We are now accepting applications to the Master of Science in Data Science program. Apply Today
In completion of the Master of Science degree in Data Science, students will:
This degree is designed to include stackable micro-credentials amenable to modularized customization to meet the specific needs of students and employers.
Total credits required: 30 credits
*No GRE exam required
The MS in Data Science program is expected to start in Fall 2023, pending final approval by the Higher Learning Commission. Thank you for your interest in the program.
M.S. Data Science Curriculum
Pending formal approval from the Higher Learning Commission
|
Prefix & Number |
Course Name |
Cr |
|
BSAD 6433 |
Data Analysis & Visualization |
3 |
|
BSAD 6434 |
Database Systems & SQL |
3 |
|
BSAD 6873 |
Data Dashboarding & Storytelling |
3 |
|
BSAD 6883 |
Data Warehousing & Advanced SQL |
3 |
|
DSCI 6313 |
Data Engineering |
3 |
|
DSCI 6323 |
Descriptive Analytics & Data Integrity |
3 |
|
DSCI 6413 |
Applied Machine Learning 1 |
3 |
|
DSCI 6423 |
Data Preprocessing |
3 |
|
DSCI 6513 |
Applied Machine Learning 2 |
3 |
|
DSCI 6523 |
Machine Learning Operations |
3 |
DSCI 6113 Data Analysis & Visualization
An introduction to current best practices in data analysis, with a focus on descriptive analytics and data visualization. Students will develop proficiency designing and developing interactive digital dashboards using software such as Tableau Desktop, Microsoft Power BI, etc. (3 credits)
A graduate-level survey of current database technologies with hands-on experience designing, managing, and querying databases. Includes training in SQL for composing queries. (3 credits)
DSCI 6213 Data Dashboarding & Storytelling
An advanced study of best practices in planning, designing, and producing information-rich data dashboards, combined with study and practice communicating and presenting the meaning and implications of analysis to data stakeholders. (3 credits)
DSCI 6223 Data Warehousing
A study of data warehousing concepts and current best practices, including on-premises and cloud solutions, snowflake and star schemas, relational and NoSQL database structures, Extract-Transform-and-Load (ETL) processes, and intermediate to advanced SQL skills. (3 credits)
DSCI 6313 Data Engineering
A hands-on experience planning and developing data pipelines with industry standard software. Includes practice designing and implementing an ETL process, extracting data from transactional databases, transforming data to prepare it for analysis, and then loading the data into a data warehouse environment. (3 credits)
DSCI 6323 Descriptive Analytics & Data Integrity
A hands-on experience conducting Exploratory Data Analysis using Python. Includes current best practices in data science, including inspection of data integrity, documentation of findings, and common data remediation steps. (3 credits)
DSCI 6413 Applied Machine Learning 1
A study of and hands-on experience with current best practices in the development of machine learning models and processes essential to the development of predictive analytics workflows. Includes a study of and experience working with industry-leading statistical analyses and machine learning algorithms from linear and logistic regression to decision trees, random forests, neural networks, boosting algorithms, and ensembles. (3 credits)
DSCI 6423 Data Preprocessing
Advanced hands-on exploration and experience in developing, testing, refining, and automating the preprocessing steps necessary to prepare data efficiently and effectively for predictive modeling. (3 credits)
DSCI 6513 Applied Machine Learning 2
Further study of and hands-on experience in the development of highly performative machine learning models and processes in accordance with current industry best practices. (3 credits)
DSCI 6523 Machine Learning Operations
A study of current best practice in the deployment and maintenance of data models in the context of a software system. This course does not require extensive programming experience. Rather, it will provide a thorough introduction to the interrelationships between software development and machine learning. Those with previous programming experience will leave this course better prepared to work in MLOps roles. Those with little previous programming experience will leave ready to be knowledgeable contributors to the design and maintenance of MLOps processes. (3 credits)
David Cochran, Dean of the School of Business & Technology
[email protected] | 316-942-4291, ext. 2255
Our faculty and staff have the knowledge and experience to help you prepare for a career in Data Science. If you need clarification on a class assignment or advice on how to approach your future, you can feel confident that our faculty is available and ready to help.
“This program does an amazing job of being flexible with a student who is working full-time (with a family).”:
David (Bradley) Hart, current MBA Data Analytics Student
“I have skills now that I can directly and already apply to my job — that is really valuable!”
Nancy Iverson, current MBA Data Analytics Student