Bibliographic record
Abstract
Abstract Essential Statistics for Data Science is a very short crash course for students entering a serious graduate program in data science without knowing enough statistics. However, it is not the type of introductory course that simply teaches students how to plug numbers into a formula and perform a t-test. While the course does start from the basics of probability and random variables, it moves along rapidly and ambitiously takes students in a matter of weeks to a number of relatively advanced topics in both frequentist and Bayesian inference as well as uncertainty assessment—such as the EM algorithm, the Gibbs sampler, and the bootstrap. The “main plot” unfolds in three parts. Part I, Talking Probability: The statistical approach to analysing data begins with a probability model to describe the data generating process; that's why, to study statistics, one must first learn to speak the language of probability. Part II, Doing Statistics: Before a model becomes truly useful, one must learn something about the unknown quantities in it—e.g., its parameters—from the data it is presumed to have generated, whether one cares about the parameters themselves or not; that's what much of statistical inference is about. Part III, Facing Uncertainty: Although one usually does not care much about parameters that don't have intrinsic scientific meaning, for those that do, it is important to explicitly describe how much uncertainty we have about them and take that into account when making decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.032 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".