Issues

• Module 2 - Lesson 10: Consolidate what you have learned, and explore machine learning

  Lesson
  whythawk/data-as-a-science#19 · Filter by author turukawa opened on Sep 16, 2019 · Module 2
  

• Module 2 - Lesson 9: Liquid modernity, multiple jurisdictions, and assessing causality in randomised control trials

  Lesson
  whythawk/data-as-a-science#18 · Filter by author turukawa opened on Sep 16, 2019 · Module 2
  

• Module 2 - Lesson 8: Human agency and autonomous systems, and permutation testing for classification

  Lesson
  whythawk/data-as-a-science#17 · Filter by author turukawa opened on Sep 16, 2019 · Module 2
  

• Module 2 - Lesson 7: Counterfactual consequences, and implementing, testing and optimising classifiers

  Lesson
  whythawk/data-as-a-science#16 · Filter by author turukawa opened on Sep 16, 2019 · Module 2
  

• Module 2 - Lesson 6: Emergent systems, strange loops, and supervised and unsupervised learning techniques

  Lesson
  whythawk/data-as-a-science#15 · Filter by author turukawa opened on Sep 16, 2019 · Module 2
  

• Module 2 - Lesson 5: Strong and weak machine intelligence, and classification using logistic regression

  Lesson
  whythawk/data-as-a-science#14 · Filter by author turukawa opened on Sep 16, 2019 · Module 2
  

• Module 2 - Lesson 4: Ultimatum games, “fairness” and model selection for multiple regression

  Lesson
  whythawk/data-as-a-science#13 · Filter by author turukawa opened on Sep 16, 2019 · Module 2
  

• Module 2 - Lesson 3: Reflective equilibrium, and methods for multiple regression

  Lesson
  whythawk/data-as-a-science#12 · Filter by author turukawa opened on Sep 16, 2019 · Module 2
  

• Module 2 - Lesson 2: Doctrine of double effect, and interpreting regression with visual and numerical diagnostics

  Lesson
  whythawk/data-as-a-science#11 · Filter by author turukawa opened on Sep 16, 2019 · Module 2
  

• Module 2 - Lesson 1: Trolley problems, and predictions using regression and least squares

  Lesson
  whythawk/data-as-a-science#10 · Filter by author turukawa opened on Sep 16, 2019 · Module 2
  

• Module 1 - Lesson 10: Publishing and evaluating studies based on cohort data and analysis of variance

  Lesson
  whythawk/data-as-a-science#9 · Filter by author turukawa opened on Sep 16, 2019 · Module 1
  

• Module 1 - Lesson 9: Sample robustness, central limit theory, and the ethics and abuses of p-hacking

  Lesson
  whythawk/data-as-a-science#8 · Filter by author turukawa opened on Sep 16, 2019 · Module 1
  

• Module 1 - Lesson 8: Bootstrapping and the risks of algorithmic decision-making

  Lesson
  whythawk/data-as-a-science#7 · Filter by author turukawa opened on Sep 16, 2019 · Module 1
  

• Module 1 - Lesson 7: Hypothesis testing, and risks for policy from poor data

  Lesson
  whythawk/data-as-a-science#6 · Filter by author turukawa opened on Sep 16, 2019 · Module 1
  

• Module 1 - Lesson 6: Techniques in data and population sampling, and assessing standard error

  Lesson
  whythawk/data-as-a-science#5 · Filter by author turukawa opened on Sep 16, 2019 · Module 1
  

• Module 1 - Lesson 5: Expected statistical outcomes using distributions, and issues for analysis

  Lesson
  whythawk/data-as-a-science#4 · Filter by author turukawa opened on Sep 16, 2019 · Module 1
  

• Module 1 - Lesson 4: Sampling, data distribution, and secure data custody

  Lesson
  whythawk/data-as-a-science#3 · Filter by author turukawa opened on Sep 16, 2019 · Module 1
  

• Module 1 - Lesson 3: Probability, randomness, and the risk of de-anonymization

  Lesson
  whythawk/data-as-a-science#2 · Filter by author turukawa opened on Sep 16, 2019 · Module 1
  

• Module 1 - Lesson 2: Research and experiments with data

  Lesson
  whythawk/data-as-a-science#1 · Filter by author turukawa opened on Sep 16, 2019 · Module 1