APPLIED ANALYTICS IN ORGANIZATIONAL CONT
APP AN IN ORG CONTEXT
- M 6:10 p.m.-8 p.m.
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Instructor:
Panagiotis Tarhanidis
Info - 3 points
DATA MODELING
DATA MODELING ONLINE
- M 6:10 p.m.-8 p.m.
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Instructor:
Steven Hoberman
Info - 3 points
INTRO TO PROGRAMMING USING JAVA
INTRO TO PROGRAMMING - JA
This course introduces students to the foundational principles of object-oriented programming (OOP) using Java, a robust, platform-independent language widely used in industry for building scalable and secure applications. Through hands-on experience and instruction, students will explore core OOP concepts, including abstraction,
encapsulation, inheritance, and polymorphism, while gaining practical experience with modular code design and reusability. Java’s extensive libraries and frameworks will be introduced to demonstrate its applications to the real-world use cases.
This course is designed for students without prior programming experience in programming. While not required, familiarity with based computing concepts (variables, loops, functions,) is recommended.
- M 6:10 p.m.-8 p.m.
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Instructor:
Feyzi Bagirov
Info - 1.5 points
APPLIED MACHINE LEARNING I
APPLD MACHINE LEARNING I
APPLIED MACHINE LEARNING I
- M 8:10 p.m.-10 p.m.
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Instructor:
David Shilane
Info - 3 points
APPLIED MACHINE LEARNING I
APP MACHINE LEARN I ONLIN
APPLIED MACHINE LEARNING I
- R 8:10 p.m.-10 p.m.
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Instructor:
David Shilane
Info - 3 points
APPLIED MACHINE LEARNING II
APPLIED MACHINE LEARNING II
- T 8:10 a.m.-10 a.m.
- F 10:10 a.m.-noon
- W 6:10 p.m.-8 p.m.
- R 8:10 p.m.-10 p.m.
- T 8:10 p.m.-10 p.m.
- M 8:10 p.m.-10 p.m.
- F 12:10 p.m.-2 p.m.
- R 8:10 p.m.-10 p.m.
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Instructor:
Kitty K Chan
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Instructor:
Vishal Lala
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Instructor:
Birol Emir
CULPA: 14 Info -
Instructor:
Andrew Assing
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Instructor:
Evgeniya Reshetnyak
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Instructor:
Vinay P Mahadeo
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Instructor:
Vishal Lala
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Instructor:
Zachary Kurtz
Info - 3 points
PYTHON FOR DATA ANALYSIS
PYTHON FOR DATA ANALYSIS ONLNE
- M 8:10 p.m.-10 p.m.
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Instructor:
Max Vilenchik
Info - 3 points
SQL
SQL & RELATIONAL DB
- T 8:10 p.m.-10 p.m.
- W 6:10 p.m.-8 p.m.
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Instructor:
Day Yi
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Instructor:
Michael J Coakley
Info - 3 points
SQL
SQL & RELATIONAL DB ONLINE
- S 10:10 a.m.-noon
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Instructor:
Nikolaos Machairas
Info - 3 points
FUNDAMENTALS OF DATA ENGINEERING
FUNDAMENTALS OF DATA ENGINEERING
- T 6:10 p.m.-8 p.m.
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Instructor:
Robert Simione
Info - 3 points
FUNDAMENTALS OF DATA ENGINEERING
FUND OF DATA ENG ONLINE
FUNDAMENTALS OF DATA ENGINEERING
- R 6:10 p.m.-8 p.m.
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Instructor:
Darshan Desai
Info - 3 points
CLOUD COMPUTING
Data analytics have 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, et al. 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.
- R 8:10 a.m.-10 a.m.
- M 8:10 p.m.-10 p.m.
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Instructor:
Mohsen Golalikhani
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Instructor:
Mohamed Mneimneh
Info - 3 points
MARKET RESEARCH
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.
This course is designed for students who have completed the Research Design and Strategy and Analytics core courses, and who are exploring how research fits into product marketing. You will leave this class understanding the essential aspects of market research, when and how they should be deployed, and the role you could play in small and large companies directing and executing on market research opportunities.
- R 8:10 p.m.-10 p.m.
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Instructor:
Diahanna L Post
Info - 3 points
AI USER INTERFACES FOR ANALYTICS
AI UI FOR ANALYTICS
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.
The course content comprises a blend of conceptual learning and practice assignments. Weekly lectures and reading materials will cover the fundamentals of data visualization and user experience designs. Students will put the gained knowledge into practice through individual design and coding assignments and a group term project.
- M 6:10 p.m.-8 p.m.
