In a world of fast-evolving technology, science and business, data science is an essential tool for organizations looking to stay ahead of the curve. If you are fascinated by analysis and you have a mind for numbers, the data science program may be perfect for you. Our MS in data science provides students with a broad course of study in programming, algorithms, statistics and data management, as well as a depth of understanding in specific fields such as data mining, machine learning and parallel systems.
Graduates of the data science program go on to work in a wide variety of careers, including business, government, education and the natural sciences. Whether you're interested in research or want to bring your data expertise to the entrepreneurial realm, you will be prepared to reach across disciplines, making sense of the past and present to improve the future.
With technology existing in a state of constant evolution, the ability to access, understand and analyze data is essential for any organization or company looking to stay ahead of the curve. In our MS in Data Science program, you’ll learn and develop comprehensive data science skills, including programming, algorithms, machine learning, data mining, parallel and distributed systems, and data management.
In addition to learning how to use existing statistical and analytical tools for evaluating and interpreting data, you'll also learn how to build new tools that facilitate the use of data in making research, policy and business decisions. Your learning will be reinforced with practical, hands-on team projects, where you'll apply your skills to real-world problems.
Graduates of the Data Science program go on to work in a wide variety of careers, including business, government, education and the natural sciences. Whether you're interested in going into research or want to bring your data expertise to the entrepreneurial realm, you will be prepared to reach across disciplines, making sense of the past and present to improve the future.
Details about the MS in Data Science
You’ll learn and develop comprehensive data science skills and knowledge, including programming, algorithms, machine learning, data mining, parallel and distributed systems, and data management. Not only will you learn how to use existing statistical and analytical tools necessary for evaluating and interpreting data, you will also learn how to build new tools that facilitate the use of data in making critical research, policy, and business decisions.
Your learning will be reinforced with practical, hands-on team projects, giving you several opportunities to apply your skills to real world problems.
12 Courses (48 Credits)
Data Science Coursework Requirements Twelve Courses – 48 Credits
- COMP 3006 Python Software Development
- COMP 3421 Introduction to Database Management Systems
- COMP 4333 Parallel and Distributed Computing for Data Science
- COMP 4431 Data Mining
- COMP 4432 Machine Learning
- COMP 4433 Data Visualization
- COMP 4441 Introduction to Probability and Statistics for Data Science
- COMP 4442 Advanced Probability and Statistics for Data Science
- COMP 4447 Data Science Tools 1
- COMP 4448 Data Science Tools 2
- COMP 4581 Algorithms for Data Science
- COMP 4449 Capstone Project in Data Science
Foundational Courses (12 credits if required)
- COMP 3005 Foundational Course I: Computer Science Programming Basics
- COMP 3007 Foundational Course II: Calculus for Data Science
- COMP 3008 Foundational Course III: Discrete Math & Linear Algebra for Data Science
These foundation courses are designed to prepare students to be successful in the core curriculum. Incoming students have the option to take a pre-assessment exam to test out of these courses, otherwise, they are required. With an impressive mix of full-time university faculty and industry experts, you’ll learn from instructors who are on the front lines of data science.
As organizations seek to build new tools that capture and make sense of tremendous amounts of data, data scientists are in high demand across all sectors and industries of the economy, commanding an average annual salary of $126,000 (Glassdoor).
With an MS in Data Science from the University of Denver, you’ll be ready to assume roles such as Data Scientist, Data Engineer, and Data Strategist. Your work will include data mining, machine learning, AI, data visualization, programming, and technical work that contributes to decision-making.
Tuition & Financial Aid
Tuition for all students in the MS in Data Science degree is reduced from full tuition rates and is calculated on a per credit hour basis.
For the 2022-2023 academic year, the cost per credit hour for the MS in Data Science is $1,122.
Total tuition for the full MS in Data Science degree is roughly $53,856-$67,320 depending on the number of foundational classes needed as determined by the admitted student’s performance on a pre-assessment prior to enrollment. Find out additional costs outside of tuition.
