Data Science B.S. Learning Outcomes

LO1: Knowledge of foundational mathematical concepts relevant to data analysis, including

  • calculus (integration and differentiation), both univariate and multivariate
  • applied linear algebra (matrix operations, linear transformations, projections in Euclidean
  • concepts in probability including univariate and multivariate distributions for both
    discrete and continuous densities, random variables, independence and conditional
    independence, expectation, limit theorems

LO2: Knowledge of basic principles in computer science, including

  • basic concepts in Boolean algebra and logic
  • basic concepts in computer hardware
  • basic concepts in operating systems and programming languages
  • basic concepts in data management
  • basic concepts in algorithms and data structures
  • basic concepts in computational complexity

LO3: Knowledge of foundational statistical concepts, including

  • foundational statistical theory including parameter estimation, hypothesis testing,
    decision theory, maximum likelihood, and Bayesian methods
  • statistical modeling principles, including linear and logistic regression models, nonparametric methods, diagnostic techniques
  • design of statistical studies, including sampling methodologies, random assignment,
    data collection, efficiency, and issues of bias, causality, confounding, and coincidence;
    sample size and power calculations
  • exploratory data analysis methods including visualization, projection methods, clustering,
    and density estimation

LO4: Knowledge of basic principles in statistical computing including

  • ability to understand and program randomization and simulation techniques such as
    bootstrap, Monte Carlo and cross-validation methods
  • ability to understand and program optimization methods such as gradient descent and
    solving linear systems of equations
  • ability to understand and implement basic concepts and algorithms in machine learning,
    such as predictive modeling for classification and regression
  • basic knowledge of issues in handling large-scale data, such as scalability, indexing,
    data management, and distributed computing

LO5: Ability to take a real-world data analysis problem, formulate a conceptual approach to the
problem, match aspects of the problem to previously learned theoretical and methodological
tools, break down the solution into a step-by-step approach, and implement a working solution
in a modern software language, including

  • knowledge of basic concepts in developing and testing software programs
  • knowledge of basic concepts in software engineering skills and experience in developing
    software in at least one modern programming language such as Python
  • basic skills in data management, including relational database systems and SQL
  • basic skills in at least one statistical programming environment such as R
  • basic concepts in data collection such as sampling methods, handling missing data, data
    provenance, ethics in data collection
  • basic concepts in data privacy, e.g., legal aspects, institutional review boards,
    algorithmic techniques for data anonymization

LO6: Ability to communicate effectively in data analysis projects, including

  • effective technical writing and presentations
  • teamwork and collaboration
  • communicate results to both data analysis specialists and non-specialists accurately,
    concisely, and effectively

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