In a recent project, I was looking to plot data from different variables along the same time axis. The difficulty was, that some of these variables I wanted to have as point plots, while others I wanted as box-plots. Because I work with the tidyverse, I wanted to produce these plots with ggplot2. Faceting was the obvious first step but it took me quite a while to figure out how to best combine facets with point plots (where I have one value per time point) with and box-plots (where I have multiple values per time point).
I have written the following post about Predictive Maintenance and flexdashboard at my company codecentric’s blog: Predictive Maintenance is an increasingly popular strategy associated with Industry 4.0; it uses advanced analytics and machine learning to optimize machine costs and output (see Google Trends plot below). A common use-case for Predictive Maintenance is to proactively monitor machines, so as to predict when a check-up is needed to reduce failure and maximize performance.
Yesterday and today I attended the data2day, a conference about Big Data, Machine Learning and Data Science in Heidelberg, Germany. Topics and workshops covered a range of topics surrounding (big) data analysis and Machine Learning, like Deep Learning, Reinforcement Learning, TensorFlow applications, etc. Distributed systems and scalability were a major part of a lot of the talks as well, reflecting the growing desire to build bigger and more complex models that can’t (or would take too long to) run on a single computer.
Today, I have given a webinar for the Applied Epidemiology Didactic of the University of Wisconsin - Madison titled “From Biology to Industry. A Blogger’s Journey to Data Science.” I talked about how blogging about R and Data Science helped me become a Data Scientist. I also gave a short introduction to Machine Learning, Big Data and Neural Networks. My slides can be found here: https://www.slideshare.net/ShirinGlander/from-biology-to-industry-a-bloggers-journey-to-data-science
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.
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