MS in Data Science

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MS in Data Science

Degree Overview

Organizations in all industries are collecting vast amounts of data about their customers, operations, and financial performance. However, leaders of these organizations struggle with how to make that data useful so informed decisions can be made. They are seeking people with the right expertise to guide them.

Bellevue University’s online Master of Science in Data Science degree is designed to prepare you to be in the driver’s seat to curate, analyze, and make relevant discoveries from large data sets. You’ll learn the tools, methodologies, and systems needed to solve complex problems in almost any field. No data science or computing experience is required but it would be helpful to have some background in math, computing, and statistics.

The online Data Science graduate program brings together business analytics, visualization, computer science, and statistics to address the entire process of data science. You’ll apply your skills by gathering, preparing, analyzing and presenting data to business leaders and/or clients. While many programs emphasize programming or statistics, ours strikes a balance between the two in order to prepare you for this ever-changing field.

Program Outcomes

At completion of the online Master’s in Data Science, graduates will be able to:

Prepare datasets for solving problems.
Recommend appropriate data modeling techniques to test hypotheses.
Communicate data science results into answers for domain challenges.
Identify ethical considerations in dataset preparation and modeling.

Degree Information

The Master of Science in Data Science is an interdisciplinary program designed for students who want to apply the data science discipline to solve problems and answer critical questions in their industry domain. The program consists of courses in the foundations of data science, data preparation, data modelling, and applied data science. The program culminates with a capstone which will allow the student to apply the entire suite of data science tools to answer questions and enable informed decision-making.

Curriculum

Major Requirements (30 credit hours)

This course introduces the possibilities, history, and ethics surrounding Data Science. Basics of data science are explored, including vocabulary, programming languages, big data frameworks, visualization, and statistics. Prior programming experience is not needed for this course.
This course introduces the Python programming language as a tool to clean, slice, and build tools to analyze an existing dataset. Basic principles of programming are explored as well as techniques for configuring a computer for data science work. Prerequisite: Recommend DSC 500
The R programming language and software environment is commonly used for to explore all types of data. Using R, students perform statistical tests on the data. Report writing and presentation of data are introduced. Prerequisite: Recommend DSC 500
This course introduces complex techniques needed for profiling and exploring data. Students use programming and statistics-based inference to ask and answer insightful questions of data. Prerequisite: Recommend DSC 510 and DSC 520
Much like life, the data humans produce is infinitely variable in its structure, presentation, and scale. This course prepares students for this infinite variety of data. Students use Python, SQL, and other tools to acquire, prepare, clean, and automate dataset creation. Prerequisite: DSC 510 or equivalent and recommend DSC 530
Data can often contain patterns and anomalies that only emerge at large scale. In this course, students explore techniques to mine and analyze large datasets to discover useful knowledge. Text mining, unstructured data, social networks, and other types of unsupervised data mining methods for data science are included. Prerequisite: Recommend DSC 540
This course assembles topics covered in previous courses into an applied project. Students have the opportunity to find, clean, analyze, and report on a project they define. Advanced methods of analysis using Python and R allow students to delve deeper into their projects. Prerequisite: DSC 540 or equivalent and recommend DSC 550
Data scientists should be great storytellers, whether using visual, text, or other means. In this course, students explore the basic storytelling components of data science and apply them to different types of data for different types of clients and audiences. Presentation techniques, language use for different audiences, and visualization tools techniques are included. Prerequisite: Recommend DSC 630
This course covers the fundamentals of data infrastructure and how technologies fit together to form a process, or pipeline, to refine data into usable datasets. This course focuses on building a predictive modeling pipeline used by the various types of projects that are called, “big data.” Prerequisite: Recommend DSC 540
In the final course of the Data Science program, students will conduct several data science projects from origin-to-presentation. Students will gather data, then prepare, clean, analyze, and present their analysis to an audience. Prerequisite: Completion of all other required DSC courses

Students applying for professional license or certification should verify the University’s offerings meet the requirements with the professional organization.