Course Progressions

In this course, we will explore statistical distributions and how they help us assign probabilities to events during model training. We’ll also learn statistical methods for exploring, describing, and summarizing data. We’ll learn to use a sample to make inferences about a hypothesized population, discover and evaluate relationships between variables, and make predictions based on those relationships.

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.

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.

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.

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 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.

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.

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.

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.

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.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Applied Generative AI: Advanced Concepts & Multimodal Modeling, Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Machine Learning with GenAI Applications, Data Science for Algorithmic Marketing, Data Visualization Techniques, Next-Gen NLP: LLM and AgenticAI in Practice, Quantitative Finance: Methods and Applications, Data Science for Healthcare, Machine Learning Operations and Inference Engineering, Optimization and Simulation Methods for Data Science, Real-Time Intelligent Systems, Deep Reinforcement Learning, Python for ML Engineering, Causal Models for Data Science, Scalable Data Systems & AI Pipelines, and Supply Chain Optimization.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Applied Generative AI: Advanced Concepts & Multimodal Modeling, Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Machine Learning with GenAI Applications, Data Science for Algorithmic Marketing, Data Visualization Techniques, Next-Gen NLP: LLM and AgenticAI in Practice, Quantitative Finance: Methods and Applications, Data Science for Healthcare, Machine Learning Operations and Inference Engineering, Optimization and Simulation Methods for Data Science, Real-Time Intelligent Systems, Deep Reinforcement Learning, Python for ML Engineering, Causal Models for Data Science, Scalable Data Systems & AI Pipelines, and Supply Chain Optimization.

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.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Applied Generative AI: Advanced Concepts & Multimodal Modeling, Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Machine Learning with GenAI Applications, Data Science for Algorithmic Marketing, Data Visualization Techniques, Next-Gen NLP: LLM and AgenticAI in Practice, Quantitative Finance: Methods and Applications, Data Science for Healthcare, Machine Learning Operations and Inference Engineering, Optimization and Simulation Methods for Data Science, Real-Time Intelligent Systems, Deep Reinforcement Learning, Python for ML Engineering, Causal Models for Data Science, Scalable Data Systems & AI Pipelines, and Supply Chain Optimization.

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.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Applied Generative AI: Advanced Concepts & Multimodal Modeling, Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Machine Learning with GenAI Applications, Data Science for Algorithmic Marketing, Data Visualization Techniques, Next-Gen NLP: LLM and AgenticAI in Practice, Quantitative Finance: Methods and Applications, Data Science for Healthcare, Machine Learning Operations and Inference Engineering, Optimization and Simulation Methods for Data Science, Real-Time Intelligent Systems, Deep Reinforcement Learning, Python for ML Engineering, Causal Models for Data Science, Scalable Data Systems & AI Pipelines, and Supply Chain Optimization.

In this course, we will explore statistical distributions and how they help us assign probabilities to events during model training. We’ll also learn statistical methods for exploring, describing, and summarizing data. We’ll learn to use a sample to make inferences about a hypothesized population, discover and evaluate relationships between variables, and make predictions based on those relationships.

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.

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.

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.

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 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 Career Seminar (Pass/Fail) supports the development of industry professional skills, job and/or internship searches, and other in-demand areas of competency among today’s employers. Students enroll in the Career Seminar each quarter in order to engage in unique content throughout their degree program. Students with significant full-time work experience may be eligible to waive this course. 0 units, no cost.

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.

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.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Applied Generative AI: Advanced Concepts & Multimodal Modeling, Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Machine Learning with GenAI Applications, Data Science for Algorithmic Marketing, Data Visualization Techniques, Next-Gen NLP: LLM and AgenticAI in Practice, Quantitative Finance: Methods and Applications, Data Science for Healthcare, Machine Learning Operations and Inference Engineering, Optimization and Simulation Methods for Data Science, Real-Time Intelligent Systems, Deep Reinforcement Learning, Python for ML Engineering, Causal Models for Data Science, Scalable Data Systems & AI Pipelines, and Supply Chain Optimization.

The Career Seminar (Pass/Fail) supports the development of industry professional skills, job and/or internship searches, and other in-demand areas of competency among today’s employers. Students enroll in the Career Seminar each quarter in order to engage in unique content throughout their degree program. Students with significant full-time work experience may be eligible to waive this course. 0 units, no cost.

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.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Applied Generative AI: Advanced Concepts & Multimodal Modeling, Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Machine Learning with GenAI Applications, Data Science for Algorithmic Marketing, Data Visualization Techniques, Next-Gen NLP: LLM and AgenticAI in Practice, Quantitative Finance: Methods and Applications, Data Science for Healthcare, Machine Learning Operations and Inference Engineering, Optimization and Simulation Methods for Data Science, Real-Time Intelligent Systems, Deep Reinforcement Learning, Python for ML Engineering, Causal Models for Data Science, Scalable Data Systems & AI Pipelines, and Supply Chain Optimization.

