MS in Applied Data Science Courses

Introduction to Statistical Concepts
If you are required to take this course it will be held in the 5 weeks leading up to the start of your
first quarter. The course covers theoretical distributions and the way these distributions are used
to assign probabilities to events in some depth. The course also introduces students to
descriptive statistical methods to explore and summarize data, drawing inferences about a
population from a sample, assessing relationships between variables, and making predictions
based upon relationships between variables. Offered during the Autumn Pre-Quarter

R for Data Science
If you are required to take this course it will be held in the 5 weeks leading up to the start of your
first quarter. This course is an introduction to the essential concepts and techniques for the
statistical computing language R. Topics covered include the R and RStudio environment,
arithmetic, basic data structure, importing and exporting data, visualization, and basic statistics.
Offered during the Autumn Pre-Quarter

Python for Data Science
If you are required to take this course it will be held concurrently with the first five weeks of your
first quarter in the program. This course in Python starts with an introduction to the Python
programming language basic syntax and environment. It methodically builds up the learner’s
experience from the level of simple python statements and expressions to writing succinct,
efficient, and fast Python expressions and package the code in methods and classes. In
general, the course is geared toward developing a data scientist’s toolbox such as data
importing, cleaning and preparation, and covers a number of machine learning algorithms. The
course expands beyond these skills as it stresses upon the importance of some of Python’s
most unique and powerful features and serves as an introduction to object oriented
programming and Python Classes. Offered during the first 5 weeks of the Autumn Quarter

Advanced Linear Algebra for Machine Learning
If you are required to take this course it will be held concurrently with the second five weeks of
your first quarter in the program. The advanced linear algebra course is focused on the
theoretical concepts and real-life applications of linear algebra for machine learning. Upon
completion of this course, students will have a strong foundation of linear algebra and linear
analysis topics essential for the development of core machine learning and data mining
concepts. Offered during the second 5 weeks of the Autumn Quarter

This course will focus on your professional development as a data scientist as you work to
advance in your career throughout your master’s program and beyond. The core areas will
focus on how to identify and continue to develop your skills, explore the broad array of jobs that
data scientists obtain and determine what types of companies may be the best fit for the next
stages of your career. This class will help prepare you for the data science marketplace, present
yourself confidently, and assist you in receiving an internship and full-time job offer upon
graduation. For more information please visit the FAQ website.

ADSP 31006 Time Series Analysis and Forecasting
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. 
Prerequisite: Statistical Models for Data Science
Offered In-Person: Winter Quarter
Offered Online: Winter Quarter

ADSP 31012 Data Engineering Platforms
Effective data engineering is an essential first step in building an analytics-driven
competitive advantage in the market. Modern data engineering platforms reduce manual
data preparation by automating processes, which in turn, enable companies to focus on
deriving efficiencies in data processing to develop impactful business insights. This
course provides students with a thorough understanding of the fundamentals of data
engineering platforms, for both operational and analytical use cases, while gaining
hands-on expertise in building these platforms in a way to develop analytical solutions
effectively. Students will have the opportunity to construct both relational and analytical
databases on the cloud or on premise from real-life datasets while using programmatic
or configuration driven data pipelines. By the end of the course, students will be able to
design and implement an end-to-end data engineering platform capable of supporting
sustainable analytics solutions.
Prerequisite: None
Offered In-Person: Autumn and Spring Quarters
Offered Online: Autumn and Spring Quarters

