As I look back at my progression through the course, I realize how far I have come. When I began this course, my digital experience was limited to downloading music and using social media. Naturally, I found this course and its content extremely overwhelming at first. However, from the beginning of the course, Dr. Graham was there to provide guidance and assistance whenever I had a problem. And through Slack, I was also able to communicate with other classmates and our TA to work through any issues and solve them.

In the beginning of the course, I found Markdown frustrating to use, and after much trial and error, completing the Dillinger exercise felt very gratifying. Learning GitHub was also challenging for me, I found the terminology confusing and hard to understand. The Git-It tutorial was helpful here, it was very informative and it provided a clear outline for how to understand and use GitHub. Dr. Graham helped me get the tutorial started, and explained things further to help me understand what I was trying to accomplish. With his help, I was able to create my Open-Notebook- and successfully sync my GitHub desktop to the server. When I worked through the Who-We-Are exercise, I felt more confident with navigating GitHub. Practicing how to fork a repository, contribute, and then make a pull request in the Git-It tutorial helped me to compete this exercise.

I attempted all of the exercises listed under module 2, however I decided to display my work on the Wget exercise because it was the only exercise where I understood exactly what I was doing. The other exercises, which use APIs and Outwit Hub for example, scrape data and give results that are machine readable, so it was harder for me to determine if I was completing the exercise correctly and if the correct data was collected. Moving forward, I began selecting the module exercises based on how useful I thought it would be for my final project. In the Stanford NER exercise I learned how to mine text for specific words such as people, places, and dates. I found this exercise straight forward, however I did have an issue with running NER. After trying different bodies of text, and different methods for uploading that text, Stanford NER was able to label the organizations, locations, and people mentioned in the text I uploaded.

As there are many different ways to approach visualizing data, I chose the Overview and Voyant tools to look at word frequencies and connections between words. I decided to display my notes on the Voyant exercise because this exercise challenged me to understand how to use its various tools such as RizoViz. The last exercise I selected, on Typography, was very interesting because it focused on the role of visualization in communication. I never gave much thought to the importance of font type, but this exercise helped me to understand its role in communication. As part of the exercise, I created a gh-page. Using what I learned about font paring, I created a version of my notes with fonts selected from Google.

Above all, this course taught me how important collaboration is in digital history. Collaboration strengthens a historian’s ability to research and record history. Through collaboration, historians can gain access to vast amounts of data, learn from another research project, and build on research projects for the better.

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