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- closed book, no software like R, __just TestVision__
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- 4 April (time tba; 3 hours)
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- On campus, using __TestVision__
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- Software & materials
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- access to R/RStudio, Git, make
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- access to github.com/course-dprep and classroom.github.com; no access to ChatGPT or other AI tools
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- I'm making selecting resources available on the instruction page - [check them out here](https://github.com/hannesdatta/course-dprep/raw/master/content/docs/exam/cheatsheets-exam.zip)
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- What?
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- How to prepare?
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- familiarize yourselves with [how TestVision works with a practice test](https://oefentoetsen.testvision.nl/online/fe/login_ot.htm?campagne=tlb_demo_eng&taal=2)
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- let's look at [some questions now](https://dprep.hannesdatta.com/docs/exam/examplequestions/#theoretical-part)
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- let's look at [some questions now](https://dprep.hannesdatta.com/docs/exam/examplequestions/)
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Exam overview: Practical part
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=============
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- Organization
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- When: 17 October, 10am - 11.59am + 1 minute, take home
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- Work max. 2 hours on this part
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- How?
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- open book, on your computer
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- ChatGPT and other tools ONLY when explicitly asked for.
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- What?
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- let's look at [some questions now](https://dprep.hannesdatta.com/docs/exam/examplequestions/#practical-part)
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- prep well - expect new datasets that are big (too big maybe even) -- aggregation, selection of number of rows, etc.
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Some tips for your exam
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=======
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incremental: true
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- Expect an unexpected data set & data wrangling
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- Know common data operations in `dplyr` & become fast!
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- When handing in documents, check what I require (for `.Rmd`, I sometimes ask for rendered `.pdf` documents - does it work on your computer?)
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- Be prepared to make commits to GitHub repositories -- know how clone, fork, write issues, do PRs, roll back to previous versions, etc.
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- Be prepared to run, correct and develop new `make` workflows
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- Be prepared to work with Git Bash
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- know how to make commits with commit messages, create branches, switch branches, etc.
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- download git repository, unzip, do your commits, zip again and submit as a zipfile
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- Be prepared to work with GitHub
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- know how to clone, fork, write issues, do PRs
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- know how to roll back to previous version
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- Be prepared to use `make`
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- run, correct and develop new `make` workflows
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- Be prepared to download data sets from Testvision (`.Rdata`) - on the Cover page of the exam or in a specific question on the exam.
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Learning goals + distribution of points
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==========
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- 100 points in total, about 25 questions
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- Mix of open and closed questions
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- Learning goals & question weights
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1. Use R to clean and transform data for analysis (e.g., aggregation, merging, de-duplication, reshaping, data conversions, regular expressions) [synthesis; __20% of points__]
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2. Use GitHub for managing empirical research projects (e.g., GitHub Issues and Project Boards) [evaluation; __10% of points__]
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3. Use Git/GitHub for versioning files and collaborating on privately-shared and publicly-available (open science) GitHub repositories [application; __30% of points__]
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4. Use R for generating automatic reports (e.g., to assess data quality, to report research findings in a paper) and deploying research findings in novel ways (e.g., apps) [comprehension; __15% of points__]
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5. Use Workflow Management Tools to create and run portable, automated, and reproducible research pipelines [application; __25% of points__]
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