Courses

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. 

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Prerequisites

Python Prerequisite

Prior to starting the Master's in Data Science at the University of Denver, it's essential to meet the Python prerequisite to establish a strong foundation for the curriculum. Proficiency in Python not only sets the stage for success in your courses but also facilitates professional achievement after graduation.

View Requirements
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Required 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 facilities 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. 

  • 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.

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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.

  • 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.

  • 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 techniques 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.