Data Science & Entrepreneurship 101 for Master Students — 1st Semester

Agis Georgiou
Sep 6, 2018 · 5 min read

This article is a continuation of a previous article by Roy Klaasse Bos. Have a look at that article for general information about for example housing or some tips and tricks for the premaster courses. In this article I will focus on the courses of the first semester of the master program. Take into account though that this is a description of the courses as they were delivered last year (2017/2018). Hence, course mechanics might have slightly changed. Nevertheless, I hope you find it helpful! Finally I am collected material from the previous semester and will share it here when available,stay tuned for updates !

Data Entrepreneurship in Action I (JM0120)

The key idea of the Entrepreneurship in Action series is to solve real business problems in close collaboration with industry partners. Last year we worked on a case from CB, a logistics company mainly involved in book distribution. Each team will be assigned a specific role, for example new business creation or customer satisfaction, and you will have to come up with an idea on how to deliver a data science solution based on the data they supplied you with.

During the first lecture, there will be a general introduction to the course and you will play a fun game. The next lecture introduces the lean startup principles (believe me, you will hear that term a lot during your masters!). In the third and last lecture the company case is introduced, and from then on, you will be the ones presenting!

Furthermore, there won’t be any weekly classes any longer. Instead, there will be bi-weekly progress meetings where your team pitches the preliminary solution to the professors and company representatives.

Exam Type: No written exam, instead you will be graded on your deliverables (bi-weekly reports, pitch, midterm presentation, final presentation, final report, individual reflection report).

Important notes: If I could give you one tip for this course it would be to ask good questions. Bas, one of the professors of the course, will quietly keep track of your involvement and you will even receive a grade for it! Also, I would recommend coming up with a business idea better sooner . Following the lean principles, a good way to do that is to use the input of the company as a starting point, rather than try to think of a creative idea yourself.

Data Engineering (JM0140)

Each lab covers the content of the previous lecture. It is important to install the necessary software beforehand as it can take quite a while, and as always with software: never everything works as expected.

Exam Type: 2 sets of group assignments (40%) and a written exam (60%).

You will have two group assignments. The first one about database systems (SQL, ER and No-SQL) and the second about Spark. The corresponding deadlines are early November and early December respectively.

Important notes: The course material is huge, and although you’re not expected to know everything by heart, you need to have a rough understanding of the concepts and how it all fits together. That’s why I would recommend reading the dedicated book chapters early on. Finally, expect a lot of frustration from (re)installing software.

As a tip for the exam, last year most of the questions involved topics from the the second part of the course (data integration and batch large data processing).

Data Mining (JM0150)

Exam Type : 2 sets of group assignments (50%) and a written exam (50%).

In general, every week another group exercise is published of which the solutions need to be handed in as 2 sets. The first set around the end of October and the second one early December. Most of the questions can be answered using nothing more than pen and paper, as the focus is on understanding the concepts.

In the final exam there is one question for every lecture. In order to help you prepare for the exam, the lecturer posts a set of representative questions well in advance.

Important notes: Remember to bring a calculator with you during the exams, you will need it…

Intellectual Property & Privacy (JM0160)

Exam Type : 2 individual assignments (30%) & Exam (70%)

Each of the group assignments makes up 15% of your total grade. The first one is about the GDPR and you will have to asses a use case in about 2.500 words. The second one is again a use case but this time about IP rights.

The final exam comprises three parts: 1) course definitions, 2) use case for IP and 3) use case for privacy.

Important notes: Presence is mandatory and is almost always checked.

Strategy and Business Models (JM0170)

Exam Type: 6 intermediate tests (45%) group assignment (55%).
Once in every two weeks your understanding of the prescribed literature will be examined (e.g. ‘What did the writer mean with the statement X’). Most questions follow a multiple choice format, but keep in mind that there can be open ended ones too (e.g. ‘Name and describe an example of indirect network effects’).

In the group assignment you investigate a relationship related to one of the course topics given a company dataset (e.g. Apple Store). For example, my team looked into the effect of different revenue models on app adoption.

Important notes: Don’t underestimate the time it takes to prepare for the tests (it took me between 8 and 10 hours before each session!).

Summaries: here you can find the previous semester article summaries (credits to Colin!).

Hopefully I made your life just a little bit easier. Good luck with your studies!

The Outlier

Deep Data Science Stories of Academics, Entrepreneurs and Students by D.S.A. Pattern, the study association for all Data Science students at Eindhoven University of Technology (TU/e), Tilburg University (TiU) and the Jheronimus Academy of Data Science (JADS).

Thanks to Roy Klaasse Bos

Agis Georgiou

Written by

Data Scientist | A.I. | Real Estate | Entrepreneurship | Business LinkedIn :

The Outlier

Deep Data Science Stories of Academics, Entrepreneurs and Students by D.S.A. Pattern, the study association for all Data Science students at Eindhoven University of Technology (TU/e), Tilburg University (TiU) and the Jheronimus Academy of Data Science (JADS).

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