# Where to continue?

Here I'll gather some relevant links to further resources. I think these are good, but they're also somewhat of a random selection.

* Term glossaries:
  * [Data science](https://www.datacamp.com/blog/data-science-glossary)
  * [Statistics](https://www.stat.berkeley.edu/~stark/SticiGui/Text/gloss.htm)
* General course lists:
  * [Elective theory and practice courses available as part of the Helsinki DH module](https://www.helsinki.fi/en/helsinki-centre-for-digital-humanities/teaching#section-44332)
  * [Elective method courses as part of the Helsinki DH module](https://www.helsinki.fi/en/helsinki-centre-for-digital-humanities/teaching#section-44333)
  * [Online courses at dariahTeach](https://teach.dariah.eu/course/index.php)
* Tools and data processing:
  * [Data Carpentry tutorials](https://datacarpentry.org/lessons/#social-science-curriculum)
  * [Library Carpentry tutorials](https://librarycarpentry.org/lessons/)
* The programming humanist:
  * [Python Programming for the Humanities](http://fbkarsdorp.github.io/python-course/), the best introduction to programming for humanists that I could find
  * [Software Carpentry tutorials](https://software-carpentry.org/lessons/)
  * [CodeRefinery tutorials](https://coderefinery.org/lessons/)
  * [The programming historian](http://programminghistorian.org/), lessons and tutorials for doing various DH things
  * [Eloquent Javascript](http://eloquentjavascript.net/), a nicely built general, interactive introduction to programming
* On visualisation:
  * [Fundamentals of Data Visualization](http://serialmentor.com/dataviz/index.html) (a good introductory book on choosing suitable visualisations for highlighting different aspects in data, and avoiding pitfalls in tuning them)
  * [Data Visualization - a practical introduction](http://socviz.co/) (starts with a good chapter organised around general principles, but then continues with very down to earth practical instructions on how to plot stuff using ggplot2 in R. This is very useful, but doesn't cover the general view on different graph types and their usefulness. Thus, a very nice complement to the book before)
* On statistics and computational data science:
  * [Statistical Inference via Data Science: A ModernDive into R and the Tidyverse!](https://moderndive.com/index.html), a very good and clear resource introducing both statistical concepts, as well as how to apply them in practice in R and Tidyverse. An excellent follow-up to the introduction in this course.
  * [Online Statistics Education: An Interactive Multimedia Course of Study](https://onlinestatbook.com/), an excellent alternative simple introduction to core statistical concepts
  * [Introduction to Open Data Science MOOC](https://mooc.helsinki.fi/course/view.php?id=158) at the University of Helsinki 
  * [Computational and Inferential Thinking - The Foundations of Data Science](https://www.inferentialthinking.com/), an excellent introduction to statistical analysis with interactive Python notebooks
  * [R for Data Science](https://r4ds.had.co.nz/) book
  * [The historian’s macroscope](http://www.themacroscope.org/?page_id=584), a good general-purpose book introducing different types of humanities data analysis
  * [Statistics for the Humanities](http://www.statisticsforhumanities.net/book/wp-content/uploads/2014/07/StatisticsforHumanities%205Sept14.pdf)
  * [Explained Visually](http://setosa.io/ev/)
  * [Kristoffer Magnusson's Visualizations of statistical concepts](https://rpsychologist.com/viz/)
* Further resources to go through that may cover bits and bobs missed by the above:
  * [Natural Language Processing for Historical Texts](http://nlphist.hypotheses.org/)
  * [Six Septembers: Mathematics for the Humanist](https://doi.org/10.13014/K2D21VHX)
  * [Text Analysis with R for Students of Literature](http://www.matthewjockers.net/text-analysis-with-r-for-students-of-literature/)
  * [Teaching yourself to code in DH list](http://scottbot.net/teaching-yourself-to-code-in-dh/)
  * [Technical Foundations of Informatics](https://info201.github.io/index.html)
  * [Computational Historical Thinking *With Applications in R*](http://dh-r.lincolnmullen.com/)
  * [Humanities Data in R](http://humanitiesdata.org/)
  * [Text Mining with R](http://tidytextmining.com/)


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