In-Person Program

Time Series Analysis is a science as well as the art of making rational predictions based on previous records. It is widely used in various fields in today’s business settings. For example, airline companies employ time series to predict traffic volume and schedule flights; financial agencies measure market risk via stock price series; marketing analysts study the impact of a newly proposed advertisement by the sales series. A comprehensive knowledge of time series analysis is essential to the modern data scientist/analyst. This course covers important issues in applied time series analysis: a solid knowledge of time series models and their theoretical properties; how to analyze time series data by using mainstream statistical software; practical experience in real data analysis and presentation of their findings in a logical and clear way to various audiences.

In a traditional linear model, the observed response follows a normal distribution, and the expected response value is a linear combination of the predictors. New methods based on probability distributions other than Gaussian appeared only in the second half of the twentieth century. These methods allowed working with variables that span a broader variety of domains and probability distributions. Besides, methods for the analysis of general associations were developed that are different from the Pearson correlation. This course begins in linear normal models. We will visit the foundations of generalized linear models (GLM) and take a detour to see the survival models. This journey ends at some nonlinear model lookout posts at the instructor’s discretion. This course will prepare students to be ready for and capable of the statistical analysis process. Students will first discover the insights, formulate the propositions, validate the evidence, and finally build the solutions for solving business problems. Following the process properly raises credibility and increases the impact of the results. Besides developing Python codes for carrying out the process, students will learn to tune the software tools for the most efficient implementation and optimal performance. At the end of this course, students will have built their inventory of data analysis codes and their confidence in advocating their propositions to the business stakeholders.

This course is aimed at providing students an introduction to machine learning with data mining techniques and algorithms. It gives a rigorous methodological foundation in analytical and software tools to successfully undertake projects in Data Science. Students are exposed to concepts of exploratory analyses for uncovering and detecting patterns in multivariate data, hypothesizing and detecting relationships among variables, conducting confirmatory analyses, and building models for predictive and descriptive purposes. It will present predictive modeling in the context of balancing predictive and descriptive accuracies.

The objective of this course is three-folds – first, to extend student understanding of predictive modeling with machine learning concepts and methodologies from Machine Learning 1 into the realm of Deep Learning and Generative AI. Second, to develop the ability to apply those concepts and methodologies to diverse practical applications, evaluate the results and recommend the next best action. Third, to discuss and understand state-of-the machine learning and deep learning research and development and their applications. This course clarifies concepts such as Artificial Intelligence, Machine Learning and Deep Learning and distinguishes between Expert and Learning systems. It introduces different types of learning systems such as error-based, information-based, representation, active, and generative modeling. The course expands on recommender systems and regularized regression from Machine Learning 1 using different types of data encoding for learning systems. Additional topics include model selection, fairness, and ethics. Students learn state-of-the-art machine and deep learning applications in the industry along with their pros and cons via case studies, assignments, and a class project. The course is taught in Python. The class covers popular machine learning library APIs and implementations using examples from public github repositories.

This course provides a comprehensive, hands-on introduction to modern cloud-native data engineering. Students learn how to ingest, transform, model, and serve data across distributed cloud environments. Topics include APIs and data pipelines in Python, Linux and cloud foundations, SQL and relational modeling, data warehousing with Snowflake, lakehouse architectures with Databricks and Delta Lake, and distributed data processing with Spark.
The course emphasizes cross-platform cloud design, exposing students to services and deployment patterns across the three major cloud providers: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Students gain experience with containerization using Docker, orchestration with Kubernetes, CI/CD workflows with GitHub Actions, and cloud-based machine learning platforms.
Through hands-on labs, students build production-grade pipelines and AI-ready data applications using modern data application frameworks and user interface design principles. A final project integrates engineering, infrastructure, and multi-cloud deployment best practices into a scalable, end-to-end data system.

Organizations increasingly rely on AI and data science to support strategic decision-making, improve operations, and create competitive advantage in ambiguous real-world situations. Successful data scientists must be able to do more than build models—they must understand business problems, lead stakeholders through ambiguity, communicate effectively, collaborate within teams, and guide projects from definition through implementation.

Leadership and Consulting in Data Science develops the business and communication skills required to deliver organizational value through AI and data science. Students learn how to frame business challenges as data science opportunities; gather and interpret information within complex organizational environments; communicate insights persuasively to technical and non-technical audiences; and work effectively within cross-functional teams.

Using a structured data science delivery process, students will apply methodologies and techniques related to problem definition, data collection, modeling, and business impact guided by responsible AI principles. Through case discussions, team-based exercises, and presentations, students will develop leadership and consulting capabilities to become effective, influential, and trusted AI and data science professionals.

