Merge branch 'master' of https://github.com/hannesdatta/course-dprep

content/docs/exam/examplequestions.md

@@ -8,89 +8,40 @@ draft: false
 
 # Example questions
 
-Questions will be asked along the course's learning goals, and complexity levels (e.g., knowledge, application, evaluation). For details, see [here](../exam#content).
+Questions will be asked along the course's learning goals, and complexity levels (e.g., comprehension, application, synthesis, evaluation). For details, see [here](../exam#content).
 
 Below, you can find a few example questions, which will be discussed with students in the final live stream of this course.
 
 
-## Theoretical part
 
 {{< hint warning >}}
 
-This part of the exam consists of __personalized open and closed (multiple-choice) questions__, shown in __random order__. You can freely go back and forth between these questions.
+This exam consists of __open and closed (multiple-choice) questions__. You can freely go back and forth between these questions.
 
 {{< /hint >}}
 ![](../dprep_part1.png)
 
 *Note: the number of questions depends on the points awarded to each question. The instructions during the final exam may slightly vary, so make sure to still read it accordingly.*
 
-1. Please name a tool that can be used to automate workflows. (*knowledge*)
-
-2. Please name three ways to deploy one's research findings. (*knowledge*)
-
-3. What are the main benefits of exploring data using RMarkdown documents, compared to “point-and-click” interfaces (e.g., SPSS), or manually investigating data by issuing commands in the R terminal? (*comprehension*)
-
-4. What are the benefits from automating pipelines, compared to manually executing source code files? (*comprehension*)
-
-5. Please view the code snippet below.
-
-```
-library(dplyr)
-df <- read.csv('data.csv')
-df <- df %>% filter(age >= 18)
-```
-Please assess the completeness of the script with regard to the ITO components of a source code file. Can you identify any missing piece in the code? (*analysis*)
-
-6. Please assess whether the makefile below will run when you type "make". (*analysis*)
-
-__Directory Structure__
-```
-\readme.md
-\code\makefile
-\code\load.R
-\data\dataset.csv
-```
-__Makefile__
-```
-data/dataset.csv: load.R
-      R --vanilla < load.R
-```
-
-![](../dprep_overview.png)
-
-## Practical part
-
-{{< hint warning >}}
-
-This part of the exam consists of __personalized open questions__, shown in __random order__. You can freely go back and forth between these questions.
-
-{{< /hint >}}
-
-![](../dprep_part2.png)
-
-*Note: the instructions during the final exam may slightly vary, so make sure to still read it accordingly.*
-
-### Question 1
-
-Imagine you have just enrolled as a thesis student, and you receive the following email from your advisor:
+1. Please download the `datasets.RData` workspace file from the exam cover page and open it in RStudio. Please answer the following questions using the objects in this R workspace.
+    1. Please use the dataset stored in `data1`. Using `dplyr`, reshape this dataset from wide to long. Paste the code snippet with the solution below.
+    2. Please use the dataset stored in `data2`. Using `dplyr`, please create an aggregated dataset, taking an average of `variable1` and `variable2` for all users in the data (i.e., you obtain a dataset with the number of rows equal to the number of users in the data).
+    3. Please take a look at `data3`. Please propose which data preparation steps are necessary to clean this data.
+2. Imagine you have just enrolled as a thesis student, and you receive the following email from your advisor. Submit your PDF document, and provide a conclusion on the suitability of the explored data for the research question.
 
 {{< hint >}}
 
 Dear (name of student),
 
-I really look forward to working with you on this exciting dataset, capturing the consumption of music on Spotify. I scraped it from spotifycharts.com a while ago.
+I really look forward to working with you on this exciting dataset, capturing the consumption of music on Spotify. I scraped it from spotifycharts.com a while ago. Please download this `data.zip`, which contains a stripped-down version of an RMarkdown file and the data. 
 
 As a starting point, please explore the data set using RMarkdown. I’d love to learn more about the data myself (haven’t looked into it yet) - maybe you can figure out a way to shed some light on how the start of the global pandemic (let’s assume that was March 2020) affected music consumption?
 
 Please render your RMarkdown as a PDF document. Please keep any code that you’re writing (e.g., to load the data, or to explore and do some minor data preparations) visible so I can learn from it!
 
 {{< /hint >}}
 
-Submit your PDF document for question 1, and provide a conclusion on the suitability of the explored data for the research question? (*analysis*)
-
-### Question 2
-
-Imagine you are a research assistant at Tilburg University, and you receive the following email from your project supervisor:
+3. Please download the `github_repository.zip` file from the exam cover page and unzip it to a folder on this computer. Open this folder using Git Bash. Imagine you are a research assistant at Tilburg University, and you receive the following email from your project supervisor. Please submit your Git repository, by zipping the folder and uploading it here. 
 
 {{< hint >}}
 
@@ -105,21 +56,38 @@ Starting from `run.R`, can you apply your learnings from dPrep, and submit a lin
 - Have a proper readme at the repository (in an `.md` file),
 - Ignore files that should not be versioned using .gitignore, and
 - remove `run.R` and replace it by a proper makefile for this project.
+- throughout, make use of frequent commits and commit messages.
 
