Topics

A variety of computations are essential for successful data science, including but not limited to traditional statistical and numerical methods. This course will examine current capabilities and challenges in data science including techniques important for: obtaining and organizing data; discovering and examining structure; valid explicit inference and prediction; and communicating scientific information effectively, both for policy advice and for general understanding.

The material may involve a variety of programming languages (e.g., R and Python), development environments (e.g., RStudio, Jupyter) and computing facilities (e.g., Cloud, GPU). Individuals are unlikely to be expert over this wide range, but our approach emphasizes being open to the best computing for the task and using modern tools to integrate facilities for a research goal.

Our first offering of this seminar will be in Spring quarter 2022 as a 1-credit seminar. This, of course, limits the selection of topics can be discussed, but we will use the experience to modify future offerings.

Some topics under consideration are:

  1. Privacy-preserving Computation
  2. Valid Inference
  3. Generating Training Data
  4. Machine Learning and Science
    • Data + ML vs Models (good, bad examples)
    • Cross validation pitfalls, what it measures…
  5. Natural Language Processing
    • Stanford NLP SDK
    • BERT/GPT
  6. Causal Inference
    • The Framework
    • R/Python packages
  7. Large-scale computation
  8. Metadata
    • Ontologies tools and strategies
    • Automatic computation with metadata
  9. Reproducibility frameworks