Course Progressions
Pursue Your Education on Your Own Terms
We prepare you to advance in the competitive landscape of data science career paths with a focus on industry applications. Full- and Part-time options are available in both the Online and In-Person formats. Please note: Courses offerings are subject to change.
If you are a student in a full time program, you are expected to take 300 units per quarter (~3 classes). If you are in a part time program, you are expected to take 200 units per quarter (~2 classes) to remain on track for graduation.
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In Person (Full Time)
Prequarter • 5 WeeksFoundational
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Introduction to Statistical Concepts
Pass/Fail
If you are required to take this course it will be held the 5 weeks leading up to the start of your first quarter. This course provides general exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced courses in the program.
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R for Data Science
Pass/Fail
If you are required to take this course it will be held 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.
Quarter 1 • 10 WeeksFoundational-
Python for Data Science
Pass/Fail
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.
-
Advanced Linear Algebra for Machine Learning
Pass/Fail
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.
Core-
Statistical Analysis
Letter Grade
This course provides a comprehensive and practical introduction to statistical data analysis. The statistical techniques taught in this course will enable students to analyze complex datasets and formulate and solve real- world problems to facilitate data-driven decisions.
Core-
Leadership and Consulting for Data Science
Letter Grade
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.
Core (Choose 1)-
Data Engineering Platforms for Analytics or Big Data and Cloud Computing
Letter Grade
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.
Quarter 2 • 10 WeeksCore-
Data Mining Principles
Letter Grade
Drawing on statistics of collecting and analyzing data, and machine learning algorithms that learn from experiences, data mining is a process of applying statistics and machine learning algorithms to discover patterns and rules that can generate business values.
Core-
Linear and Nonlinear Models for Business Application
Letter Grade
This course concentrates on the following topics: Review of statistical inference based on linear model, extension to the linear model by removing the assumption of Gaussian distribution for the output (Generalized Linear Model), extension to the linear model by allowing a correlation structure for the model residuals (mixed effect models), and extension of the linear model by relaxing the requirement that inputs are combined linearly (nonparametric regression, regime switches).
Elective-
Elective 1
Letter Grade
Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.
Quarter 3 • 10 WeeksCore-
Machine Learning and Predictive Analytics
Letter Grade
This course in advanced data mining will provide a practical, hands-on set of lectures surrounding modern predictive analytics and machine learning algorithms and techniques.
-
Time Series Analysis and Forecasting
Letter Grade
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.
Capstone-
Data Science Capstone Project
Letter Grade
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.
Quarter 4 • 10 WeeksCapstone-
Data Science Capstone Project
Letter Grade
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-
Elective 2
Letter Grade
Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.
-
Elective 3
Letter Grade
Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.
-
Introduction to Statistical Concepts
Pass/Fail
-
In Person (Part Time)
Prequarter • 5 WeeksFoundational
-
Introduction to Statistical Concepts
Pass/Fail
If you are required to take this course it will be held the 5 weeks leading up to the start of your first quarter. This course provides general exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced courses in the program.
-
R for Data Science
Pass/Fail
If you are required to take this course it will be held 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.
Quarter 1 • 10 WeeksFoundational-
Python for Data Science
Pass/Fail
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.
-
Advanced Linear Algebra for Machine Learning
Pass/Fail
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.
Core-
Statistical Analysis
Letter Grade
This course provides a comprehensive and practical introduction to statistical data analysis. The statistical techniques taught in this course will enable students to analyze complex datasets and formulate and solve real- world problems to facilitate data-driven decisions.
Core (Choose 1)-
Leadership and Consulting for Data Science
Letter Grade
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.
-
Data Engineering Platforms for Analytics or Big Data and Cloud Computing
Letter Grade
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.
Quarter 2 • 10 WeeksCore-
Data Mining Principles
Letter Grade
Drawing on statistics of collecting and analyzing data, and machine learning algorithms that learn from experiences, data mining is a process of applying statistics and machine learning algorithms to discover patterns and rules that can generate business values.
Core-
Linear and Nonlinear Models for Business Application
Letter Grade
This course concentrates on the following topics: Review of statistical inference based on linear model, extension to the linear model by removing the assumption of Gaussian distribution for the output (Generalized Linear Model), extension to the linear model by allowing a correlation structure for the model residuals (mixed effect models), and extension of the linear model by relaxing the requirement that inputs are combined linearly (nonparametric regression, regime switches).
Quarter 3 • 10 WeeksCore-
Machine Learning and Predictive Analytics
Letter Grade
This course in advanced data mining will provide a practical, hands-on set of lectures surrounding modern predictive analytics and machine learning algorithms and techniques.
Core (Choose 1)-
Data Engineering Platforms for Analytics or Big Data and Cloud Computing
Letter Grade
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.
-
Leadership and Consulting for Data Science
Letter Grade
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.
Quarter 4 • 10 WeeksCore-
Time Series Analysis and Forecasting
Letter Grade
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-
Elective 1
Letter Grade
Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.
Quarter 5 • 10 WeeksElective-
Elective 2
Letter Grade
Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.
Capstone-
Data Science Capstone Project
Letter Grade
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.
Quarter 6 • 10 WeeksElective-
Elective 3
Letter Grade
Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.
