Quantitative Management Analytics Concentration

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.

Instructor: Fatih Bulut

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.

Instructor: Lei Yu

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?