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Applied Data Science

Are you triggered by what data can do for the world we live in & are you willing to learn?

So what is that data can do for the world live in? Medical specialists f.e. use data science, to predict a person’s health based on decades of experience and historical data. In aviation data is crucial to ensure passengers get on board on time and to predict when maintenance is needed for the most effective use of the airplanes. But data is also near you. Think about Siri, Chatbots, Tesla or Google Maps. These services are used daily, most likely also by you, and are based on years of data processing and machine learning.

We have created the ultimate conditions to experience and learn Applied Data Science in real life challenges. The entire minor we apply a structured process for data solutions by using the “Cross Industry Standard Process for Data Mining (CRISP-DM)”, known for understanding the challenge and/or problem, setting realistic data mining goals and reserving the time which is actually needed for data preparation. All in order to achieve an iterative process to reach data mining goals and further improving the machine learning models.

Leerdoelen

Topics you will work on In this minor you will learn how to set up, carry out and motivate practically-orientated research. Partly by following instructions (lectures) but above all, by carrying out your own research project. You will be supported by experienced researchers and process-supporting lecturers. Instructions will be given in a way that optimally support the research that you carry out.

During the minor you will learn about (Advanced) Machine Learning & Deep learning. Most people know about (linear) regression models. In this minor you will be introduced to more advanced models, ranging from ensemble models, such as random forest to gradient boosting. Which could be applied in in predicting remaining “useful life” of infrastructural assets like a lock. For challenges with object recognition with a huge amount of images, you will be introduced to use “Deep Learning” to train your model.

Also note that many learning goals are optional: you have the freedom to choose the ones that match your research. The context of your research will be a real-life problem so the context of your research will be determined on demands, based on the needs in the work field when you start your minor

Aanvullende informatie

Practical details

Location: HZ (Middelburg)

Language: English - depending on the project Dutch is possible as well

Ingangseisen

Participation requirements Technical domain: HBO-ICT

To participate in this minor, you must have completed your first year. You have a more than average interest in Data Science. Furthermore you have experience with the CRISP-DM Methodology and basis programming skills, at least Python, you may be asked to show proof of your python knowledge.

Toetsing

Min. grade: 5,5