-I really look forward seeing your work. Your deliverable is just a link to a (private!) GitHub repository, provided in the answer box below.
+I really look forward seeing your work. Your deliverable is the zipped Git repository, which you can upload in the answer box below.
 
 {{< /hint >}}
 
-a) Please submit your GitHub link with your end-to-end GitHub workflow using make (*application*)
-
-b) How could you determine whether the GitHub workflow runs well, beyond merely executing it yourself? (*evaluation*)
-
-
-<!--
-
-{{< hint info >}}
-
-__This section is still work-in-progress (i.e., we are still adding examples and add code/data where needed).__
-
-{{< /hint >}}
--->
+4. Other example questions.
+      1. Please name three ways to deploy one's research findings. (*knowledge*)
+      2. What are the main benefits of exploring data using RMarkdown documents, compared to “point-and-click” interfaces (e.g., SPSS), or manually investigating data by issuing commands in the R terminal? (*comprehension*)
+      3. What are the benefits from automating pipelines, compared to manually executing source code files? (*comprehension*)
+      4. Please view the code snippet below and assess the completeness of the script with regard to the ITO components of a source code file. Can you identify any missing piece in the code? (*analysis*)
+
+      ```
+      library(dplyr)
+      df <- read.csv('data.csv')
+      df <- df %>% filter(age >= 18)
+      ```
+
+      5. Please assess whether the makefile below will run when you type "make". (*analysis*)
+
+      {{< hint >}}
+      Directory Structure:
+      
+      \readme.md
+      \code\makefile
+      \code\load.R
+      \data\dataset.csv
+     
+      Makefile:
+      
+      data/dataset.csv: load.R
+            R --vanilla < load.R
+      
+      {{< /hint >}}
+      

content/docs/modules/week3/_index.md

@@ -39,7 +39,7 @@ __Tips & tricks__
 - Curious how to use Git with a graphical user interface?
   - Use Git directly from within R - [find out how!](https://swcarpentry.github.io/git-novice/14-supplemental-rstudio/)
   - Another fantastic Git client is [Sourcetreeapp](https://www.sourcetreeapp.com), which works on Windows, Mac and Linux!
-- [Git & Github cheatsheet](https://github.com/tilburgsciencehub/website/raw/master/content/building-blocks/collaborate-and-share-your-work/use-github/github_cheatsheet_tsh.pdf)
+- [Git & Github cheatsheet](https://tilburgsciencehub.com/topics/automation/version-control/start-git/images/github_cheatsheet_tsh.pdf)
 - Optional activity: [Contributing to an open source web site](activity)
 
 {{< /hint >}}

content/docs/modules/week7/slides.Rpres

@@ -22,7 +22,9 @@ Welcome to the final lecture in dPrep!
 
 If you haven't done so, please **explore the exam page & example questions** at [https://dprep.hannesdatta.com/docs/exam]().
 
+<!--
 The __course evaluation__ is live at https://app.evalytics.nl. Please voice your opinions!
+-->
 
 <!--
 - Team project:
@@ -84,7 +86,7 @@ incremental: true
   - delete stuff that isn't needed, roll back when you want
   - Oh no, not authenticated!
   - Oh no! Can't push! (pull first!)
-  - GUIs (e.g., in R) are available, too!
+  - GUIs (e.g., in R or VS Code) are available, too!
 
 Lessons learnt #1: Versioning and Project Management with Git(Hub) (II)
 ========================================================
@@ -93,7 +95,7 @@ incremental: true
 - Ready to use Git/versioning in a business
   - Know what to version, and what not
   - Purpose of `.gitignore`
-  - How to work together (issues, feature branches, PRs)
+  - How to work together (issues, feature branches, pull requests/PRs)
 - Collaborate on open source projects
   - You know what forks are!
   - You may even actually contributed to public projects!
@@ -105,7 +107,7 @@ incremental: true
 - RMarkdown! (mixing code with reporting)
   - ability to quickly produce clean docs to share!
   - uh... but how to run it w/ make?
-  - __verify you can render markdown documents on your computer!__
+  - __verify you can render markdown documents!__
 - Doing data quality checks? 
   - getting back to your data supplier if needed!
   - reporting summary stats
@@ -123,7 +125,7 @@ incremental: true
 - Make ITO blocks
   - setup to load libraries, __then__ input, transformation, output!
   - super crucial for `make`, too!
-- Modularize code, iterate, and use functions
+- Modularize code, loop, and use functions
 
 Lessons learnt #4: Pipeline building and automation
 ========================================================
@@ -171,6 +173,7 @@ incremental: true
 
 - Gradually implement across classes or projects
   - e.g., some projects just benefit from better directory structure, while others may need "more"
+  - risk of "losing" the skill (!)
 - Realize it takes time to learn
   - it took me years to become proficient
 
