Data Science, BS

This is the first version of the 2024–25 General Catalog. Please check back regularly for changes. The final edition and the historical PDF will be published during the fall semester.

Learning Outcomes

Data Curation Skills

Graduates will be able to:

  • understand issues associated with data collection, management, provenance, storage, merging, sharing, and preparation;
  • work with multiple-source, multiple-format data;
  • investigate the quality of the data; and
  • have a basic understanding of ethical and confidentiality issues associated with data collection, storage, merging, and sharing.

Computational Skills

Graduates will be able to:

  • use critical thinking skills to translate substantive questions into well-defined computational problems and choose appropriate computational techniques for a given problem;
  • understand the foundational software skills and associated algorithmic and computational problem-solving methods used in computer science;
  • be proficient in computational methods for collecting, managing, storing, preparing, sharing, and describing data numerically and graphically from a variety of sources to design and carry out basic simulation studies; and
  • use professional statistical software and understand the principles of programming and algorithmic problem-solving that underlie these packages.

Statistical/Probabilistic Skills

Graduates will be able to:

  • use critical thinking skills to translate substantive questions into well-defined statistical or probability problems and choose the appropriate graphical or numerical descriptive and/or inferential statistical techniques for a given problem;
  • understand the importance of, and issues related to, the choice of the study design, such as designed experiment versus probability sample versus convenience sample, used to produce data; 
  • understand that uncertainty, variability, and randomness play significant roles in data-driven decision-making;
  • understand how to measure and display uncertainty, the effect of randomness, confidence/credibility, and the likelihood of incorrect inferences;
  • understand and be able to explain common misperceptions, paradoxes, and fallacies of probability and statistics; and
  • understand basic regression, prediction, simulation, and visualization methods.

Mathematical Skills

Graduates will:

  • have a firm grasp of the mathematical tools underlying statistical and computational methods which are primarily based on ideas in calculus, linear algebra, and discrete mathematics, including distribution theory, uncertainty quantification (e.g., probability theory), the probabilistic basis of formal statistical inference, models, and algorithms, and combinatorial analysis and recursion, which are used for algorithmic analysis, design, and for distribution theory.

Communication Skills

Graduates will be able to:

  • clearly justify and communicate study results to a non-technical audience;
  • write accurate and meaningful reports that describe the statistical and computational analyses and summarize important findings; and 
  • work effectively as part of a team to address substantive questions that can be handled using statistical and computational methods.