The required Capstone Project is completed over two quarters and covers research design, implementation, and writing. Full-time students start their capstone project in their third quarter. Part-time students generally begin the capstone project in their fifth quarter.

The overarching goal of this course is to take students two steps closer to being “Complete Data Scientists”. The first step is by letting students manage and solve a real data science project with real clients and real problems. Students will complete the design of their Capstone Projects, and begin the implementation. The second step is by exposing them to data science methodologies in the absence of pre-existing data – by exposing them to quantitative methodologies in optimally designing data collection tasks. This course covers the Business analytic process from the translation of business problems and opportunities into questions that can be addressed by using data science, development of analytical plans including methodologies and data to address these issues, and initial implementation of these analytical plan.

In the 2nd quarter, the capstone class is designed to: 1) Provide students maximum flexibility in the latter stages of their Capstone project to work heavily with their Capstone advisors in concluding the execution of the analytic methodology and any client / sponsor deliverables for the project. 2) Provide maximum support to students in the curation and delivery of key project communications: a) Formal research paper. b) Formal business presentation of project details, value, findings and recommendations. c) Live presentation by the team in Capstone Showcase including question / answer session with a judging panel.

Computer vision is the field of computer science that focuses on creating digital systems that can process, analyze, and make sense of visual data in the same way that humans do. Deep learning is a subset of machine learning and a branch of Artificial Intelligence (AI). It involves the training, deployment, and application of large complex neural network architectures to solve cutting-edge problems. Deep Learning has become the primary approach for solving cognitive problems such as Computer Vision and Natural Language Processing (NLP) and has had a massive impact on various industries such as healthcare, retail, automotive, industrial automation, and agriculture. This course will enable students to build Deep Learning models and apply them to computer vision tasks such as object recognition, detection, and segmentation. Students will gain an in-depth understanding of the Deep Learning model development process, tools, and frameworks. Although the focus of the course will primarily be computer vision, students will work on both image and nonimage datasets during class exercises and assignments. Students will gain hands-on experience in popular libraries such as Tensorflow, Keras, and PyTorch. Students will also learn to apply state of the art models such as ResNet, EfficientNet, RCNNs, YOLO, Vision Transformers, etc. for computer vision and work on datasets such as CIFAR, ImageNet, MS COCO, and MPII Human Poses.

This course delves into advanced and evolving topics in machine learning, with a focus that may vary each semester. Students will engage in intensive programming projects, gaining hands-on experience with cutting-edge tools and techniques.

For this semester, the course will emphasize two key areas: transformers and reinforcement learning. Students will explore the internals of transformer models, build a transformer from the ground up, and apply transformers to a range of tasks including NLP, time series analysis, and image processing.

The course will involve extensive use of PyTorch, which we shall study, and TensorFlow, with a strong reliance on Hugging Face libraries, models, and datasets.

Additionally, students will learn the fundamentals of reinforcement learning, working with environments such as OpenAI Gym and leveraging tf-agents to build and train RL models.

This course is ideal for students seeking to deepen their understanding of advanced machine learning topics through practical, project-based learning.

This course explores Advanced Generative AI with a focus on multimodal modeling, a transformative AI paradigm integrating diverse data types—text, images, audio, video, time-series, and point clouds. Multimodal AI is reshaping industries, from autonomous systems and e-commerce to healthcare and intelligent media applications. Students will gain a deep understanding of generative AI, including image generation, transformers in vision, knowledge distillation, vision-language modeling, multimodal fusion, video generation, audio synthesis, and time-series analysis. A key focus is integrating Large Language Models (LLMs) with other modalities to develop next-generation multimodal conversational AI.The course blends theoretical depth with hands-on experience, covering cross-modal alignment, data fusion, and multimodal reasoning using cutting-edge tools. Industry-driven labs connect concepts to real-world applications, equipping students to design innovative AI solutions in autonomous navigation, robotics, healthcare, and finance. Additionally, the course introduces Agentic Systems and Vertical AI Agents, highlighting specialized AI frameworks for intelligent decision-making and adaptive, industry-specific AI agents. Ethical considerations and deployment strategies for autonomous agents are also explored, preparing students to lead AI-driven transformation across industries.

This course provides a strong theoretical and practical skillset for probabilistic machine learning applications. Bayesian inference and modeling methods are important for several areas including prediction, decision making, and risk assessment where modeling the uncertainty is needed. The course begins with an introduction to Bayesian statistical analysis, covering the foundations of Bayesian inference and the application of Bayes’ theorem for statistical inference. We then introduce Bayesian networks, which offer a powerful graphical tool for modeling complex systems and making probabilistic inferences. The course then advances to cover more sophisticated topics such as Markov Chain Monte Carlo (MCMC) methods for sampling from complex probability distributions, hierarchical models, and model selection techniques. The final three weeks are dedicated to cutting-edge methodologies like Generative Deep Learning, Variational Autoencoders, and Bayesian Neural Networks, all rooted in Bayesian Machine Learning. Upon completion, students will be equipped to apply Bayesian methods to a wide range of real-world problems in fields such as engineering, business, finance, and public policy, addressing challenges like missing data or training AI models that are able to say ‘I don’t know’.