ADSP 31013 Big Data and Cloud Computing
This course teaches students how to approach Big Data and large-scale machine
learning applications. While there is no single definition of Big Data and multiple
emerging software packages exist to work with Big Data, we will cover the most popular
approaches. Students will learn the Big Data infrastructure, including Linux, Massive
parallelization and Distributed Computing, and how to apply both Hadoop and Spark
map-reduce concepts for clustering, similarity search, web analytics and classification.
During the course, we will cover the applications of NoSQL systems, such as JSON
stores, object storage and Elasticsearch. The cloud computing section of the course will
focus on virtualization and container orchestration, including virtual machines, dockers
and Kubernetes. During the course students will gain hands-on expertise leveraging
Hive, Pig, Python and PySpark for Big Data applications in client-server environments.
For MScA students in the 12-course curriculum who wish to take Big Data Platforms as a
core course, instead of MSCA 31012 Data Engineering Platforms: Certain technical skills
and knowledge are required to be successful in this course.
Prerequisite: None
Offered In-Person: Autumn and Spring Quarters
Offered Online: Autumn and Spring Quarters

ADSP 31014 Statistical Models for Data Science
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.
Recommended Course: Introduction to Statistical Concepts
Offered In-Person: Autumn Quarter
Offered Online: Autumn Quarter

ADSP 31016 Leadership and Consulting for Data Science
Professional organizations see value in data science when it helps them to achieve their
strategic goals, and the current job market likewise rewards data scientists who can use
data to advance organizational interests, either as an external consultant or within
internal operations teams. Data scientists can become successful (and highly
marketable) leaders in today’s professional world, but they require an uncommon skill
set: the strategic awareness to align data requirements with business requirements, the
technical proficiency to choose a methodology appropriate to each new problem, and the
communication skills to both execute the plan as part of a broader team and persuade
others of their findings.
The Leadership and Consulting for Data Science course is focused on:
● Learning techniques and proven methods to effectively grasp the business
domain including organizational dynamics of consultancies and client
organizations
● Developing relevant solutions to enterprise problems using the sampling
methods, traditional statistical techniques and modern machine learning models
that deliver value to the organization
● Practicing successful project delivery through effective data discovery, influential
team membership and leadership, project management, and communication at
every stage
This course will not only make you a better data scientist; it will make you and your
analyses more approachable, more persuasive, and ultimately more successful.
Prerequisite: None
Offered In-Person: Autumn Quarter
Offered Online: Autumn Quarter

ADSP 31017 Machine Learning I
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.
Students will learn applications of statistical sampling and inference, conduct feature
engineering, data reduction via cluster analyses, dimension reduction via principal
components analysis, predictive modeling via Logistic Regression, Multinomial and
Ordered Logit, Classification and Regression Trees, Random Forests, Bagging,
Boosting, and Ensemble models, Naïve Bayes, K Nearest Neighbors, Support Vector
Machines and Kernel methods, t-Stochastic Neighborhood Embedding, Latent Dirichlet
Allocation, and Self Organizing Maps. Students will also be exposed to the important
class of latent class and mixture models, and recommender systems. Students will learn
about model selection criteria – in both training samples (criteria such as Likelihood
based Chi-Squares, AIC, BIC), and test samples (criteria such as Bootstrapping,
resampling, and K-fold and train-test validations). Missing Data imputation and Outlier
detection techniques will also be explored.
The course will be taught in both Python and R. Students will have the option of doing
their homework in R, Python, or both. Students will be encouraged to write original,
professional quality code for some of the advanced algorithms that we will discuss. More
advanced students will be given ample opportunities to advance their skills in data
science and programming and establish their own pace and structure of learning.
Prerequisite: Statistical Models for Data Science
Offered In-Person: Winter Quarter
Offered Online: Winter Quarter

ADSP 31018 Machine Learning II
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 (model examples in parentheses) such as error-based
(supervised/unsupervised), information-based (boosting), representation (transfer),
active, and generative modeling (variational autoencoder/ GAN/LLM). The course
expands on recommender systems and regularized regression from Machine Learning 1
using different types of data encoding for learning systems (such as one-hot, vector
embeddings). 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.
Prerequisite: Machine Learning I
Offered In-Person: Spring Quarter
Offered Online: Spring Quarter

