Course Progressions

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.

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.

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.

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.

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.

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 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.

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.

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).

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.

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.

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. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.

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.

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. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.

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.

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.

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.

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.

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.

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.

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 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.

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.

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 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.

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 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.

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.

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. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.

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.

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.

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.

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.

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.

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.

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 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.

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.

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).

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.

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 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.

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. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.

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.

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 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. A list of sample electives (subject to change) appear at the bottom of the In-Person and Online Program pages.