financial processes, with a focus on statistical learning techniques that scale to large data sets. Among the topics covered are lasso, elastic net, cross validation, Bayesian models, the EM algorithm, Support Vector Machines, kernel methods, Gaussian processes, Hidden Markov Models, and neural networks. The final project will lead the students to build a trading strategy based on the techniques learned throughout the course.

Blockchains have created a new paradigm in secure yet decentralized information management among various entities without requiring trusted intermediaries. Applications to various fields abound including cryptocurrencies (e.g., Bitcoin, Ethereum), banking (Ripple), insurance, and logistics.

This is an introductory course on blockchains and crypt-currencies. The course introduces the concepts of blockchains using Bitcoin as the main example. It then goes into the details related to underlying fundamentals including cryptographic protocols, hash, digital signatures, chaining of blocks of transactions, decentralization using mining based on proof of work, and smart contracts. The course also covers data mining of transactions using machine learning and social network methods. It helps students understand blockchain and its applications as a key peer-to-peer technology and its uses in smart contracts.

How do businesses and their employees navigate the rules and regulations that govern their operation? No matter what the industry, understanding the legal landscape is essential in today’s business environment. This course introduces the fundamental laws and principles governing businesses in the U.S. today. The effect of recent developments in case law and legislation on these topics will be discussed and debated in class.

At the end of the course, students will have a solid understanding of the role the law plays in doing business across industries.

Among the topics to be discussed are:

• Sources of laws and legal principles
• Business organizations
• Corporate governance, compliance and ethics
• Contracts, mergers and acquisitions and business transactions
• Corporate finance - capital raising, IPOs
• Employment law
• Intellectual property
• International business transactions
• Litigation and dispute resolution

How are ideas, products, and innovations protected? Every industry must ask these questions and understanding how the law works to answer them is an invaluable tool in today’s marketplace.This course introduces the fundamental principles of U.S. intellectual property law. The course will explore the basic concepts of copyright law including the requirements for copyright protection and the types of works protected, what rights and limitations come with copyright protection, and how the law is enforced. The course will also cover the main tenets of trademark law, including discussion of the Lanham Act, dilution, and unfair competition. Recent developments and controversies, including intellectual property protection for new technologies and the difficulty of enforcing protections on the Internet will also be discussed.

In this course, we will explore negotiation from several points of view and approaches. We will also look at characteristics that impact the quality of our negotiations and the outcomes, such as the role of emotions, cultural considerations, effectiveness of our communication, and opportunities to seek out negotiation to transform relationships. The course will be a blend of concepts and skills, theory and practice. On some occasions, you will be introduced to a concept and then asked to apply those concepts in an experiential activity. At other times, you will be asked to engage the activity or simulation and then the concepts will be elicited based on your experience. You will have several opportunities to practice developing your skills throughout the course, in terms of enhancing your practice and honing your analytical and conceptual understanding.

This course is a foundation course for learning software programming using the Java language. The course will introduce the student to programming concepts, programming techniques, and other software development fundamentals. Students will learn the concepts of Object Oriented programming using Java. The course will present an extensive coverage of the Java programming language including how to write, compile and run Java applications.

The purpose of this course is to learn programming concept and Object Oriented fundamentals using Java. Students will receive a solid understanding of the Java language syntax and semantics including Java program structure, data types, program control flow, defining classes and instantiating objects, information hiding and encapsulations, inheritance, exception handling, input/output data streams, memory management, Applets and Swing window components.

In this course, students will learn about the valuation of publicly traded equity securities through case study analyses, class discussion, independent exercises, reading assessments, group work, and  weekly deliverables, culminating in a final investor pitch. 

By the end of the semester students will be able to:

• Perform fundamental analysis ("bottoms-up," firm-level, business and financial analysis)

• Prepare pro forma financial statements, estimate free cash flows and apply valuation models.

• Understand the importance of reasoned analysis and critical thinking when evaluating firms.

Deep Learning has become a cornerstone of Artificial Intelligence (AI), with applications in finance, healthcare, sports, autonomous vehicles, chatbots, national security, and more. It is revolutionizing fields like Natural Language Processing, Computer Vision, and Speech Recognition. This advanced, special topic course delves into deep learning, blending key elements from Statistical Machine Learning. Students will gain a solid foundation in supervised learning and other related algorithms and methods. Topics covered include Support Vector Machines, Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures, Generative Adversial Networks (GAN), and Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate models.

The course introduces practitioners of environmental science and sustainability management to the data analysis techniques and statistical methods which are indispensable to their work. The class teaches how to build statistical substantiation and to critically evaluate it in the context of environmental problems. The statistics topics and examples have been chosen for their special relevance to environmental problems, including applications in environmental monitoring, impact assessment, environmental valuation techniques and econometric analyses of sustainable development. Students are assumed to have had no previous exposure to statistics.

The course will focus on sustainability indicators, the process through which they were developed, and how they are used to shape policy and track progress. This course will examine the science and history of our current environmental crisis with a focus on the various policy initiatives and actions being taken globally and locally including the specific efforts of the C40 Cities (40 largest cities) to both mitigate greenhouse gas emissions and prepare for the impacts of climate change. The class will look at case studies from different cities around the world as well as New York City's efforts through PlaNYC while introducing the principles underlying sustainability indicators-including greenhouse gas inventory protocols-and how they are used to influence and shape policies and decisions, and will offer students hands-on experience with these tools.

The goal of this is to make students acquainted with the debate, challenges, and opportunities of a changing climate. The course will focus on the solutions and responses to the climate change challenges facing cities using real world and current examples. The course will survey a broad range of responses to climate change from international frameworks and global treaties to specific actions at the local level. Students will be required to critically evaluate what they have read and heard. In addition, the course will give students an opportunity to learn how to express their ideas verbally and in written form and conduct critical analysis of environmental data to develop and implement public policy.

Assignments will give students the opportunity to use their technical and analytical skills while understanding the real world applications that will be important to their future professional work as planners, policymakers, advocates, architects, designers, and/or environmentalists. This course satisfies the M.S. in Sustainability Management's quantitative analysis requirement.

Underwriting