Student Eeli Susan: Signal Processing and Data Science is a perfect blend of theoretical and practical courses

'Strong theoretical background with plenty of practical course project experience is exactly the type of experience that many companies are looking for,' says Eeli Susan.
Student Eeli Susan
Eeli Susan thinks that the major in Signal Processing and Data Science opens many job opportunities.

Once student Eeli Susan understood that nearly anything can be considered a signal, the field in Signal Processing and Data Science became an obvious choice. He thinks that diverse course selection is one of the main strengths of the programme.

Why did you choose the major in Signal Processing and Data Science?

Signal Processing and Data Science was a very natural progression from the Bachelor’s degree in Information technology (in Finnish). Once I understood that nearly anything can be considered a signal (e.g., an image, a recording, even a molecule) this field became quite an obvious choice. Having the skills to solve practical problems from a multitude of fields, from self driving cars to MRI scans, felt extremely intriguing.

Now, having done all the courses I feel like this major was a perfect blend of theoretical and practical courses with its various topics such as computer vision, speech recognition, medical imaging, and deep learning. That was at least how I chose everything. The large course selection really allows customisation to ones own interests. There would have been plenty of options to take more telecommunication, networking, or programming courses for example. This was also one of the reasons why it was easy to choose this Masters’ programme specifically. I did not need to be certain about which field/industry I’d focus on immediately.

What has been the best part of your studies?

As mentioned earlier, having such a broad selection of themes and practical applications was something I enjoyed immensely. For example, theory learned in a speech recognition could all of a sudden be applied in computer vision. Finding the connections between the various fields gave my studies a lot of cohesion and the confidence to tackle signals of any type.

What has been the most interesting course?

It is very difficult to choose the most interesting course. To mention one, AI in health technologies D provided a broad overview various artificial intelligence applications in the medical domain. The course included a project with topics such as skin cancer classification from images, or heart attack detection from ECG, to mention a few. Having already done multiple theory focused courses such as Deep Learning D gave the practical project works a lot of tools to work with. I ended up working on the generation of synthetic ECG signals, which could in theory be used to improve the detection of cardiac arrest. Having experience of managing everything from data collection to finding appropriate models and methods is invaluable experience for any future career in this field.

How does the future of the field look like?

The Signal Processing and Data Science Master's provides excellent job opportunities in my opinion. Having a strong theoretical background with plenty of practical course project experience is exactly the type of experience that many companies are looking for. Course projects such as those in AI in health technologies D, Speech Recognition D, or Signal Processing for Communications are all excellent lessons of bringing theory into practice; something the industry is constantly facing. These are the courses where you realise that real world problems include a lot more challenges than the theory alone.

Read more about the programme and Finland


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Sähkötekniikan korkeakoulun opiskelijat. Kuva: Unto Rautio

Signal Processing and Data Science - Computer, Communication and Information Sciences, Master of Science (Technology)

In the Signal Processing and Data Science major, you will learn to extract useful information, discover patterns, and make predictions from large amounts of signal or data. You get to apply your knowledge to the physical world – making devices, systems and entities smarter and more environmentally friendly.

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