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 Java programming, data structures, calculus, linear algebra and computer science theory, you can complete your degree in just nine months. Once admitted, you will take a placement exam to ensure you have mastery of the foundational concepts to ensure success in the program.
12 Courses (48 Credits)
- COMP 3421: Introduction to Database Management Systems
- COMP 4333: Parallel and Distributed Computing
- COMP 4431: Data Mining
- COMP 4432: Machine Learning
- COMP 4441: Introduction to Probability and Statistics for Data Science
- COMP 4442: Advanced Probability and Statistics for Data Science
- COMP 4447: Data Science Project 1
- COMP 4448: Data Science Project 2
- COMP 4449: Capstone Project in Data Science
- COMP 4581: Algorithms for Data Science
- Data Science Elective 1
- Data Science Elective 2
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, and you can complete the program in 12 months. If you plan to enroll in the bridge courses, you won’t take the placement exam until after completing this coursework.
Bridge courses (16 credits if required)
- COMP 3001: Computer Science Theory Basics
- COMP 3005: Computer Science Programming Basics - Java
- COMP 3006: Computer Science Advanced Programming - Advanced Java and Data Structures
- COMP 3007: Data Science Theory Fundamentals - Calculus and Linear Algebra
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 $1071.
At this rate, tuition for the full MS in Data Science degree in 2019-2020 is roughly $51,408-$68,544 depending on the number of bridge classes needed as determined by the admitted student’s performance on a pre-assessment prior to enrollment.
The MS in Data Science degree is not simultaneously eligible for DU’s flat tuition rate for students taking 12-18 credit hours a quarter.
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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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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