This course is designed to equip students with the knowledge and skills to perform causal inference with machine learning. Students learn practical skills for designing and analyzing experiments. The course begins with a quick overview of the basics of correlational and cross-sectional analytical techniques. It then introduces the importance of randomization in explainability and causal inference. The issues of bias in observational studies are examined. Students use AI/ML models to quantify randomization errors and correct violations of non-randomization. Finally, counterfactuals for individual predictions are examined.

This course focuses on marketing science methods and algorithms. The course will expose and immerse students in the marketing science algorithms. The course would cover algorithms for undertaking competitive analysis in the digital landscape, market segmentation, mining databases for effective digital marketing, design of new digital and traditional products, forecasting sales and product diffusion, real time product positioning, intra omni-channel optimization and inter omni-channel resource allocation, and pricing across both omni-channel marketing effectiveness and ROI. The course will use a combination of lecture, in-class discussions, group assignments, and a final group project. The course lays special emphasis on algorithms. Hence it draws heavily from the fields of optimization, machine-learning based recommendation systems, association rules, consumer choice models, Bayesian estimation, experimentation and analysis of covariance, advanced visualization techniques for mapping brand perceptions, and analysis of social media data using advanced NLP techniques.

Given the breadth of the field of health analytics, this course will provide an overview of the development and rapid expansion of analytics in healthcare, major and emerging topical areas, and current issues related to research methods to improve human health. We will cover such topics as security concerns unique to the field, research design strategies, and the integration of epidemiologic and quality improvement methodologies to operationalize data for continuous improvement. Students will be introduced to the application of predictive analytics to healthcare. Students will understand factors impacting the delivery of quality and safe patient care and the application of data-driven methods to improve care at the healthcare system level, design approaches to answering a research question at the population level, become familiar with the application of data analytics to impacting care at the provider level through Clinical Decision Systems, and understand the process of a Clinical Trial.

In today’s data driven enterprise, data storytelling using effective visualization strategies is an essential skill for analytics practitioners in almost every field to explore and present data. This course focuses on modern data visualization technologies, tools, and techniques to convert raw data into actionable information. Modern data visualization tools are at the forefront of the “self-service analytics” architectures which are decentralizing analytics and breaking down IT bottlenecks for business experts. Moreover, with its foundations rooted in statistics, psychology, and computer science, data visualization shows you how to better understand the data, present clear evidence of your findings to your intended audience and tell engaging data stories through charts and graphics. This course is designed to introduce data visualization as a medium of effective communication using strategic storytelling, and the basis for interactive information dashboards.

Extracting actionable insights from unstructured text and designing cognitive applications remains crucial to applied data science. Students in this course will learn the foundations of natural language processing, including text preprocessing techniques such as text normalization, lemmatization, stemming, and regex. The course will cover supervised learning methods like text classification using n-grams, TF-IDF vectorization, and sentiment analysis, as well as unsupervised approaches like topic modeling (LDA, NMF, BERTopic) and paraphrase mining. Neural network-based NLP techniques will be introduced, covering distributional similarity, word embeddings (Word2Vec, FastText, GloVe), and deep learning architectures, including CNNs, LSTMs, GRUs, and transformer-based models such as BERT and ModernBERT. Students will explore question-answering systems, integrating extractive methods (SQuAD) and generative techniques (T5, BART). Advanced topics will include instruction-tuned large language models (ChatGPT, DeepSeek), reinforcement learning (RLHF, PPO), retrieval-augmented generation, and agentic AI. The course will provide hands-on experience in Python-based text analysis and practical applications using modern NLP frameworks, including transformer-based models and retrieval systems.

This course dives into the realm of Generative AI, offering a comprehensive look into the world of Large Language Models (LLMs), image generation techniques, and the fusion of vision and text through multimodal models. Drawing from core concepts in neural networks, transformers, and advanced techniques such as prompt engineering, vision prompting, and multimodality representation, students will explore the capabilities, applications, and ethical considerations of generative models. This course culminates in hands-on projects, allowing participants to apply theory to practical scenarios.

This course has two primary objectives. First, it equips students with a clear understanding of Machine Learning Operations (MLOps) and Inference Engineering, including their critical role in scalable enterprise deployment of AI/ML systems and their extension into Large Language Model Operations (LLMOps). Second, it provides students with practical exposure to software engineering, model engineering, and inference engineering, with emphasis on runtime performance, infrastructure design, and modern platforms and tools.

