References
STAT 4160: Data Science Productivity Tools
Preface
Introduction
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Session 1 — Dev environment & Colab workflow
2
Session 2 — Git essentials & Git‑LFS
3
Session 3 — Quarto Reports (Python)
4
Session 4 — RStudio Quarto cameo + Report Hygiene
5
Session 5 — Unix/Shell Essentials for Data Work
6
Session 6 — Make/Automation + rsync + ssh/tmux (survey)
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Session 7 — SQL I: SQLite Schemas & Joins
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Session 8 — SQL II: Window Functions & `pandas.read_sql` Workflows
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Session 9 — Cleaning, Joins, and Parquet
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Rolling Windows, Resampling, and Leakage‑Safe Features
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Session 11 — APIs with
requests
: Secrets, Retries, and Caching
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Session 12 — HTML Scraping: Ethics & Resilience
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Session 13
14
pre‑commit & GitHub Actions CI
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Session 15 — Framing & Metrics (Rolling‑Origin Evaluation)
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Session 16
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Session 17 — Feature Timing, Biases & Leakage
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Session 18 — Walk‑forward + Regime Analysi
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Session 19 — Tensors, Datasets, Training Loop
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Session 20 — Multi‑asset training (unified model
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Session 21 — Attention & Tiny GPT on Toy Data
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Session 22 — Adapting GPT to Time Series
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Session 23 — Packaging & CLI (Typer)
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Session 24 — Reproducibility audit & optional FastAPI
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Session 25 — Poster + Abstract Workshop
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Session 26 — In‑class Presentations & Continuation Pla
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README.html
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Lecture 5 (Pre) — Linux Basics
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Summary
References
References
Hyndman, Rob J., and George Athanasopoulos. 2021.
Forecasting: Principles and Practice
. 3rd ed.
https://otexts.com/fpp3/
.
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Summary