Title: Advanced Data Science: A Graduate Course

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Getting Started
Module 1: Acquisition and the reproducible workflow
  Overview
  Watch: The reproducible acquisition workflow
  Lecture slides (PowerPoint, editable)
  Read: Software Carpentry, Version Control with Git
  Read: Data Carpentry, Databases and SQL
  Read: Ethical Web Scraping
  Practice: Select Star SQL
  Practice: Hoppscotch
  Practice: JSONPlaceholder
  Practice: quotes.toscrape.com sandbox
  Practice: Binder
  Practice and formative deliverable
  Assessment: The Provenance Incident
  Discussion
Module 2: Preprocessing messy and dynamic data
  Overview
  Watch: Cleaning as defensible decisions
  Lecture slides (PowerPoint, editable)
  Read: Jake VanderPlas, Python Data Science Handbook
  Read: NIST/SEMATECH e-Handbook, Detection of Outliers
  Practice: Kaggle Learn, Data Cleaning
  Practice: Kaggle Learn, Feature Engineering
  Practice: missingno
  Practice and formative deliverable
  Assessment: Recover the Signal
  Discussion
Module 3: Exploratory analysis, visualization, and the stakeholder story
  Overview
  Watch: From chart to decision
  Lecture slides (PowerPoint, editable)
  Read: Fundamentals of Data Visualization (Wilke)
  Read: Tableau Public, Viz of the Day
  Read: Our World in Data
  Practice: Google Facets
  Practice: Data-to-Viz chart chooser
  Practice: Data-to-Viz misleading-figure catalog
  Practice: Financial Times Visual Vocabulary
  Practice and formative deliverable
  Assessment: The 10-Minute Executive Brief
  Discussion
Module 4: Supervised learning: regression and classification
  Overview
  Watch: Choosing the problem type first
  Lecture slides (PowerPoint, editable)
  Read: An Introduction to Statistical Learning (Python edition, ISLP)
  Read: Harvard CS109A runnable notebooks
  Practice: MLU-Explain Linear Regression
  Practice: MLU-Explain Logistic Regression
  Practice: MLU-Explain Random Forest
  Practice: TensorFlow Playground
  Practice: R2D3, A Visual Introduction to Machine Learning
  Practice and formative deliverable
  Assessment: The Private-Leaderboard Competition
  Discussion
Module 5: Rigorous model evaluation
  Overview
  Watch: Honest model evaluation
  Lecture slides (PowerPoint, editable)
  Read: An Introduction to Statistical Learning (ISLP), Ch.5 resampling
  Read: The Elements of Statistical Learning, Ch.7
  Practice: MLU-Explain Cross-Validation
  Practice: MLU-Explain ROC and AUC
  Practice: MLU-Explain Bias-Variance
  Practice: Google MLCC Thresholding
  Practice: Interpreting Confidence Intervals
  Practice and formative deliverable
  Assessment: The Ridgeline Renewables model audit
  Simulation: Ridgeline Renewables model audit
  Discussion
Module 6: Unsupervised learning: clustering and PCA
  Overview
  Watch: PCA and clustering
  Lecture slides (PowerPoint, editable)
  Read: An Introduction to Statistical Learning with Python (ISLP), Ch.12
  Read: Harvard CS109B clustering notebooks
  Practice: Setosa PCA
  Practice: Visualizing K-Means Clustering
  Practice: How to Use t-SNE Effectively
  Practice and formative deliverable
  Assessment: How Many Segments, Really?
  Simulation: How Many Segments, Really?
  Discussion
Module 7: Data and AI ethics and governance
  Overview
  Watch: Ethics as an engineering requirement
  Lecture slides (PowerPoint, editable)
  Read: Fairness and Machine Learning (Barocas, Hardt, Narayanan)
  Read: Data Feminism (D’Ignazio and Klein)
  Read: NIST AI Risk Management Framework
  Read: Data Science Code of Professional Conduct
  Practice: PAIR, Measuring Fairness
  Practice: MLU-Explain, Equality of Odds
  Practice: Survival of the Best Fit
  Practice: Aequitas
  Practice: Google What-If Tool
  Practice and formative deliverable
  Assessment: The Model-Governance Review Board
  Simulation: The Model-Governance Review Board
  Discussion
Module 8: The RiverCity consulting engagement and defense
  Overview
  Watch: The defensible engagement
  Lecture slides (PowerPoint, editable)
  Read: Evidently, drift monitoring
  Read: Streamlit, explorable app framework
  The engagement, week by week
  Assessment: The RiverCity Engagement
  Simulation: Defense practice
  Simulation: RiverCity capstone and defense
  Discussion