ADSP 32001 Quantitative Finance: Methods and Applications
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.
Prerequisite: Statistical Models for Data Science
Offered In-Person: Winter Quarter
Offered Online: Winter Quarter

ADSP 32007 Data Visualization Techniques
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.
Prerequisite: None
Offered In-Person: Autumn and Winter Quarters
Offered Online: Winter Quarter

ADSP 32009 Data Science for Healthcare
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.
Prerequisite: Statistical Models for Data Science
Offered In-Person: Spring Quarter
Offered Online: Summer Quarter

ADSP 32013 Optimization and Simulation Methods for Data Science
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
linear 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.
Prerequisite: None
Offered In-Person: Summer and Autumn Quarters
Offered Online: Summer Quarter

ADSP 32014 Bayesian Machine Learning with Generative AI Applications
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’.
Prerequisite: None
Offered In-Person: Winter, Spring, Summer, and Autumn Quarters
Offered Online: Winter and Summer Quarters

ADSP 32015 Digital Marketing Analytics in Data Science
Successfully marketing brands today requires a well-balanced blend of art and science.
This course introduces students to the science of web analytics while casting a keen eye
toward the artful use of numbers found in the digital space. The goal is to provide
marketers with the foundation needed to apply data analytics to real-world challenges
they confront daily in their professional lives. Students will learn to identify the web
analytic tool right for their specific needs; understand valid and reliable ways to collect,
analyze, and visualize data from the web; and utilize data in decision making for their
agencies, organizations or clients. By completing this course, students will gain an
understanding of the motivations behind data collection and analysis methods used by
marketing professionals; learn to evaluate and choose appropriate web analytics tools
and techniques; understand frameworks and approaches to measuring consumers’
digital actions; earn familiarity with the unique measurement opportunities and
challenges presented by New Media; gain hands-on, working knowledge of a
step-by-step approach to planning, collecting, analyzing, and reporting data; utilize tools
to collect data using today’s most important online techniques: performing bulk
downloads, tapping APIs, and scraping webpages; and understand approaches to
visualizing data effectively.
Prerequisite: None
*course not currently scheduled for the 2025-2026 academic year

ADSP 32017 Advanced Machine Learning and Artificial Intelligence
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.
Prerequisite: Machine Learning II
Offered In-Person: Summer and Autumn Quarters
Offered Online: Winter and Summer Quarters

ADSP 32018 Next-Gen NLP: LLM and AgenticAI in Practice
Explore the forefront of Natural Language Processing through Transformer architectures and large language models. Students will engage with core tasks including classification, question answering, and text generation while progressing from foundational methods to deep learning approaches. The course highlights fine-tuning LLMs, prompt engineering, retrieval-augmented generation, and agentic AI. Instruction is hands-on in Python using libraries such as PyTorch and the Hugging Face ecosystem.
Prerequisite: Statistical Models for Data Science
Offered In-Person: Winter and Spring Quarters
Offered Online: Winter and Spring Quarters

ADSP 32019 Real Time Intelligent Systems
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.
Prerequisite: None
Offered In-Person: Winter Quarter
Offered Online: Winter Quarter

ADSP 32020 Deep Reinforcement Learning
This course is an introduction to reinforcement learning, also known as neuro-dynamic
programming. It discusses basic and advanced concepts in reinforcement learning and
provides several practical applications. Reinforcement learning refers to a system or
agent interacting with an environment and learning how to behave optimally in such an
environment. An environment typically includes time, actions, states, uncertainty and
rewards. Reinforcement learning combines neural networks and dynamic programming
to find an optimal behavior or policy of the system or agent in a complex environment
setting. Neural network approximations are used to circumvent the well-known ‘curse of
dimensionality’ which has been a barrier to solving many practical applications. Dynamic
programming is the key learning mechanism that the system or the agent uses to
interact with the environment and improve its performance. Students will master key
learning techniques and will become proficient in applying these techniques to complex
stochastic decision processes and intelligent control.
Prerequisite: None
Offered In-Person: Spring Quarter
Offered Online: Spring and Autumn Quarters

