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In the third semester of the Master in Applied Data Science, the Company Cooperation Project is a mandatory module in which a group of four to nine students, depending on the project scope, are asked to solve a company’s data science problem.
For three months, we had the opportunity to work for Moody’s Analytics within an FS company cooperation project. The project goal was to build a domain-specific translation from Chinese to English for accounting terms. In order to be able to accomplish this task, we were given a dataset in Chinese and its corresponding English version. Although the project goal did not sound impossible to fulfil, we already faced first difficulties by making use of the provided dataset. Of course, we are well prepared for such tasks, as the Master in Applied Data Science is focused on the application of Machine Learning Algorithms on actual data for solving real-life problems. Nevertheless, there is always a difference between use cases or assignments and a project, in which one must keep in mind the stakeholder’s expectations on the outcome.
Once we found a way to classify the documents into English and Chinese ones, we were looking for a way to pair the English and Chinese accounting terms to create a dictionary. These main functions of our code challenged us to work creatively and efficiently. We had to consider everything we had learned during our master’s programme. This project made us realise, that it is not only about applying smart algorithms, but mostly about preparing data efficiently, understanding the data, and thinking further to work towards the best solution we could possibly offer our project’s sponsor.
I think this Company Cooporation Project was insightful and very useful for my future career, as I was able to understand the challenges companies face and to identify scenarios in which data science can be usefully applied. In addition to the scope of the project, I enjoyed working in an international group with my fellow students in which everyone had a different educational and professional background with diverse approaches to solve such tasks. The variety of smart and creative ideas bundled into the final result of our project, pleased not only us, but also satisfied our stakeholders.
This outlook on the professional challenges of a Data Scientist makes me expectant and curious about my profession after graduation.
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