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Traditional Statistics Alignment with CourseKata

Traditional Statistics Alignment with CourseKata

Owned by Caylor Davis
Apr 01, 2024
3 min read

A mapping of traditional statistics topics and where they are covered in CourseKata.

Last updated: 8/25/22

Topic Alignment, by Book 

Topics Covered Aligned with Data Science I (AB) 

  1. Probability 

    1. Law of large numbers (chapters 2, 3, 4) 

    2. Sampling with and without replacement (2, 3) 

    3. Contingency tables (2, 3, 4) 

  2. Research methods 

    1. Sampling (2, 3, 4)

    2. Measurement: categorical v. quantitative variables (2)

    3. Organizing data (2) 

    4. Research design: correlational v. experimental (4) 

    5. Correlation, causality, and confounding (4, 7, 8) 

  3. Descriptive statistics 

    1. Univariate visualizations: histograms, box plots, bar graphs (3)

    2. Bivariate visualizations: frequency tables, faceted histograms, scatterplots, bar graphs, box plots (4)

    3. Summary statistics: 

      1. center (mean, median, mode) (3, 5) 

      2. shape (skew, normal, uniform, multimodal) (3, 5) 

      3. spread (standard deviation, sums of squares, variance) (3, 5, 6) 

      4. five number summary (3) 

      5. regression, correlation coefficient (8)

    4. Quantitative and categorical predictors; quantitative outcomes (2) 

    5. Z score (6) 

  4. Inferential statistics and sampling distributions 

    1. Mathematical distributions

      1. Probability under mathematical distributions (6) 

      2. normal/Z distribution, t distribution, F distribution (6) 

    2. Computational techniques 

      1. Simulation (2, 4, 6)

      2. Bootstrapping (3) 

      3. Randomization (4)

    3. Hypothesis testing

      1. ANOVA (7, 8) 

      2. Regression (8) 

      3. Type I and Type II error (4) 

      4. Concepts of power and effect size (7) 

  5. Organizing concepts (throughout textbook) 

    1. Modeling: DATA = MODEL + ERROR 

    2. General linear model; GLM notation

    3. Data analysis using R 

Topics Covered Aligned with Advanced Data Science I (ABC) 

  1. Probability 

    1. Law of large numbers (chapters 2, 3, 4, 9, 10, 11) 

    2. Sampling with and without replacement (2, 3) 

    3. Contingency tables (2, 3, 4) 

  2. Research methods 

    1. Sampling (2, 3, 4)

    2. Measurement: categorical v. quantitative variables (2) 

    3. Organizing data (2) 

    4. Research design: correlational v. experimental (4, 9) 

    5. Correlation, causality, and confounding (4, 7, 8, 9, 10) 

  3. Descriptive statistics 

    1. Univariate visualizations: histograms, box plots, bar graphs (3) 

    2. Bivariate visualizations: frequency tables, faceted histograms, scatterplots, bar graphs, box plots (4) 

    3. Summary statistics: 

      1. center (mean, median, mode) (3, 5) 

      2. shape (skew, normal, uniform, multimodal) (3, 5) 

      3. spread (standard deviation, sums of squares, variance) (3, 5, 6)

      4. five number summary (3) 

      5. regression, correlation coefficient (8) 

    4. Quantitative and categorical predictors; quantitative outcomes (2) 

    5. Z score (6)

  4. Inferential statistics and sampling distributions 

    1. Mathematical distributions

      1. Probability under mathematical distributions (6, 9, 10, 11) 

      2. Central limit theorem (9) 

      3. normal/Z distribution, t distribution, F distribution (6, 9, 10, 11) 

    2. Computational techniques 

      1. Simulation (2, 4, 6, 9, 10, 11) 

      2. Bootstrapping (3, 9, 10, 11) 

      3. Randomization (4, 9, 10, 11)

    3. Hypothesis testing

      1. t-test (9) 

      2. ANOVA (7, 8, 10) 

      3. Regression (8, 10) 

      4. Type I and Type II error (4, 10, 11) 

      5. Concepts of power and effect size (7, 10, 11) 

    4. Confidence intervals (11) 

  5. Organizing concepts (throughout textbook) 

    1. Modeling: DATA = MODEL + ERROR 

    2. General linear model; GLM notation

    3. Data analysis using R 

Topics Covered Aligned with Statistics and Data Science II (XCD) 

  1. Probability 

    1. Law of large numbers (chapters 4, 5, 6) 

    2. Sampling with and without replacement (4, 5, 6) 

    3. Contingency tables (1, 2) 

  2. Research methods 

    1. Sampling (1, 2, 4, 5, 6) 

    2. Measurement: categorical v. quantitative variables (1) 

    3. Organizing data (1) 

    4. Research design: correlational v. experimental (2, 3) 

    5. Correlation, causality, and confounding (2, 3, 4, 5, 6, 7, 8) 

  3. Descriptive statistics 

    1. Univariate visualizations: histograms, box plots, bar graphs (1) 

    2. Bivariate visualizations: frequency tables, faceted histograms, scatterplots, bar graphs, box plots (2, 7) 

    3. Summary statistics: 

      1. center (mean, median, mode) (1, 2) 

      2. shape (skew, normal, uniform, multimodal) (1) 

      3. spread (standard deviation, sums of squares, variance) (1, 2, 3) 

      4. five number summary (1) 

      5. regression, correlation coefficient (3) 

    4. Quantitative and categorical predictors; quantitative outcomes (2, 3) 

    5. Z score (covered in AB) 

  4. Inferential statistics and sampling distributions 

    1. Mathematical distributions 

      1. Probability under mathematical distributions (4, 5, 6) 

      2. Central limit theorem (4) 

      3. normal/Z distribution, t distribution, F distribution (4, 5, 6) 

    2. Computational techniques 

      1. Simulation (4, 5, 6, 7, 8) 

      2. Bootstrapping (6, 7, 8) 

      3. Randomization (4, 5, 6, 7, 8) 

    3. Hypothesis testing 

      1. t-test (4) 

      2. ANOVA (3, 4, 5) 

      3. Regression (3, 4, 5) 

      4. Type I and Type II error (4, 5, 6) 

      5. Concepts of power and effect size (3, 4, 5, 6) 

    4. Confidence intervals (7) 

  5. Organizing concepts (throughout textbook) 

    1. Modeling: DATA = MODEL + ERROR 

    2. General linear model; GLM notation 

    3. Data analysis using R