Designed to bridge the chasm between AI/ML experimentation and enterprise production deployment, the course presents MLOps through three core pillars: data engineering, model engineering, and inference engineering. Students explore data engineering topics such as software architecture, CI/CD, and data versioning; model engineering concepts including AI/ML pipelines, Continuous Training (CT), AutoML, and A/B experimentation; and inference engineering practices such as testing, containerization, quantization, batching, and model monitoring. The course emphasizes industry best practices for deploying AI/ML systems at scale, while introducing state-of-the-art platforms and tools including Databricks, Microsoft Foundry, AWS AI, Google Enterprise, GitHub, Spark, Docker, and Kubernetes. By the end of the course, students are prepared to manage the full AI/ML lifecycle and take models from ideation to enterprise-grade production.

Extracting actionable insights from unstructured text and designing cognitive applications remains crucial to applied data science. Students in this course will learn the foundations of natural language processing, including text preprocessing techniques such as text normalization, lemmatization, stemming, and regex. The course will cover supervised learning methods like text classification using n-grams, TF-IDF vectorization, and sentiment analysis, as well as unsupervised approaches like topic modeling (LDA, NMF, BERTopic) and paraphrase mining. Neural network-based NLP techniques will be introduced, covering distributional similarity, word embeddings (Word2Vec, FastText, GloVe), and deep learning architectures, including CNNs, LSTMs, GRUs, and transformer-based models such as BERT and ModernBERT. Students will explore question-answering systems, integrating extractive methods (SQuAD) and generative techniques (T5, BART). Advanced topics will include instruction-tuned large language models (ChatGPT, DeepSeek), reinforcement learning (RLHF, PPO), retrieval-augmented generation, and agentic AI. The course will provide hands-on experience in Python-based text analysis and practical applications using modern NLP frameworks, including transformer-based models and retrieval systems.

This course introduces students to how optimization and simulation techniques can be used to solve many real-life problems. It will cover two classes of optimization methods. First class has been developed to optimize real, non- simulated systems or to find the optimal solution of a mathematical model. The methods that belong to this class include liner programming, quadratic programming and mixed-integer programming. Second class of methods has been developed to optimize a simulation model. The difference with the classical mathematical programming methods is that the objective function (which is the function to be minimized or maximized) is not known explicitly and is defined by the simulation model (computer code). The course will demonstrate multiple approaches to build simulation models, such as discrete event simulations and agent-based simulations. Then, it will show how stochastic optimization and heuristic approaches can be used to analyze the simulated system and design a sequence of computational experiments that allow to develop a basic understanding of a particular simulation model or system through exploration of the parameter space, to find robust plausible behaviors and conditions and robust near-optimal solutions that are not prone to being unstable under small perturbations.

This course provides students with an introduction to quantitative finance, covering financial institutions, markets, instruments, and core investment concepts. Students will learn to apply quantitative finance and data science techniques to develop and evaluate investment strategies using real-world financial data. The course delves into well-established methods from academia and industry, applying traditional statistical and machine learning approaches, along with optimization techniques, to signal construction and quantitative investing.

Developing end-to-end automation and intelligent systems is now the most advanced area of application for analytics. Building such systems requires proficiency in programming, understanding of computer systems, as well as knowledge of related analytical methodologies, which are the skills that this course aims to teach to students. The course focuses on python and is tailored for students with basic programming knowledge in Python. The course is partially project based. During the first three sessions, we will review basic python concepts and then learn more advanced python and the ways to use Python to handle large data flows. The later sessions are project based and will focus on developing end-to-end analytical solutions in the following areas: Finance and trading, blockchains and crypto-currencies, image recognition, and video surveillance systems.

“Big Data” continues to grow exponentially in our large-scale transactional world where 100,000s of SKUs and millions of customers are interacting with 1:1 offers that include differential pricing, shipping timing/costs and even made to order “custom” product configurations. These consumer behaviors are quickly advancing the availability of new data and techniques within the discipline of Data Science. This elective course will give students the opportunity to apply their skills in data visualization, data mining tools, predictive modeling, and advanced optimization techniques to address Supply Chain challenges. The course focuses on the use of Advanced Predictive Modeling, Machine Learning, AI and other Data Science insight and activation tools are to automate and optimize the performance of the Supply Chain. Students will also learn how to optimize the performance of the Supply Chain from the lens of multiple related disciplines including: Sales Forecasting, Warehousing/Inventory Management, Promotion, Pricing, Logistics Network Optimization, Freight Cost Management, Manufacturing, Retail POS Information, Ecommerce, Consumer Data, and Product Design/Packaging. After completing this course, you will be prepared to work in any of the numerous specialty areas possible in the world of Supply Chain Management.