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. Over the course of your studies, you'll develop a broad base of knowledge with the opportunity to specialize in an area of particular interest.
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 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.
A previous background in computer science is not required to apply.
If you have previous coursework in Python programming, data structures, calculus, linear algebra, and computer science theory you will take a placement exam once admitted. This exam is to certify that you have mastery of the foundational concepts and ensure success in the program.
11 Courses (44 Credits)
Data Science Coursework Requirements Eleven Courses – 44 Credits
- COMP 3006 Python Software Development
- COMP 3421 Introduction to Database Management Systems
- COMP 4333 Parallel and Distributed Computing
- 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
Data Science Development Coursework Requirements & Options - 4 Credits from a combination of the following:
- COMP 4449 Capstone Project* 4 credits
- COMP 4991 Independent Study 1-8 credits
*Capstone Project – Require
Choose from a variety of electives, including Computer Forensics, Bayesian Analysis, Introduction to Artificial Intelligence, and Programming Languages.
If you do not have previous coursework in these subjects, you’ll take four bridge courses that will serve as a foundation for your data science courses. If you plan to enroll in the bridge courses, you won’t take the placement exam until after completing this coursework.
Bridge courses (12 credits if required)
- COMP 3005 Bridge Course I: Python Programming I
- COMP 3007 Bridge Course III: Calculus for Data Science
- COMP 3008 Bridge Course IV: Discrete Math & Linear Algebra for Data Science
With an impressive mix of full-time university faculty, 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 starting salary of $86,000 with an average overall salary of $129,000.
With an MS in Data Science from the University of Denver, you’ll be ready to assume roles such as Data Scientist, Business Analyst, Software Engineer, or Business Intelligence Director. Your work will include data mining, processing, 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 2019-2020 academic year, the cost per credit hour for the MS in Data Science will be $1042.
At this rate, tuition for the full MS in Data Science degree in 2019-2020 is roughly $50,016-$66,688 depending on the number of bridge classes needed as determined by the admitted student’s performance on a pre-assessment prior to enrollment.
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 scholarships, assistantships or financial assistance.
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FAQ's about the MS in Data Science
When does the program start? Can I begin anytime?
The on ground MS in Data Science only starts in the Fall quarter. The courses are sequenced in such a way that it is only possible to begin the on ground program in the fall. The online MS in Data Science program starts every quarter.
Are classes offered online?
The MS in Data Science program has both an online and on ground option. Students enrolled in the on ground MS in Data Science program are able to take up to three courses online 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 between 5-9 PM on weeknights. There are no classes held on the weekend.
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, you are taught how to use software and statistical analysis tools to make sense of massive amounts of data. In our program, you actually 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 you to think strategically about data challenges and devise innovative solutions, regardless of the context. Many programs will teach students technical skills only. However, these skills will likely become obsolete as technology evolves. Our program not only prepares you with the technical fundamentals you need, but also 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?
A 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, analytics director, business development manager, business intelligence analyst and software engineer.
Job duties for these roles may include data mining, processing data, cleaning data, verifying data integrity, data visualization, building data analysis tools, using machine learning algorithms, and programming.
How long does it take to complete the program?
Full-time students can complete the programs in 18-24 months depending on the bridge courses needed.
How many credit hours are required to earn the MS in Data Science program?
For MS in Data Science students who require the bridge courses it is 60 credit hours. For students who do not require the bridge 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. Full-time enrollment status in a graduate program is achieved by taking a minimum of 8 credit hours per quarter.
Is it possible to work while enrolled in the master’s programs?
Yes. It is recommended that students take no more than 8 credit hours per quarter if they intend to continue working. Upon accepting an offer of admission, the student should discuss plans of study with their faculty advisor.
What if I did not major in Computer Science or a STEM field?
A degree in Computer Science is not required. However, candidates that do not have a background in computer science will be required to complete bridge courses providing foundational computer science coursework OR pass a pre-assessment exam before continuing to the 48 credit hours of master’s coursework.
When are the bridge courses offered?
The bridge coursework will be offered in in the fall and winter quarters for the on ground program. Bridge coursework is offered every quarter for the online program. All incoming students are either required to test out of the bridge coursework or pass these courses with at least a 3.0 GPA.
Will I be required to take the pre-assessment?