Federal student loans are available for domestic students.
As tuition is already reduced on a per credit hour basis for all admitted students, we do not provide additional departmental scholarships, graduate teaching or graduate research assistantships.
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FAQ's about the MS in Data Science
When does the program start? Can I begin anytime?
The on-campus MS in Data Science program starts in the fall quarter. Students can start the online MS in Data Science program at the beginning of every quarter.
Are classes offered online?
The MS in Data Science program offers both an online and on-campus option. Students enrolled in the on-campus MS in Data Science program are able to take up to three online courses and vice versa. Students interested in the online MS in Data Science program can find more information here.
When are classes held? Are there weekend classes?
The program is designed for working professionals with all classes being held after 5:00 p.m. Monday-Thursday. We do not offer classes on the weekends.
What is the average class size?
The average class size for the program is 20-25 students.
What makes the Ritchie School’s MS in Data Science program different?
First, our program is focused on the computer science approach to data science. In many programs, we teach students how to use software and statistical analysis tools to make sense of massive amounts of data. In our program, students learn how to build these tools. These skills are in high demand by companies because they are harder to learn on the job and are less common.
Second, our program trains students to think strategically about data challenges and devise innovative solutions, regardless of the context. Many programs only teach students technical skills. However, these skills will likely become obsolete as technology evolves. Our program not only prepares students with the technical fundamentals they need but also gives them the ability to think strategically about data and ask the right questions, even as the technology changes.
What kind of job opportunities will be available to me with an MS in Data Science degree?
The MS in Data Science degree would qualify most individuals for the role of data scientist across a broad range of industries. Additional titles may include business analyst, data analyst, business development manager, machine learning scientist, and software engineer.
Job duties for these roles may include data mining, programming, processing data, cleaning data, verifying data integrity, data visualization, building data analysis tools, and using machine learning algorithms and AI.
How long does it take to complete the program?
Full-time students can complete the program in 18 months. If the student needs to take the foundational courses, it will take 24 months to graduate.
How many credit hours are required to earn the MS in Data Science program?
For MS in Data Science students who require the foundational courses, the degree is 60 credit hours. For students who do not require the foundational courses, the degree is 48 credit hours.
How many credits do I need to be considered full-time? Can I enroll part-time?
Students have the option of enrolling full or part-time. Students need to take 8 credit hours per quarter to be considered full-time.
Is it possible to work while enrolled in the master’s programs?
Yes. The program is designed to accommodate working professionals. Many of our students maintain a full-time profession while enrolled in the program.
What if I did not major in Computer Science or a STEM field?
A degree in Computer Science or a STEM field is not required. Foundational courses are offered for students who do not have a technical background.
When are the foundational courses offered?
The foundational coursework will be offered in the fall and winter quarters.
Will I be required to take the pre-assessment?
The pre-assessment is only required if a student wants to test out of the foundational courses. If a student knows they need to take the foundational courses, they are not required to take the pre-assessment and may proceed immediately to enrolling in foundational coursework.
There are generally three outcomes from the pre-assessment. Admitted students may:
- Test out of all the bridge coursework entirely
- Test out of one or two of the foundational courses
- Demonstrate that they need to take all three bridge courses.
The pre-assessment has no bearing on the student's overall GPA.
Applications and Admissions
Do you require the GRE or GMAT for admission?
The GRE or GMAT are not required for the application.
Is there an application deadline?
The priority deadline for Fall 2022-2023 enrollment is May 15th, 2023. The final deadline to apply is August 15th, 2023.
Is there a minimum GPA?
The minimum undergraduate GPA for admission consideration for graduate study at the University of Denver is a cumulative 2.5 on a 4.0 scale or a 2.5 on a 4.0 scale for the last 60 semester credits or 90 quarter credits (approximately two years of work) for the baccalaureate degree.
