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Advanced Data Science 2020

See the course website live here: www.jtleek.com/ads2020. The live website has the most up-to-date information about the course.

Assumptions

  1. You know the central dogma of statistics
  • Basics of statistical inference (estimates, standard errors, basic distributions, etc.)
  1. You know how to fit and interpret statistical models
  • Linear Models
  • Generalized Linear Models
  • Smoothing splines
  • Basic mixture models
  1. You know the basics of R or Python
  • You can read in, clean, tidy data
  • You can fit models
  • You can make visualizations
  1. You know the basics of reproducible research
  • You know what version control is
  • You know how to use Github
  • You know how to use R/Rmarkdown

Learning Objectives

  1. You will be able to critique a data analysis and separate good from bad analysis. Specifically you will be able to:
  • Identify the underlying question
  • Evaluate the "arc" of the data analysis
  • Identify the underlying type of question
  • Identify the study design
  • Determine if visualizations are appropriate
  • Determine if methods are appropriate
  • Identify pipeline issues
  • Identify reproducibility issues
  • Identify common fallacies and mistakes
  • Distinguish what is a real problem from what is just hard
  • Identify common fallacies and mistakes.
  • Evaluate the relationship between study design, data, and claims to data justification
  1. You will be able to produce a complete data analysis. Specifically you will learn to:
  • Translate general questions to data analysis questions
  • Explore your data skeptically
  • Select appropriate data analytic tools given the study design
  • Combine appropriate data analytic tools into pipelines
  • Identify strengths and weaknesses of data pipelines you produce
  • Describe the results of your analysis accurately
  • Decide what is and is not relevant to the "arc" of the data analysis
  • Write the "arc" of the data analysis
  • Avoid "reinventing the wheel"
  1. You will be able to produce the components of a data analytic paper:
  • The "arc" of a data analysis
  • Abstracts
  • Introductions
  • Figures
  • Tables
  • Methods sections
  • Discussion/limitations sections
  1. You will be able to produce the components of a methods paper:
  • The "arc" of a methods paper
  • Abstracts
  • Introductions
  • Figures
  • Tables
  • Simulation sections
  • Applications sections
  • Discussion/limitations sections
  1. You will be able to produce the components of a data analytic presentation for technical and non-technical audiences:
  • Problem introduction
  • Methods
  • Results
  • Conclusions
  1. You will be able to identify key issues in data analytic relationships. Specifically you will be able to:
  • Elicit objective functions from collaborators
  • Identify types of data analysis relationships (collaboration, consultation, employment)
  • Identify successful stategies for data analysis based on relationship type
  • Identify key ethical issues in data analysis
  • Understand your responsibility as a data analyst
  • Explain the value of data science to non-technical audiences

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