MS in Data Science - Online Degree

Joint Degree between Department of Computer Science and Department of Statistics

Our master’s degree program is designed to equip you with advanced skills for success in today's data-driven world. This strategic program seamlessly blends statistics and computer science, providing a deep understanding of data analysis, predictive modeling, and machine learning techniques while establishing a robust foundation in statistical methodologies and computational proficiency. Navigate data collection, interpretation, and cleaning intricacies, simultaneously mastering programming languages and tools crucial for efficient data manipulation and analysis. Through a specialized focus on statistics, you'll gain the expertise to extract valuable insights from complex datasets, make informed data-driven decisions, and expertly communicate findings to diverse audiences. By harmoniously merging statistics and computer science, this program empowers you to tackle real-world challenges using the power of data. Whether your ambitions gravitate towards data science, machine learning, or business analysis, our flexible master's degree, enriched by the guidance of experienced faculty from two departments, opens doors to opportunities in the dynamic landscape of data science.

Moreover, in the age of big data, the role of a data scientist exceeds conventional analytics, demanding proficiency across the entire data science lifecycle. In addition to analytical skills, it is crucial to master the capacity for crafting relevant questions, consolidating data from various sources, integrating insights, transforming raw information into actionable solutions, and effectively communicating discoveries to facilitate impactful decision-making. As the industry landscape evolves, the escalating demand for this comprehensive proficiency underscores the need for an innovative M.S. program. This initiative cultivates upcoming data scientists, equipping them with indispensable skills to not only fulfill but surpass these rapidly growing expectations.

https://www.ndsu.edu/data_science/


Curriculum

Total number of credits: 33

CORE COURSES:

  • STAT 711 Basic Computational Stats using R (Spring Odd Years, and Summer)
  • STAT 713 Introduction to Data Science (Spring Even Years, and Summer)
  • STAT 725 Applied Statistics (Fall, Spring, Summer)
  • STAT 726 Applied Regression and Analysis of Variance (Fall, Spring, Summer) 
  • CSCI 622 Fundamentals of Data Engineering (Fall)
  • DATA 720 Programming for Data Science (Fall)
  • DATA 761 Applied Machine Learning (Spring) OR STAT 712 Applied Statistical Machine Learning (Spring and Summer) 
  • DATA 765 Applied Database Systems
  • CSCI 689 Social Implications of Computers (Spring 2025); from Spring 2026 onwards CSCI 770 Data Science Ethics

Electives: (6 credits)

Choose 2 from:

  • STAT 660 Applied Survey Sampling (Fall odd years)
  • STAT 662 Introduction to Experimental Design (Spring starting 2026)
  • STAT 714 Visualization and data storytelling (Fall odd years, and Summer)
  • CSCI 650 Cloud Computing (Fall)
  • CSCI 706 Data-Driven Security (Spring)
  • DATA 760 Applied AI (Spring)
Learning Outcomes 

Program completers will:  

1. Ingest, transform, and serve data. 

  • Programmatically ingest data from databases, streaming devices, and other sources using appropriate data management techniques. 
  • Use software tools to detect and fix anomalies as well as properly address missing data. 
  • Model and store transformed data for use in data science solutions. 

2. Visualize and communicate data.

  • Design new metrics and proficiently use data visualization tools to enable new insights and drive better decision making. 
  • Apply strong data communication skills to cross functional situations, including data storytelling, written communications, and data interpretation. 

3. Develop predictive and prescriptive solutions.

  • Analyze use cases and recommend the appropriate predictive or prescriptive solution, including new machine learning models, pre-trained models, reinforcement learning, and other approaches. 
  • Develop solutions consistent with quantitative and statistical analysis expectations, including an understanding of regression, hypothesis testing, clustering analysis, and network analysis. 
  • Design and develop optimal machine learning models for the entire data science lifecycle from problem identification through deployment. 

4. Build trustworthy solutions.

  • Address ethical issues in data science solutions, including concerns such as bias, 
  • Address important factors for human interactions with data science solutions. 
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