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Master of Science in Data Science

Master of Science in Data Science
We are now accepting applications to the Master of Science in Data Science program. Apply Today

The MS in Data Science is designed for

  • Graduate students seeking careers in data science, including data analytics, data engineering, and machine learning.
  • Students preparing to enter and shape one of the fastest-growing, exciting, and impactful sectors of our regional and global economy.
  • Students who wish to equip themselves with a well-developed understanding of the interrelationships between technology and ethics. Technology is never value-neutral, and the workforce must be diligent to safeguard human values even as we participate in technological innovation.

Data Science 2 sq

Program Learning Outcomes

In completion of the Master of Science degree in Data Science, students will:

  1. Analyze data systems and data sets and identify steps for data preparation, descriptive analytics, data storytelling, and predictive modeling, consistent with current best practices in Data Science.
  2. Design, develop, test, and evaluate data preparation, analysis, and modeling processes and projects in relation to defined user questions and needs.
  3. Identify, analyze, and develop sound judgments related to professional, legal, and ethical problems arising in Data Science and related fields.
  4. Communicate effectively in interpersonal, team, and large group contexts.

Program Requirements

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

  • Must include at least 18 credits of graduate level DSCI courses.
  • May include as many as 12 credits of graduate level BSAD courses.

*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.

Program Curriculum

M.S. Data Science Curriculum

Pending formal approval from the Higher Learning Commission

M.S. Data Science (30 credits)

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

Fall 2023 Course Schedule

Fall 1st 8 weeks

  • BSAD 6433 Data Analysis & Visualization
  • BSAD 6443 Database Systems & SQL

Fall 2nd 8 weeks

  • BSAD 6873 Data Dashboarding & Storytelling
  • BSAD 6883 Data Warehousing & Advanced SQL

Anticipated Spring-Summer 2024 Course Schedule

Spring 1st 8 weeks

  • DSCI 6323 Descriptive Analytics & Data Integrity
  • DSCI 6313 Data Engineering

Spring 2nd 8 weeks

  • DSCI 6413 Applied Machine Learning 1
  • DSCI 6423 Data Preprocessing

Summer 1st 8 weeks

  • DSCI 6513 Applied Machine Learning 2
  • DSCI 6523 Machine Learning Operations

Course Descriptions

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)

DSCI 6123 Database Systems & SQL

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)

Contact

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.

Career paths with a Master of Science in Data Science

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Data Governance Supervisor

Student Testimonials

“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