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- You know the central dogma of statistics
- Basics of statistical inference (estimates, standard errors, basic distributions, etc.)
- You know how to fit and interpret statistical models
- Linear Models
- Generalized Linear Models
- Smoothing splines
- Basic mixture models
- You know the basics of R or Python
- You can read in, clean, tidy data
- You can fit models
- You can make visualizations
- 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
- 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
- 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"
- 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
- 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
- You will be able to produce the components of a data analytic presentation for technical and non-technical audiences:
- Problem introduction
- Methods
- Results
- Conclusions
- 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