How to Build a Career in Data Science in Germany even if you are not an Engineer?

Data drives almost every industry in today’s technological world. As a result, making sense of data is vital for nearly all organizations. This has led to an increased number of students aspiring to become Data Scientists at good organizations. Furthermore, because Data Science is an interdisciplinary field that uses different analytical and logical techniques and procedures to extract and analyze information, it is a field open to one and all.

To an outsider, it may seem like you need to be a computer science engineer, or at least an engineer, to build a career in Data Science. However, that is a myth. It is beneficial if you are from a CS/IT background, but that’s not what decides whether or not you get to have a career as a Data Scientist.

In this article, we will explore how you can become a Data Scientist without an Engineering background, and secondly, How to achieve this feat in one of the best places and study abroad in Germany!

Career in Data Science in Germany

Becoming a Data Scientist without an Engineering background

It is a widespread misunderstanding that Data Science is for people from only engineering backgrounds. Though some of the engineers indeed decide to pursue a career in Data Science, that is not it. Many successful Data Scientists in the world did not start with coding or programming.

So, if you are from a non-technical background wishing to pursue a career in Data Science in Germany, here are some things for you to get you started:

1. Upskill with an updated and curated curriculum

Data Science is a dynamic field – simply because it is relatively new and includes various distinct elements. As a result, if you are just beginning with your Data Science journey, you need to get a hold of an updated curriculum. You can look at online courses or YouTube infotainment videos to get some idea and then pursue formal education. It is essential to do this in a structured manner, otherwise, it will soon become chaotic.

We understand that curating resources from virtually unlimited resources online is a time taking the task, especially if you’re a beginner. We understand that – so if you are struggling with finding resources, drop us a comment below, and we will get back to you!

2. Get hands-on skills

Data Science is all about doing and less about theory. While you will need to know the essential theoretical concepts, at the end of the day, you’ll be working on some other project. So, to get a flavor of how it actually rolls in the Data Science field, you can either start working on a real-life project using the skills you’ve acquired, or, if you are not confident enough, you can audit a real-life Data Science project closely, and understand the workflow. If you do this enough, you will be able to figure out things on your own and will eventually be able to execute a real-life project on your own.

A portfolio of real-life projects will also go a long way in helping you get into better colleges abroad and also find jobs there. When you are working on real-life projects, look to tackle substantive problems, and try to come up with solutions based on exploring and analyzing various sets of data.

Try to explore questions like – is it possible to predict election results based on voting trends? Is it possible to trace a cricketer’s performance to tweets of them partying the night before? You can analyze anything – all you need to do is get started!

Protip: If you cannot find datasets, check out dataset search engines like Quandl. Platforms like Kaggle, Datakind, and Datadriven are also useful in getting a hands-on experience of working with real-world data.

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3. Be an active part of Data Science communities

Community plays an important role – especially during the initial phases of your career. It is advised to be a part of various Data Science communities on different platforms like Reddit, Kaggle, Facebook, LinkedIn, and more. Interact with the posts, try to solve any problems they post, and even post any issues that you might have. Build relations and networks in the Data Science domain. This will also keep you closer to any job openings or Data Science requirements.

4. Attend Data Science events, watch lectures and podcasts

To really immerse yourself into all the happenings in the Data Science world, follow various physical and online events and conferences – you might find things that will spark your attention. Further, you can explore podcasts and lecture series on Data Science to acquire more knowledge and information.

There are three large conferences that you can look to follow – Strata Conference, KDD (Knowledge Discovery in Data Science), and NIPS (Neural Information Processing Systems). Many Data Scientists professionals and aspirants across the globe attend this conference. You will learn about various technical concepts and gain a new perspective on things and problems while attending these conferences.

5. Find a mentor in the field

Navigating the various nuances of Data Science is difficult, especially if you are a fresher coming from a non-technical background. To make sure it does not get too overwhelming for you, you can find a Data Science professional and consult them as a mentor. This can be done by being active on various platforms and groups, meeting up with different Data Scientists, attending conferences, and more.

All in all, definitely ensure that you are active in the Data Science community so that you have a few people looking out for you. This can really make the difference between finding a Data Science job and working your first day as a Data Scientist.

6. Prepare hard for the interview

Interviews are challenging for everybody – but it is pretty confusing if you are from a non-technical background. Data Science interviews tend to include elements of software engineering, statistics, data analysis, data warehousing, mathematics, and more. You do not need to be an expert at everything to crack the interview, but you need to have a working knowledge of it all.

You might even be asked about why did you switch from a non-tech to a Data Science field, and what is your opinion about the Data Science field – what more can you bring to the table, and more. You must anticipate these questions and prepare well for these. Answering these without preparation, being put suddenly on the spot, might get difficult – so make sure to have some pointers written for some key questions you think you will definitely be asked.

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