If a student feels they have the background necessary to test out of the bridge classes, they may opt to take the pre-assessment exam. There are generally three outcomes from the pre-assessment. Admitted students may 1) test out of the bridge coursework entirely, 2) test out of one to two of the four bridge courses, or 3) demonstrate that they need all require all four of the bridge courses.
If a student knows they need the bridge courses, they are not required to take the pre-assessment and may proceed immediately to enrolling in bridge coursework.
Applications and Admissions
Do you require the GRE or GMAT for admission?
Proof of quantitative ability is required as part of the application process. The following can be submitted as evidence of quantitative ability: GRE scores, GMAT scores with a request for GRE waiver, or a GRE Waiver Request.
Is there a minimum GRE or GMAT Score for admission?
On the GRE competitive applicants earn a score of at least 156 on the quantitative section and at least a 2 on the analytical writing section.
How do I know if I would qualify for a GRE waiver?
The MS in Data Science program will only consider GRE waivers on a case-by-case basis once all other application requirements are satisfied. Faculty reviewing an application must be confident in an applicant’s potential for success in a program based on past academic performance and thus must consider an application holistically before evaluating waiver requests. Students interested in a GRE waiver should email email@example.com for more information about the GRE waiver requirements.
Is there an application deadline?
The application deadline for Fall 2020-2021 is August 15th, 2020. Late applications will be considered on a case-by-case basis, provided space is still available.
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. An earned master’s degree or higher from a regionally accredited institution supersedes the minimum standards for the baccalaureate. For applicants with graduate coursework but who have not earned a master’s degree or higher, the GPA from the graduate work may be used to meet the requirement. The minimum GPA is a cumulative 3.0 on a 4.0 scale for all graduate coursework undertaken.
Students with lower GPAs will need to demonstrate quantitative ability and success elsewhere in their applications to be admitted.
From whom should I get letters of recommendation?
It is best to obtain letters of recommendation from professors or professionals who know you well. For recent graduates, ask for recommendation letters from former professors that 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 professionals who have worked with. Applicants do not provide the actual letters of recommendation, but list their references contact information and we send the references a form to fill out.
Can I defer my admission if necessary?
Deposited students can request a one-time change deferment to the following start term of his/her intended enrollment for up to one academic year. Contact the Kevin Alt at firstname.lastname@example.org for more information about changing your admissions term.
What is the application fee?
The application fee is $65.
Do you grant application fee waivers?
We can grant application fee waivers to current DU alumni, veterans and active duty military.
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.
Tuition and Financial Aid
What is the cost of tuition?
The current graduate credit costs can be found at the can be found at the following link: Cost of Attendance.
Will I need to pay out-of-state tuition, or how do I get in-state tuition?
The University of Denver is a private institution so tuition is the same for both in-state and out-of-state students.
Do you have graduate teaching, research assistantships or additional scholarships?
The MS in Data Science students are not eligible for assistantships. As tuition is already substantially reduced for all students in the program, additional scholarships and assistantships are not available.
Is financial aid available?
In order to apply for financial aid at the University of Denver, and be considered for federal grants and scholarships, you must have a current Free Application for Federal Student Aid (FAFSA) form on file. Federal financial aid is available to qualified students. For information on financial aid, visit the Office of Financial Aid.
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.
Do you offer provisional acceptance to applicants who have not achieved the minimum TOEFL or IELTS score?
In cases where minimum TOEFL/IELTS scores were not achieved or no English proficiency test was taken, we may offer English Conditional Admission (ECA) to academically qualified non-native English speakers. Such applicants must enroll in DU’s English Language Center to meet the English language requirement prior to beginning the MS in Data Science curriculum. English language training at centers outside of DU will not be counted toward meeting English language proficiency requirements. International applicants with a three-year baccalaureate degree or any other academic deficiencies cannot be granted English Conditional Admission.
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.
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 directl
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 a STEM OPT extension. The STEM OPT 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. On May 10, 2016, this extension effectively replaced the previous 17-month STEM OPT extension.
About this Course
Data Mining is the process of extracting useful information implicitly hidden in large databases. Various techniques from statistics and artificial intelligence are used here to discover hidden patterns in massive collections of data. This course is an introduction to these techniques and their underlying mathematical principles. Topics covered include: basic data analysis, frequent pattern mining, clustering, classification, and model assessment.
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 2020 Priority Deadline