Focus Areas

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

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

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?