Internship

Course goals

Learning Goals
The fundamental learning goal is to develop the ability to apply the theoretical knowledge and technical skills that have been acquired in the programme to real-world challenges through industrial and research partners. Furthermore, it aims at helping students improve their problem-solving abilities, and at the same time, gaining valuable insights about data-driven organisations, developing professional networks, and acquiring knowledge in practical fields. 
The internship offers industry supervisors the opportunity to train the students on their products and witness firsthand their abilities, which in turn may lead them to become a potential future employer for the students.
 
At the end of the course, the students will have learned to: 
   • work effectively in an industrial environment 
   • transform practical problems into research challenges
   • make use of the knowledge acquired in the programme courses in real-world scenarios 
   • perform research and practical work under limited supervision
   • perform the necessary studies (experimental evaluations or literature reviews) to guarantee the quality of the delivered work 
   • manage data in an efficient, effective, and responsible way
 
Evaluation
At the end of the internship, the student delivers a report (up to five pages) that describes the work performed and the results achieved. The report should include the contact information of the company supervisor (name, role, affiliation, email, address). The company supervisor may be asked for feedback on the internship collaboration.  It should also include information on what the student has learned (both technical and non-technical), and how the work performed is related to Data Science. The report forms the basis of the evaluation of the course, which is pass/fail. The pass is when the work performed is substantial to correspond to the work of the course ECTS, and is related to Data Science. In particular, the criteria are: 
Does the problem constitute an industrial/societal challenge and is this work well motivated? Why would anyone have an interest in this work? 
Is the problem scientifically challenging? Why is it not a simple software exercise or some straightforward study? 
Is the result of the problem adequate for a work of such duration? 
Is the tackled theme clearly shown to be related to Data Science?
Has the work shown an adequate degree of autonomy, independent thinking, writing, and execution?
Has the work demonstrated a critical and reflective attitude, integrity, and responsibility? 
Is the document well-structured, and the English language of scientific quality? Does it flow well with the necessary layouts (figures and tables)?
Are the necessary references included? 

Content

Aim
An internship is a fundamental component of the Master’s in Data Science. It reinforces the relevance of their academic knowledge to the industrial and societal demands and bridges the gap between the academic training and the professional practice. At the same time, it serves as a means to establish connections with industry, broaden the network of professional associations. 

Procedure 
The student needs to find an external company (research groups from Utrecht University are not considered external companies), a daily supervisor from within that company, and have an internship agreement signed between the company and the UU. The internship has to be approved by the internship coordinator, who is the instructor of this course. 
 
The internship course is different from an internship performed in the context of a thesis. Its topic and work cannot be included or partially overlapping with the work used to fulfil the thesis requirements. It can, however, be on a similar or highly related topic, can be work performed before the thesis (like preparatory work), or can be an extension (follow-up) of the work of the thesis. In that sense, the internship offers a useful tool for students to work beyond the topic of their thesis, for example, extending that work for publication, or improving a prototype to become production-ready.