INFO-I 223 Data Fluency

3 credits

  • Prerequisites: None
  • Delivery: On-Campus
  • Pervasive, vast, and growing describe data in today’s environment. This course introduces fundamental skills for extracting from data actionable knowledge. Students create, access, munge, analyze, and visualize data to draw inferences and make predictions. The course uses real datasets from a variety of disciplines including healthcare, business, and the humanities.

    This course is approved for the Analytical Reasoning, List B, component of the General Education core.

    Learning Outcomes

    1. Store, structure, and access data of different types using simple relational models and tables.
    2. Munge data to prepare raw data for further analysis.
    3. Analyze large, complex datasets with supervised learning methods, including linear regression and k-nearest neighbors for functional approximation and naïve Bayesian classifiers and decision trees for classification and predictive modeling.
    4. Analyze large, complex datasets with unsupervised learning methods, including k-means clustering.
    5. Calculate probabilities by applying additive and multiplicative laws, permutations and combinations, and conditional probability.
    6. Calculate expectation and variance from the probability distribution of a random variable.
    7. Assess model fit (e.g., overfitting or underfitting).
    8. Create visualizations of data to communicate and persuade.
    9. Derive information from data and support conclusions or recommendations based on evidence existing in the data.

    Course Schedule