Session 1: Welcome, Tooling & the Data Science Workflow
Monday, 14 September 2026, 09:00–11:15 · Room MAD 099
What this session is about
Where data science sits in a business, what the semester looks like, and getting the three tools running on your laptop. By the end you will have written and rendered your first reproducible report — and found two problems in the data that nobody warned you about.
Learning goals
- Know how the course works: schedule, assessment, and the rules on AI
- Have R, RStudio and Quarto installed and working
- Understand the data science workflow: import → tidy → transform → visualize → model → communicate
- Set up an RStudio project, and know why absolute file paths break analyses
- Write a Quarto document mixing text and code, and render it to HTML
- Produce a first
ggplotchart — and interrogate it rather than trust it
Please install R, RStudio and Quarto beforehand — see Getting Started and the Tutorials. All three are free, and Quarto installs separately from RStudio; people miss that one.
If it does not work, come anyway and bring your laptop. There is a quarter of an hour set aside at 09:30, before we start coding, to get everybody running, and nobody is expected to sort it out alone. Do not skip the session over a broken install — that is precisely the problem we solve today.
If you already know yours is broken, come at 08:45 and we will start on it before the room fills up.
R on its own cannot do very much. Almost everything we write uses packages — add-ons you install once and then switch on whenever you need them. Two today:
install.packages(c("tidyverse", "flextable"))Run that line once, in the Console, before Monday. It takes a few minutes and needs an internet connection. Inside a document you then switch them on at the top, every time:
library(tidyverse)
library(flextable)tidyverse is what we read data and draw charts with; flextable makes tables look presentable.
If the installation fails, read the last line of the output, not the first — and see Installing R packages, which explains what installing and loading actually do and what to try when it goes wrong.
Slides
Open the slides in a separate tab
Open the slides in a separate tab in Chrome, add ?print-pdf to the end of the address, then print to PDF.
Materials
| File | Description |
|---|---|
| Exercise sheet | The in-session exercise |
| hotels-vienna.csv | The data — save it into a data/ folder in your project |
| Slides | The deck from the session |
| Live coding document | The narrated code we wrote together |
| Exemplary solution | Worked answers to the exercise |
Everything you need for the session is here beforehand. The slides, the live coding document and the exemplary solution go online after the session — you get more out of the exercise when you have not already read the answer.
Data
Vienna hotels. 428 properties, price for one night in November 2017, with star rating, distance to the city centre, guest rating and accommodation type. One row is one hotel.
From Békés and Kézdi (2021), gabors-data-analysis.com, licensed CC BY-NC-SA 4.0. Unmodified.
We found some problems with this data by plotting it. You should also do this. Check out the demo script in case you cannot remember any more.
The diagnostic
At the end of the session there is a short set of questions on regression and descriptive statistics. It is anonymous and does not count for anything.
Its only purpose is to tell me how much of Statistik I and II is still fresh, so I can set the difficulty of the later sessions to match. If I get that wrong in either direction, a session is wasted — and it is your session. Ticking “I don’t know” is therefore genuinely helpful, and costs you nothing.
Take-home until session 2
Not graded, but session 2 assumes it.
- Fix any remaining installation problems
- Work through First steps in R (Gräbner-Radkowitsch 2026)
- Post one question in the Moodle forum — anything from the session or the tutorial that confused you. We start session 2 with them, and posting rather than just thinking of one means everybody sees the answer.
Going further
- Wickham et al. (2023), ch. 1 “Data visualization”
- Wickham et al. (2023), ch. 28 “Quarto”
- Quarto: Get Started
Every document on this site has a </> Code button in the top right. Click it and choose View Source to see the raw .qmd. Copy it into RStudio, save it with a .qmd extension, and you have a working local copy.