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Instructor:
Michelle Morales
Info - 3 points
MATHEMATICS FOR AI
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 between 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.
- T 8:10 a.m.-10 a.m.
- M 6:10 p.m.-8 p.m.
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Instructor:
Robert Simione
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Instructor:
Thilan Arachchi Appuhamillage
Info - 3 points
ANALYTICS FOR BUSINESS OPERATIONS MANAGE
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.
Analytics for Business Operations Management is an elective that is intended for students who are interested in pursuing a career using analytics and operational insights to drive organizational success in a competitive global marketplace across various industries.
- R 6:10 p.m.-8 p.m.
- F 10:10 a.m.-noon
- M 6:10 p.m.-8 p.m.
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Instructor:
Arash Khatibi
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Instructor:
Janice Winch
Info -
Instructor:
Andy W Chen
Info - 3 points
FINANCIAL SEC ANALYSIS AND PORTFOLIO MGT
FIN SEC ANALYSIS AND PORT
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.
ARTIFICIAL INTELLIGENCE OF THINGS
AI OF THINGS
- R 6:10 p.m.-8 p.m.
- W 8:10 p.m.-10 p.m.
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Instructor:
Petros Zerfos
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Instructor:
Javid B Huseynov
Info - 3 points
ANALYTICS FOR CYBERSECURITY MANAGEMENT
ANALYTICS FR CYBERSECURIT
This course is designed to immerse students in the intersection of cybersecurity and data analytics. The course explores how modern data-driven approaches are revolutionizing the way organizations detect and manage cyber threats. Students will engage deeply with core cybersecurity concepts, such as network security, vulnerability management, and threat intelligence, while also learning to leverage cutting-edge data analytics and artificial intelligence to solve real-world security problems. Through hands-on exercises, coding assignments, and case studies, students will gain practical skills in analyzing logs and telemetries, building detection systems, and applying machine learning to security operations.
- M 8:10 p.m.-10 p.m.
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Instructor:
Muhammed Bulut
Info - 3 points
APPLIED AGENTIC AI
Artificial intelligence (AI) has entered a new era defined not just by what models can generate, but by what AI systems can autonomously do. Agentic AI, systems built on large language models (LLMs) that can reason, plan, use tools, access data, and act on behalf of users, is rapidly reshaping how organizations approach analytics, decision-making, and workflow automation. From AI agents that autonomously query databases and build dashboards, to multi-agent systems that coordinate complex analytical pipelines, agentic AI represents a significant shift in how analytics professionals design, build, and deliver insights.
This course provides a comprehensive, hands-on introduction to the design, development, and deployment of agentic AI systems for analytics use cases. Students will explore the foundational concepts behind LLM-based agents, including reasoning patterns (ReAct, Chain-of-Thought, Plan-and-Execute), tool use and function calling, memory architectures, and retrieval-augmented generation (RAG). The curriculum emphasizes practical agent development using modern frameworks such as LangGraph, LlamaIndex, CrewAI, and the OpenAI and Anthropic SDKs, and it addresses the ethical, governance, and safety considerations essential to responsible deployment.
- M 8:10 p.m.-10 p.m.
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Instructor:
Javid B Huseynov
Info - 3 points
STRATEGY AND ANALYTICS
n/a
- F 12:10 p.m.-2 p.m.
- F 2:10 p.m.-4 p.m.
- M 8:10 p.m.-10 p.m.
- R 6:10 p.m.-8 p.m.
- F 10:10 a.m.-noon
- W 6:10 p.m.-8 p.m.
- R 8:10 p.m.-10 p.m.
- M 6:10 p.m.-8 p.m.
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Instructor:
Joseph R Schlosser
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Instructor:
Arunava Bhattacharyya
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Instructor:
Ranjit Kumble
Info -
Instructor:
Peter Went
Info -
Instructor:
Lisa Godek
Info -
Instructor:
Ashoke S Bhattacharjya
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Instructor:
Huan Liu
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Instructor:
Sandy Balkin
Info - 3 points
STRATEGY AND ANALYTICS
STRATEGY AND ANALYTICS ONLINE
n/a
- T 6:10 p.m.-8 p.m.
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Instructor:
David Kreutter
Info - 3 points
ANALYTICS & LEADING CHANGE
ANALYTICS & LEADNG CHNG ONLINE
- W 6:10 p.m.-8 p.m.
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Instructor:
David Kreutter
Info - 3 points
STORYTELLING WITH DATA AND AI
STORYTELLING WITH DATA AI
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.
- W 6:10 p.m.-8 p.m.
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Instructor:
Benjamin Kinsella
Info - 3 points