Graduated in April 2024 with the Petrol Raccoons cohort

I studied applied linguistics and translation, made some experience in customer service during university as well (student job) and worked for 5 years as Knowledge Engineer/Annotator where I was preparing data for NLP and ML purposes, doing a lot of QA and setting up annotation specific tools. Because I wanted to dig further into the technical part of working with data and at the same time break out of the niche I put myself in with my working experience I decided to join the bootcamp.

What are the reasons you chose Code Academy Berlin?

The bootcamp was recommended by a friend. I especially liked that it is on-site and full time. The curriculum and project based learning also were a big advantage.

About the Data Analytics course

The CAB program is a full-time, intense, mostly self-taught learning program. There are mentors who are there to give you introductory lectures (so called spikes) to the students and help them if they get stuck or want to additionally discuss any of the topics. The program lasts up to 5 months, starting with a few weeks of introduction to the major tools like Tableau, Git, Python and basic statistics. Right after that the project based learning starts, where the students learn to clean the data, explore and visualise it. During that time they learn additional libraries like Pandas and Numpy for data handling, machine learning with Scikit-learn, Seaborn and Plotly express for visualisation and the query language SQLite to build and manage databases. There are daily stand up meetings and the projects results are shown in the form of presentations with a strong focus on storytelling. Each project is unique, represents a new domain with its data and focuses on learning a new aspect of data analysis, but also builds up on the knowledge from the projects finished before.

About your learning experience

In general, the longer I was attending the course the more challenging it was, but at the same time satisfaction and understanding of the topic grew. In the beginning I was on one hand quite confident, because I had already some basic experience with Python and was a bit ahead thanks to it, but at the same time I was a bit worried that most of the time we are left to ourselves. That actually turned later out to be quite an advantage, because in the end, after completing several projects it gave me confidence in my ability to work independent with new technologies. The projects were quite diversified and each time introduced something new, building on top of what we learned in the last one. I personally enjoyed all of them in the beginning, but sometimes had my lows while being paralyzed by taking the decision on what direction my analysis should go in. The EDA and data preprocessing part was usually the most calm and enjoyable for me. Finishing the project up and plotting all the data was probably the most satisfying. The experience was also positively enhanced by the fact, that we all worked on the same data and could exchange ideas and discoveries with peers, which by itself is a valuable skill. My favorites projects were the ones towards the end. The e-commerce project that revolved around designing and building our own SQLite database and my web-scraping project, where I created a data pipeline that was on the cloud and would run in regular intervals without my intervention. It was exciting to a point, where I happily worked into the night figuring out how to make my idea fit the tools available. The learning curve was in my opinion quite logical and not too steep, although it would make sense to combine some of the sprints in the beginning, because only after reading both of them their content was clearer.

Project Showcase


Python scripts designed to scrape WG-Gesucht, a popular platform for shared housing listings, to gather information about available rooms or apartments in Berlin.

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