ADSP 32021 Machine Learning Operations
The objective of this course is two-fold: first, to understand what Machine Learning
Operations (MLOps) is and why it is a key component in enterprise production
deployment of machine learning projects, and second, to expose students to software
engineering, model engineering and state-of-the-art deployment engineering with
hands-on platform and tools experience. This course crosses the chasm that separates
machine learning projects/experiments and enterprise production deployment. It covers
three pillars in MLOps: software engineering such as software architecture, Continuous
Integration/Continuous Delivery and data versioning; model engineering such as AutoML
and A/B experimentation; and deployment engineering such as docker containers and
model monitoring. The course focuses on best practices in the industry that are critical to
enterprise production deployment of machine learning projects. Having completed this
course, a student understands the machine learning lifecycle and what it takes to go
from ideation to operationalization in an enterprise environment. Furthermore, students
get exposure to state-of-the-art MLOps platforms such as allegro, xpresso, Dataiku,
LityxIQ, DataRobot, AWS Sagemaker, and technologies such as gitHub, Jenkins, slack,
docker, and kubernetes.
Prerequisite: Machine Learning I and Data Engineering Platforms or Big Data & Cloud
Computing
Offered In-Person: Spring and Autumn Quarters
Offered Online: Spring and Autumn Quarters

ADSP 32023 Advanced Computer Vision with Deep Learning
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.
Prerequisite: Python for Data Science (Recommended); Machine Learning I
Offered In-Person: Spring, Summer, and Autumn Quarters
Offered Online: Spring, Summer and Autumn Quarters

ADSP 32024 Data Science for Algorithmic Marketing
This course focuses on marketing science methods and 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.
Prerequisite: Statistical Models for Data Science
Offered In-Person: Winter Quarter
Offered Online: Winter Quarter

ADSP 32025 Supply Chain Optimization
“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 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.
Prerequisite: None
Offered In-Person: Winter and Autumn Quarters
Offered Online: Course not currently scheduled for the 2025-2026 academic year

ADSP 32026 Python for ML Engineering
This course prepares data science students to go beyond programming constructs and
data science libraries. This course provides an understanding of web applications so
students can get a deeper understanding of how their models are deployed. They are
taught python environment management, required for production work. They are taught
advanced data structures such as trees and graphs to allow them to work with more
complex modes and more advanced control structures, such as generators and
recursive functions to help them write more expressive code.
Prerequisite: Students are expected to be fluent in programming and very comfortable with
Python.
Offered In-Person: Winter Quarter
Offered Online: Spring Quarter

ADSP 32027 Generative AI: Principles and Applications
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.
Prerequisites: Machine Learning I
Offered In-Person: Spring Quarter
Offered Online: Spring and Autumn Quarters

ADSP 32028 Applied Generative AI: Agents and Multimodal Intelligence
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.
Prerequisites: Machine Learning II
Offered In-Person: Summer and Autumn Quarters
Offered Online: Summer and Winter Quarters

ADSP 32029 Causal Models for Data Science
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.
Prerequisite: Statistical Models for Data Science and Machine Learning I
Offered In-Person: Spring, Summer, and Autumn Quarters
Offered Online: Spring, Summer, and Autumn Quarters

The Capstone Projects are the culminating experience for the MS in Applied Data Science Program and are designed to be an opportunity to showcase knowledge and expertise gained from completing core courses and advanced electives. Therefore, students will complete Capstone during their final two consecutive quarters.

ADSP 34002 Capstone I
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. 
Offered In-Person: Spring and Autumn Quarters
Offered Online: Spring and Autumn Quarters

ADSP 34003 Capstone II
The Capstone II 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.
Offered In-Person: Summer and Autumn, and Winter Quarters
Offered Online: Summer and Winter Quarters