Interview with former Data Science student, David Furrer
Hi David. Could you introduce yourself? What were you doing before you joined SIT Academy’s Data Science Bootcamp?
“Hi, my name is David Furrer. I originally studied chemistry and then I worked at Novartis for some time. After I studied economics, I started looking into modeling and statistics. During this time, back in 2019, I discovered SIT and decided to do the Data Science Bootcamp to get a more in depth view on these topics.”
What topic did you enjoy studying the most and what did you like about SIT in general?
“The broad overview of everything that exists at this time in this area. At the beginning I didn't know exactly what to expect. So it was a great chance to get to know all these technologies like web scraping, basic data analysis with Python and then advanced topics like neural networks and NLP. The social aspect really appealed to me. We still have a very good network of all the students and we regularly exchange news and updates. We can help each other out, for example if there's somebody looking for a job. Having this group of people that also work in this field is very helpful.”
What was your capstone project about?
“It was in collaboration with a job cloud from the company jobs.ch. We did a project with them where we tried to recommend improvements for the titles that people post for the job ads. We analyzed what features improve the click-through rate for a job title and we built a model that predicted how good a title was based on the texts and the features that were extracted from that text. We then also recommended alternatives on how to improve that title. The end result was a prototype for an app where people could type in keywords to search for jobs. The app itself was a bokeh app and the model behind it was based on NLP.”
What did you do after the Bootcamp? Was it easy to find a job?
“After the Bootcamp I had a break of three weeks and then I started an internship at an AI startup called OTO.ai whose core business involves emotion modeling based on voice input, so MP3s WAV files. From this, the emotions of this call or this audio file can be extracted. My job involved modeling the satisfaction of call center calls where we did a lot of small plcs or proof of concepts for potential customers. The goal was to predict the satisfaction score of a given call for which we had labels from the customers themselves.”
Do you have any advice for people who also want to do a Data Science Bootcamp?
“One thing is to use the network you build during the course, because in my case it was very helpful. SIT Academy connected me with the CTO of OTO.ai and this is how I got my first interview. Another piece of advice is to not only look for Data Scientist positions, but also Data Analyst positions. There is a lot of overlap between these two jobs. I would also say that the project phase at the end of the Bootcamp is crucial. Make the most of it to really polish it up and put it on GitHub in order to have a nice portfolio to show during the application process.”
Interested in reading more about Constructor Learning and tech related topics? Then check out our other blog posts.