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Lecture notes. Written from the material of Berkeley Stat 243, licensed CC BY 4.0. These are notes, not a transcript: the material has been reorganised and rewritten. This adaptation carries the same licence, and the original is linked above.

7. First Three Weeks Logistics

What this covers

This chapter documents the administrative structure of the first three weeks of Stat 243 (Berkeley, Fall 2021): what students were required to do and by when, what was optional, and which tools the course expected them to pick up before the technical material started. It is not a lecture on a technical topic — the source is the course's own to-do list and events calendar — so it is presented here as a schedule and toolkit checklist rather than as a sequence of results. It assumes only that the reader is following the course from its start; no programming or statistics background is assumed beyond what is listed below.

Week 1: getting set up

Before any technical content, the course opened with logistics:

  • Two required surveys, due Friday August 27 at noon: a class survey, and a survey of possible office-hour times.
  • One optional survey, same deadline, for anyone wanting an extra R help session — used only to schedule it.
  • Required reading: Sections 1–3 of Unit 2, on getting CSV and similar data into and out of R and the pitfalls that arise, due by Monday August 30. Class time would only touch on a point or two from these sections, on the assumption the reading had been done.
  • Optional UNIX catch-up: anyone not comfortable with the basic UNIX usage shown in the first class was pointed to a UNIX basics tutorial (with self-check questions, nothing to submit) and to a Friday UNIX help session, ahead of the following Wednesday's class.

The pattern set here — required items with hard deadlines, optional catch-up material for anyone behind, and a help session timed just before the material is needed in class — repeats through weeks 2 and 3.

Weeks 2–3: shell, regular expressions, and version control

  • Optional R catch-up: for anyone not yet at the level of modules 1–5 of the R bootcamp, working through those modules (and their breakout problems) by the end of the week of September 6, with a catch-up session offered for extra practice.
  • Required bash tutorial, due Friday September 3 at 10am: work through a tutorial on using the bash shell and submit answers to its first 10 problems via bCourses, as plain text — no formatting or explanation required, and hand-written scanned answers were explicitly acceptable. Not every section of the tutorial was assigned; a list of sections to skip was given separately (Section 2 of the course's unit4-bash.pdf). A live demonstration of the bash shell in class on Wednesday September 1 was meant to support this.
  • Problem Set 1, due Wednesday September 8 at 10am — the course's first graded problem set.
  • Required regular-expression reading and practice, due Friday September 10 at 10am: the regex material in Section 3 of the bash tutorial (Section 3.6 excluded), plus Section 2.1 of a string-processing tutorial (with the stringr material in 2.1.2 as an optional focus), followed by a set of regex practice problems submitted through a Google form. This was graded only as complete or not — not one of the numbered problem sets.

Events in the first three weeks

Alongside the to-do items, several sessions were scheduled, mixing optional support with required sections:

  • Optional LaTeX sessions (library-run), Thursday August 26 or Wednesday September 8, 4–5:30pm — recommended in particular for statistics graduate students, on the grounds that being able to typeset equations in LaTeX is worth having early.
  • Optional software/UNIX help session, Friday August 27, noon–3:30pm, for installing software, getting a UNIX-style command line working, and basic UNIX usage — aimed at having everyone set up before the Wednesday September 1 class.
  • Optional R help session, date/time to be determined by the survey above, for anyone below the R bootcamp modules 1–5 level.
  • Required section/lab, Friday September 3: Git, setting up a GitHub repository for problem sets, and using RMarkdown/knitr to produce dynamic documents. Students attended only their registered section, given room capacity.
  • Required section/lab, Friday September 10: assertions and testing. Same attendance rule.

Read together, the two lists show what the course treats as prerequisite machinery before any statistical computing proper begins: a working UNIX-style shell, enough R to be productive, basic regular expressions and string processing, and a Git/GitHub workflow for submitting problem sets via reproducible documents (RMarkdown/knitr). The technical content of the course builds on top of this toolkit rather than introducing it later.

Sources

  • docs/statistical-computing/berkeley/stat243/stat243-fall-2021/first_three_weeks/01-assignments-to-dos.md — "Assignments / to-dos", Week 1 and Weeks 2–3 sections.
  • docs/statistical-computing/berkeley/stat243/stat243-fall-2021/first_three_weeks/02-events.md — "Events" list for the same period.
  • Both converted losslessly (CC0-1.0) from first_three_weeks.md in the berkeley-stat243/stat243-fall-2021 course repository.
  • Referenced but not contained in these files, and not reproduced here: the class survey and office-hours survey (Google Forms), the UNIX basics tutorial, the R bootcamp (modules 1–5), the bash shell tutorial and its first 10 end-of-tutorial problems, unit4-bash.pdf (Section 2, for which sections to skip), the string-processing tutorial (Section 2.1), the regex practice-problem form, and the bCourses assignment submission page. Unit 2, Sections 1–3, on reading and writing CSV-type data in R, is likewise referenced but not included.

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