Capstone-
Data Science Capstone Project
Letter Grade
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.
-
Introduction to Statistical Concepts
Pass/Fail
-
Online (Full Time)
Prequarter • 5 WeeksFoundational
-
Introduction to Statistical Concepts
Pass/Fail
If you are required to take this course it will be held the 5 weeks leading up to the start of your first quarter. This course provides general exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced courses in the program.
-
R for Data Science
Pass/Fail
If you are required to take this course it will be held 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.
Quarter 1 • 10 WeeksFoundational-
Python for Data Science
Pass/Fail
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.
-
Advanced Linear Algebra for Machine Learning
Pass/Fail
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.
Core-
Statistical Analysis
Letter Grade
This course provides a comprehensive and practical introduction to statistical data analysis. The statistical techniques taught in this course will enable students to analyze complex datasets and formulate and solve real- world problems to facilitate data-driven decisions.
Core-
Leadership and Consulting for Data Science
Letter Grade
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.
Core (Choose 1)-
Data Engineering Platforms for Analytics or Big Data and Cloud Computing
Letter Grade
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.
Quarter 2 • 10 WeeksCore-
Data Mining Principles
Letter Grade
Drawing on statistics of collecting and analyzing data, and machine learning algorithms that learn from experiences, data mining is a process of applying statistics and machine learning algorithms to discover patterns and rules that can generate business values.
Core-
Linear and Nonlinear Models for Business Application
Letter Grade
This course concentrates on the following topics: Review of statistical inference based on linear model, extension to the linear model by removing the assumption of Gaussian distribution for the output (Generalized Linear Model), extension to the linear model by allowing a correlation structure for the model residuals (mixed effect models), and extension of the linear model by relaxing the requirement that inputs are combined linearly (nonparametric regression, regime switches).
Elective-
Elective 1
Letter Grade
Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.
Quarter 3 • 10 WeeksCore-
Machine Learning and Predictive Analytics
Letter Grade
This course in advanced data mining will provide a practical, hands-on set of lectures surrounding modern predictive analytics and machine learning algorithms and techniques.
Elective-
Elective 2
Letter Grade
Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.
Capstone-
Data Science Capstone Project
Letter Grade
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.
Quarter 4 • 10 WeeksCore-
Time Series Analysis and Forecasting
Letter Grade
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-
Elective 3
Letter Grade
Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.
Capstone-
Data Science Capstone Project
Letter Grade
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.
-
Introduction to Statistical Concepts
Pass/Fail
-
Online (Part Time)
Prequarter • 5 WeeksFoundational
-
Introduction to Statistical Concepts
Pass/Fail
If you are required to take this course it will be held the 5 weeks leading up to the start of your first quarter. This course provides general exposure to basic statistical concepts that are necessary for students to understand the content presented in more advanced courses in the program.
-
R for Data Science
Pass/Fail
If you are required to take this course it will be held 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.
Quarter 1 • 10 WeeksFoundational-
Python for Data Science
Pass/Fail
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.
-
Advanced Linear Algebra for Machine Learning
Pass/Fail
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.
Core-
Statistical Analysis
Letter Grade
This course provides a comprehensive and practical introduction to statistical data analysis. The statistical techniques taught in this course will enable students to analyze complex datasets and formulate and solve real- world problems to facilitate data-driven decisions.
Core (Choose 1)-
Leadership and Consulting for Data Science
Letter Grade
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.
-
Data Engineering Platforms for Analytics or Big Data and Cloud Computing
Letter Grade
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.
Quarter 2 • 10 WeeksCore-
Data Mining Principles
Letter Grade
Drawing on statistics of collecting and analyzing data, and machine learning algorithms that learn from experiences, data mining is a process of applying statistics and machine learning algorithms to discover patterns and rules that can generate business values.
Core-
Linear and Nonlinear Models for Business Application
Letter Grade
This course concentrates on the following topics: Review of statistical inference based on linear model, extension to the linear model by removing the assumption of Gaussian distribution for the output (Generalized Linear Model), extension to the linear model by allowing a correlation structure for the model residuals (mixed effect models), and extension of the linear model by relaxing the requirement that inputs are combined linearly (nonparametric regression, regime switches).
Quarter 3 • 10 WeeksCore-
Machine Learning and Predictive Analytics
Letter Grade
This course in advanced data mining will provide a practical, hands-on set of lectures surrounding modern predictive analytics and machine learning algorithms and techniques.
Core (Choose 1)-
Leadership and Consulting for Data Science
Letter Grade
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.
-
Data Engineering Platforms for Analytics or Big Data and Cloud Computing
Letter Grade
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.
Quarter 4 • 10 WeeksCore-
Time Series Analysis and Forecasting
Letter Grade
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-
Elective 1
Letter Grade
Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.
Quarter 5 • 10 WeeksElective-
Elective 2
Letter Grade
Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.
Capstone-
Data Science Capstone Project
Letter Grade
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.
Quarter 6 • 10 WeeksCapstone-
Data Science Capstone Project
Letter Grade
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-
Elective 3
Letter Grade
Elective offerings vary. Students will work with their academic advisor to select electives based on their interests and course availability. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.
-
Introduction to Statistical Concepts
Pass/Fail