@@ -191,8 +194,8 @@ incremental: true
 
 - Become part of the open science community at Tilburg
   - contribute to [Tilburg Science Hub](https://tilburgsciencehub.com)
-  - improve [music-to-scrape](https://music-to-scrape.org)
-  - follow us on [LinkedIn](https://www.linkedin.com/company/tilburgsciencehub/) and [Twitter](https://twitter.com/tilburgscience)
+  - improve [music-to-scrape.org](https://music-to-scrape.org)
+  - follow us on [LinkedIn](https://www.linkedin.com/company/tilburgsciencehub/) and [X](https://twitter.com/tilburgscience)
 - Invest in your research skills (e.g., Docker, coding)
 
 Looking ahead: After your studies
@@ -207,7 +210,7 @@ incremental: true
 Next steps: Submissions and preparing for the exam
 ========================================================
 
-- Deadline for project coming up!
+- Deadline for project coming up next week!
   - remember: __make it work__ on different computers and operating systems
   - __do not use__ absolute paths!
   - verify data can be downloaded smoothly
@@ -217,68 +220,83 @@ Next steps: Submissions and preparing for the exam
   - https://dprep.hannesdatta.com/docs/exam/examplequestions/
 
 
-Exam overview: Theoretical part
+Exam planning 
 =============
 
 - Organization
-  - When: 16 October, 1-2pm (1 hour)
-- How?
-  - closed book, no software like R, __just TestVision__
+  - 4 April (time tba; 3 hours)
+  - On campus, using __TestVision__
+- Software & materials
+  - access to R/RStudio, Git, make
+  - access to github.com/course-dprep and classroom.github.com; no access to ChatGPT or other AI tools
   - 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)
-- What?
+- How to prepare?
   - 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)
-  - let's look at [some questions now](https://dprep.hannesdatta.com/docs/exam/examplequestions/#theoretical-part)
+  - let's look at [some questions now](https://dprep.hannesdatta.com/docs/exam/examplequestions/)
 
 
-Exam overview: Practical part
-=============
-
-- Organization
-  - When: 17 October, 10am - 11.59am + 1 minute, take home
-  - Work max. 2 hours on this part
-- How?
-  - open book, on your computer
-  - ChatGPT and other tools ONLY when explicitly asked for.
-- What?
-  - let's look at [some questions now](https://dprep.hannesdatta.com/docs/exam/examplequestions/#practical-part)
-  - prep well - expect new datasets that are big (too big maybe even) -- aggregation, selection of number of rows, etc.
-
 Some tips for your exam
 =======
 incremental: true
 
 - Expect an unexpected data set & data wrangling
 - Know common data operations in `dplyr` & become fast!
 - When handing in documents, check what I require (for `.Rmd`, I sometimes ask for rendered `.pdf` documents - does it work on your computer?)
-- Be prepared to make commits to GitHub repositories -- know how clone, fork, write issues, do PRs, roll back to previous versions, etc.
-- Be prepared to run, correct and develop new `make` workflows
+- Be prepared to work with Git Bash
+  - know how to make commits with commit messages, create branches, switch branches, etc.
+  - download git repository, unzip, do your commits, zip again and submit as a zipfile
+- Be prepared to work with GitHub
+  - know how to clone, fork, write issues, do PRs
+  - know how to roll back to previous version
+- Be prepared to use `make`
+  - run, correct and develop new `make` workflows
+- Be prepared to download data sets from Testvision (`.Rdata`) - on the Cover page of the exam or in a specific question on the exam.
+
+
+Learning goals + distribution of points
+==========
 
+- 100 points in total, about 25 questions
+- Mix of open and closed questions
+- Learning goals & question weights
+  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__]
+  2. Use GitHub for managing empirical research projects (e.g., GitHub Issues and Project Boards) [evaluation; __10% of points__]
+  3. Use Git/GitHub for versioning files and collaborating on privately-shared and publicly-available (open science) GitHub repositories [application; __30% of points__]
+  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__]
+  5. Use Workflow Management Tools to create and run portable, automated, and reproducible research pipelines [application; __25% of points__]
 
 
 Next steps: Official course evaluation
 ========================================================
 
 - Course evaluation has been immensely important to this course
-  - this semester: new data set, coaching sessions with breakout groups on campus and online
-  - last semester: developed new on-campus tutorials, released example projects
+  - this semester: switched order of sessions, revised GitHub tutorial/onboarding
+  - last semester: new data set, coaching sessions with breakout groups on campus and online
+
 - Course evaluation has been critical to my career
   - Without my past evaluations, I wouldn't be teaching to you today
   - I will look at all comments. 
   - __Scores are most important to show importance of this course__
 
-- You will be invited via [Evalytics](https://app.evalytics.nl/#/login)
+- You will be invited via [Evalytics](https://app.evalytics.nl/#/login) at the end of the week
+
+Next steps: Self- and peer assessment
+========================================================
+
+- You will be invited via e-mail
+- To be filled in using Google Forms
+
 
 
 Informal feedback
 ========================================================
 incremental: true
 
-- Coaching sessions?
+- Coaching sessions? (Online, offline)
 - How was it for beginners? 
 - What's are three things you'd like me to change?
 
 
-
 Stay in touch!
 ========================================================