Working in Data Science, I often feel like I have to justify using R over Python. And while I do use Python for running scripts in production, I am much more comfortable with the R environment. Basically, whenever I can, I use R for prototyping, testing, visualizing and teaching. But because personal gut-feeling preference isn’t a very good reason to give to (scientifically minded) people, I’ve thought a lot about the pros and cons of using R.
It’s been a long time coming but I finally moved my blog from Jekyll/Bootstrap on Github pages to blogdown, Hugo and Netlify! Moreover, I also now have my own domain name www.shirin-glander.de. :-) I followed the blogdown ebook to set up my blog. I chose Thibaud Leprêtre’s tranquilpeak theme. It looks much more polished than my old blog. My old blog will remain where it is, so that all the links that are out there will still work (and I don’t have to go through the hassle of migrating all my posts to my new site).
I have written the following post about Data Science for Fraud Detection at my company codecentric’s blog: Fraud can be defined as “the crime of getting money by deceiving people” (Cambridge Dictionary); it is as old as humanity: whenever two parties exchange goods or conduct business there is the potential for one party scamming the other. With an ever increasing use of the internet for shopping, banking, filing insurance claims, etc.
GitHub vs. GitLab Git is a distributed implementation of version control. Many people have written very eloquently about why it is a good idea to use version control, not only if you collaborate in a team but also if you work on your own; one example is this article from RStudio’s Support pages. In short, its main feature is that version control allows you to keep track of the changes you make to your code.
I have written the following post about Social Network Analysis and Topic Modeling of codecentric’s Twitter friends and followers for codecentric’s blog: Recently, Matthias Radtke has written a very nice blog post on Topic Modeling of the codecentric Blog Articles, where he is giving a comprehensive introduction to Topic Modeling. In this article I am showing a real-world example of how we can use Data Science to gain insights from text data and social network analysis.
For all my other posts, see my old website: shiring.github.io
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