Data Science

Data Science Program Info

Program at a Glance

• Online or In-Person Tracks
• 12 Courses–Finish in 18-24 Months. 
• Industry-Aligned Courses and Projects. 
• Start Dates: Online Jan., April, June, or Sept.; or In-Person: Sept. 
• Top-Ranked University

Ready to Shape your Future in Data Science?

• Resume 

  Submit a resume via the application status page listing your current employer(s) and professional experiences. 

• Transcripts

  A regionally accredited baccalaureate degree is required for admission. You may submit an unofficial transcript with your initial application. 
   
  Upon acceptance, you will need to have one official transcript from each college or university. This includes transcripts for credit earned for transfer work and study abroad. Official electronic transcripts can be sent to gradinfo@du.edu through a secure third party. Official transcripts may also be sent via postal mail in an unopened envelope that has been sealed by the issuing university.

  Official transcripts can be sent to:


  University College
  University of Denver
  Attn: Admission
  2211 South Josephine Street
  Denver, CO 80208

• Recommendation Letters 

  Two (2) letters of recommendation are required but three (3) are preferred. Letters should be submitted by recommenders through the online application. 

• Personal Statement 

  A personal statement of at least 300 words is required. Your statement should provide a response to the following questions: 

  • What is your motivation for pursuing data science at this time and why are you interested in pursuing this study at the University of Denver? Please be specific. 

  • How has your academic training, work experience, and/or self-directed learning prepared you for graduate work in data science? 

  • What do you expect will be the most challenging aspects of participating in the data science graduate program for you personally? What preparations can you take to overcome these challenges? 

  • What do you want to accomplish in the future as a data scientist? 

• Python Prerequisite 

  Applicants must meet the program’s Python prerequisite prior to matriculation. The prerequisite can be met either through a college-level, credit-bearing, computer programming course in Python (completed within the past two years) or by taking the University of Denver’s COMP4401, Introduction to Python for Data Scientists. In either case, a grade of ‘B’ or higher is required for prerequisite approval. Applicants who meet the prerequisite with a prior Python programming class may be asked to submit the course description and/or syllabus for review and approval. 

• Additional Requirements

  If you wish to study on an F1 or J1 visa, please review the admission requirements for international applicants. 

Courses and Curriculum

The curriculum prepares students to design tools that collect, evaluate and interpret data to inform critical decisions. Through rigorous, mathematically based coursework, learners master advanced concepts for a strong foundation for professional success and self-direction. 

• Core Courses

  Python Software Development (COMP 3006)

  In this advanced Python programming course, you will design custom classes to accomplish data science tasks. You will also develop facility with Python's standard library as well as important data processing packages.

  Data Science Math I & II  (COMP 3007/3008)

  In this set of courses you will develop and refine your knowledge of calculus, linear algebra, basic probability, and discrete math to master computational processes and problems in machine learning and statistics.

  Database Organization & Management  (COMP 3421)

  This course will enable you to design, diagram, and query relational and non-relational database management systems. Particular focus will be given to SQL scripting and embedded programming.

  Machine Learning  (COMP 4432)

  In this course you will acquire industry-ready skills for predictive modeling and data mining based contemporary machine learning algorithms and techniques.

  Data Visualization (COMP 4433)

  In this course you will investigate and apply a range of visualization strategies and methods for data exploration, analysis, and communication.

  Probability & Statistics for Data Science (COMP 4441/4442)

  In this set of courses you will use the R programming language to apply inferential concepts and applications. Linking theory and practice, you will deepen your understanding of parametric and nonparametric statistics for data analysis as well as key steps in model selection, testing and evaluation. You will also apply these skills to complete an independent project based on real world data. 

  Deep Learning (COMP 4531)

  In this course you will learn how to design, train, and evaluate complex multi-layer neural networks in Python. You will also add to your data science portfolio by completing a culminating project of your own design.

  Algorithms for Data Science (COMP 4581)

  In this course you will design, analyze, and code algorithms for computational efficiency and problem-solving. Including the processing of large data sets.

• Elective Courses

  Internship (COMP 3904)

  This course allows you to receive credit toward graduation for participating in a paid data science internship. You will also receive mentoring from an an academic advisor of your choice.

  Parallel & Distributed Computing  (COMP 4334)

  This course will enable you to work with machine learning algorithms and big data at scale. Along the way, you will learn industry practices for implementing these technqiues as well as insight into the architectures that support them.

  Data Science Tools I & II  (COMP 4447/4448)

  These courses are designed to strengthen your coding and data acquisition skills as well as familiarize you with a broad range of data science methods. You will also refine skills for data-centric project development and collaboration that are prerequisites to professional practice. 

  Capstone (COMP 4449)

  In this course you will learn to design, develop, test and present 'full-cycle' data science products or services. And assess their value in relation to real world situations and needs.