Course Progressions

This course is held in the 5 weeks leading up to the start of your first quarter and provides general exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced courses in the program. 0 units, no cost.

This course is held in the 5 weeks leading up to the start of your first quarter and is an introduction to the essential concepts and techniques for the statistical computing language R. 0 units, no cost.

This course is held concurrently with the first five weeks of your first quarter in the program and starts with an introduction to the Python programming language basic syntax and environment. 0 units, no cost.

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. 0 units, no cost.

In a traditional linear model, the observed response follows a normal distribution, and the expected response value is a linear combination of the predictors.  Since Carl Friedrich Gauss (1777-1855) and Adrien-Marie Legendre (1752-1833) created this linear model framework in the early 1800s, the “Linear Normal” assumption has been the norm in statistics/data science for almost two centuries.  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.

Data Engineering Platforms teaches effective data engineering—an essential first step in building an analytics-driven competitive advantage in the market. Big Data and Cloud Computing teaches students how to approach big data and large-scale machine learning applications. There is no single definition of big data and multiple emerging software packages exist to work with it, and we will cover the most popular approaches.

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.

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.

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.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Generative AI Principles, 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, Digital Marketing Analytics in Theory and Practice, Financial Analytics, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, 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: Generative AI Principles, 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, Digital Marketing Analytics in Theory and Practice, Quantitative Finance, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, 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: Generative AI Principles, 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, Digital Marketing Analytics in Theory and Practice, Quantitative Finance, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, 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: Generative AI Principles, 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, Digital Marketing Analytics in Theory and Practice, Quantitative Finance, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, Supply Chain Optimization.

This course is held in the 5 weeks leading up to the start of your first quarter and provides general exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced courses in the program. 0 units, no cost.

This course is held in the 5 weeks leading up to the start of your first quarter and is an introduction to the essential concepts and techniques for the statistical computing language R. 0 units, no cost.

This course is held concurrently with the first five weeks of your first quarter in the program and starts with an introduction to the Python programming language basic syntax and environment. 0 units, no cost.

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. 0 units, no cost.

In a traditional linear model, the observed response follows a normal distribution, and the expected response value is a linear combination of the predictors.  Since Carl Friedrich Gauss (1777-1855) and Adrien-Marie Legendre (1752-1833) created this linear model framework in the early 1800s, the “Linear Normal” assumption has been the norm in statistics/data science for almost two centuries.  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.

Data Engineering Platforms teaches effective data engineering—an essential first step in building an analytics-driven competitive advantage in the market. Big Data and Cloud Computing teaches students how to approach big data and large-scale machine learning applications. There is no single definition of big data and multiple emerging software packages exist to work with it, and we will cover the most popular approaches.

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 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.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Generative AI Principles, 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, Digital Marketing Analytics in Theory and Practice, Financial Analytics, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, 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.

Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. Past electives include: Generative AI Principles, 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, Digital Marketing Analytics in Theory and Practice, Quantitative Finance, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, 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: Generative AI Principles, 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, Digital Marketing Analytics in Theory and Practice, Quantitative Finance, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, 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: Generative AI Principles, 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, Digital Marketing Analytics in Theory and Practice, Quantitative Finance, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, 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: Generative AI Principles, 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, Digital Marketing Analytics in Theory and Practice, Quantitative Finance, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, 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: Generative AI Principles, 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, Digital Marketing Analytics in Theory and Practice, Quantitative Finance, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, 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.

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: Generative AI Principles, 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, Digital Marketing Analytics in Theory and Practice, Quantitative Finance, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, 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: Generative AI Principles, 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, Digital Marketing Analytics in Theory and Practice, Quantitative Finance, Health Analytics, Machine Learning Operations, Natural Language Processing and Cognitive Computing, Real Time Intelligent Systems, Reinforcement Learning, 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.

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.