Data and Modeling Overview [Data and Modeling Overview]
- What it means to ‘model’ a phenomenon
- What is a variable
- What is a measurement
  - Observation (direct) vs estimation (indirect)
  - Interpolation as an instance of estimation (indirect)
  - Introduce concepts such as sampling vs complete population-level observations
    - Types of sampling e.g., random, systematic, cluster, etc.
Basic Data Analysis
- Summary Statistics [Data and Modeling Overview, Deep Dive into Data “Set”]
  - Measures of central tendency and their benefits/drawbacks
    - E.g., Mean, median, mode
  - Measures of variability and their benefits/drawbacks
    - E.g., Variation, range, standard deviation
  - Discuss the limitations of summary statistics with examples
    - E.g., Anscombe Quartet
- Aggregations
  - When and why do aggregations make sense or are necessary [Relating Data “Sets”, Mapping]
  - How to aggregate two data sets into one [Relating Data “Sets”]
  - How to aggregate data sets between levels of granularity [Mapping]
- Outliers [Plotting/Graphing]
  - Why we care about outliers
  - How to spot and handle outliers
Techniques for Data Visualization [Plotting/Graphing]
- Plotting vs graphing
- Why we visualize data
- Types of plots
  - Scatter, line, map, bar, histogram, etc.
- How to visualize 1-D vs 2-D vs N-D Data
Techniques for Statistical Modeling
- Distributions [Plotting/Graphing(Advanced Topic)]
  - Theoretical vs empirical (i.e. probability distributions vs frequency distributions)
  - Normal/Gaussian distribution
    - Properties of the normal distribution
    - When it is appropriate to model using the normal distribution
  - Exponential distribution
  - Other distributions
    - Bernoulli
    - Binomial
- Correlations [Correlations]
  - Linear vs quadratic vs exponential
  - Degree and direction of correlation
  - How to identify correlations
    - Visually (e.g., scatter plot)
    - Numerically (e.g., correlation coefficient)
  - Common pitfalls such as overgeneralization, Simpson’s paradox, etc.
  - Correlation vs. causation
Data Science in the Real World [Data and Modeling Overview]
- Lies, damn lies and statistics
  - Manipulative visuals
  - Manipulative statistics
- Cognitive biases
  - Survivorship bias
  - False causality fallacy
  - Other biases