From whom should I get letters of recommendation?
Letters of recommendation can come from an applicant's professional or academic background. For recent graduates, ask for recommendation letters from former professors who can speak to your academic abilities as well as your character. For individuals entering the program with years of work experience, letters of recommendation from professional colleagues or supervisors.
Applicants do not provide the actual letters of recommendation. Applicants will list their references' contact information and we will send the references a form to fill out.
Can I defer my admission if necessary?
Deposited students can request a one-time deferment to the following start term.
For more information about the deferment process, contact Kevin Alt at firstname.lastname@example.org.
What is the application fee?
There is a $65 application fee.
Do you grant application fee waivers?
We can grant application fee waivers to current DU alumni, veterans, and active-duty military personnel.
What if I do not have a bachelor’s degree?
Applicants must hold an earned baccalaureate from a regionally accredited college or university or the recognized equivalent from an international institution. Applicants who are in the process of completing their degree are eligible for admission so long as their bachelor’s degree is conferred prior to the start of the master’s program.
Do you require TOEFL or IELTS?
Official scores from the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS), or the Cambridge English: Advanced (CAE) assessment are required of all graduate applicants, regardless of citizenship status, whose native language is not English or who have been educated in countries where English is not the native language. We are currently accepting Duolingo as proof of English language as well. Please see DU’s English proficiency requirements for more information.
What is OPT?
Optional Practical Training (OPT) is a work benefit allowed to international students in F-1 immigration status who are enrolled in, or completing a degree program in the U.S. This employment can be used pre-completion of studies, over the annual vacation or leave term, or post-completion of studies, after the student finishes the degree. For more information on OPT, please visit DU’s International Student & Scholars Services OPT website.
Will I be eligible for OPT?
F-1 students who have been enrolled for a minimum of nine months are eligible for up to twelve (12) months of Optional Practical Training (OPT) work authorization by the U.S. Citizenship & Immigration Services (USCIS). Employment under OPT must be directly related to a student's field of study and appropriate to the level of education.
Is this program eligible for an OPT STEM extension?
Yes. Eligible F-1 students with STEM degrees who finish their program of study and participate in an initial period of regular post-completion OPT (often for 12 months) have the option to apply for an OPT STEM extension. The OPT STEM extension is a 24-month period of temporary training that directly relates to an F-1 student's program of study in an approved STEM field.
About this Course
This course will give an overview of machine learning techniques, their strengths and weaknesses, and the problems they are designed to solve. This will include the broad differences between supervised, unsupervised and reinforcement learning and associated learning problems such as classification and regression. Techniques covered, at the discretion of the instructor, may include approaches such as linear and logistic regression, neural networks, support vector machines, kNN, decision trees, random forests, Naive Bayes, EM, k-Means, and PCA. After taking the course, students will have a working knowledge of these approaches and experience applying them to learning problems. Enforced Prerequisites: COMP 2370 and COMP 2355.
Introduction to Probability and Statistics for Data Science
About this Course
The course introduces fundamentals of probability for data science. Students survey data visualization methods and summary statistics, develop models for data, and apply statistical techniques to assess the validity of the models. The techniques will include parametric and nonparametric methods for parameter estimation and hypothesis testing for a single sample mean and two sample means, for proportions, and for simple linear regression. Students will acquire sound theoretical footing for the methods where practical, and will apply them to real-world data, primarily using R. Enforced Prerequisites and Restrictions: COMP 1671, MATH 1951, MATH 1952, or Data Science Bridge Courses I-IV, or equivalent experience
Parallel and Distributed Computing
About this Course
Current techniques for effective use of parallel processing and large scale distributed systems. Programming assignments will give students experience in the use of these techniques. Specific topics will vary from year to year to incorporate recent developments. This course qualifies for the Computer Science "Advanced Programming" requirement. Prerequisites: COMP2370 and COMP2355, or equivalent.
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Fall 2023 Final Deadline