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 Career Seminar (Pass/Fail) supports the development of industry professional skills, job and/or internship searches, and other in-demand areas of competency among today’s employers. Students enroll in the Career Seminar each quarter in order to engage in unique content throughout their degree program. Students with significant full-time work experience may be eligible to waive this course. 0 units, no cost.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Applied Generative AI: Advanced Concepts & Multimodal Modeling, Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Machine Learning with GenAI Applications, Data Science for Algorithmic Marketing, Data Visualization Techniques, Next-Gen NLP: LLM and AgenticAI in Practice, Quantitative Finance: Methods and Applications, Data Science for Healthcare, Machine Learning Operations and Inference Engineering, Optimization and Simulation Methods for Data Science, Real-Time Intelligent Systems, Deep Reinforcement Learning, Python for ML Engineering, Causal Models for Data Science, Scalable Data Systems & AI Pipelines, and Supply Chain Optimization.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Applied Generative AI: Advanced Concepts & Multimodal Modeling, Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Machine Learning with GenAI Applications, Data Science for Algorithmic Marketing, Data Visualization Techniques, Next-Gen NLP: LLM and AgenticAI in Practice, Quantitative Finance: Methods and Applications, Data Science for Healthcare, Machine Learning Operations and Inference Engineering, Optimization and Simulation Methods for Data Science, Real-Time Intelligent Systems, Deep Reinforcement Learning, Python for ML Engineering, Causal Models for Data Science, Scalable Data Systems & AI Pipelines, and Supply Chain Optimization.

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 Career Seminar (Pass/Fail) supports the development of industry professional skills, job and/or internship searches, and other in-demand areas of competency among today’s employers. Students enroll in the Career Seminar each quarter in order to engage in unique content throughout their degree program. Students with significant full-time work experience may be eligible to waive this course. 0 units, no cost.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Applied Generative AI: Advanced Concepts & Multimodal Modeling, Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Machine Learning with GenAI Applications, Data Science for Algorithmic Marketing, Data Visualization Techniques, Next-Gen NLP: LLM and AgenticAI in Practice, Quantitative Finance: Methods and Applications, Data Science for Healthcare, Machine Learning Operations and Inference Engineering, Optimization and Simulation Methods for Data Science, Real-Time Intelligent Systems, Deep Reinforcement Learning, Python for ML Engineering, Causal Models for Data Science, Scalable Data Systems & AI Pipelines, and Supply Chain Optimization.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Applied Generative AI: Advanced Concepts & Multimodal Modeling, Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Machine Learning with GenAI Applications, Data Science for Algorithmic Marketing, Data Visualization Techniques, Next-Gen NLP: LLM and AgenticAI in Practice, Quantitative Finance: Methods and Applications, Data Science for Healthcare, Machine Learning Operations and Inference Engineering, Optimization and Simulation Methods for Data Science, Real-Time Intelligent Systems, Deep Reinforcement Learning, Python for ML Engineering, Causal Models for Data Science, Scalable Data Systems & AI Pipelines, and Supply Chain Optimization.

The required thesis for the 2-year, thesis track program is completed over 2, 10-week quarters and results in a written thesis. Full-time, in-person students admitted to the 2-year, thesis track will complete the required thesis during 2, 100 unit courses offered winter and spring quarter of year 2.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Applied Generative AI: Advanced Concepts & Multimodal Modeling, Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Machine Learning with GenAI Applications, Data Science for Algorithmic Marketing, Data Visualization Techniques, Next-Gen NLP: LLM and AgenticAI in Practice, Quantitative Finance: Methods and Applications, Data Science for Healthcare, Machine Learning Operations and Inference Engineering, Optimization and Simulation Methods for Data Science, Real-Time Intelligent Systems, Deep Reinforcement Learning, Python for ML Engineering, Causal Models for Data Science, Scalable Data Systems & AI Pipelines, and Supply Chain Optimization.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Applied Generative AI: Advanced Concepts & Multimodal Modeling, Advanced Computer Vision with Deep Learning, Advanced Machine Learning and Artificial Intelligence, Bayesian Machine Learning with GenAI Applications, Data Science for Algorithmic Marketing, Data Visualization Techniques, Next-Gen NLP: LLM and AgenticAI in Practice, Quantitative Finance: Methods and Applications, Data Science for Healthcare, Machine Learning Operations and Inference Engineering, Optimization and Simulation Methods for Data Science, Real-Time Intelligent Systems, Deep Reinforcement Learning, Python for ML Engineering, Causal Models for Data Science, Scalable Data Systems & AI Pipelines, and Supply Chain Optimization.

The required thesis for the 2-year, thesis track program is completed over 2, 10-week quarters and results in a written thesis. Full-time, in-person students admitted to the 2-year, thesis track will complete the required thesis during 2, 100 unit courses offered winter and spring quarter of year 2.