Traineeship Data Science with Erik

Hello, I’m Erik, I work at CB as a member of a data science team who work towards predicting the future. I focus on Natural Language Processing and make data-driven predictions on a product’s pre-destined performance in the market. When I am not predicting the future, I am growing it: cultivating oyster mushrooms from coffee grounds at my urban farm, Fungi Factory.

My studies specialized in sustainable business models and innovation, and I was approached by Young Mavericks in the finale stage of my master’s programme. I finished my Master’s degree at KLM, where we analyzed total ticket sales. I found that playing with large amounts of data, getting them to answer the right questions, was fun and interesting, so from there I took the course Data Learning.

Starting at Young Mavericks

I came to Young Mavericks with a keen interest in data, I was considering a traineeship and I was sold after I considering the benefits of joining a start-up with young energetic talent. I learned a lot from the training courses. The sessions were a great introduction to machine learning and helped me gain extensive insight into the various machine learning tools and processes. In addition, I learned a lot about dealing with and tackling specific problems that can arise during a project.

Working at the partner

I was a little nervous for my interview at CB, but I was pleasantly surprised by how smoothly the conversation went. I started at CB in a data science team where we were given a lot of freedom and space to discover what we could do for certain CB data. Our first assignment gave us three months to demonstrate what we could do with the data. We have already accomplished various assignments as a team. One example had us building a keyword generator that could automatically identify keywords to describe the book mined from its digital content.

The value of Young Mavericks’ training

I can say with certainty that I have developed my analytical skills during my time with Young Mavericks; I now have better insight into defining value and letting the rest go. I am not afraid of making mistakes, in fact, it is better to make mistakes, and as soon as possible, because I then learn from those mistakes sooner. It was an exploration of trial and error, by trying as much as possible I learned a lot more than I would have otherwise and could take big steps quickly.

What does it take to be a successful data scientist?

The first step to becoming a successful data scientist is to have an affinity for data and numbers, if not, it might not be your gig. You have to like trying new things, getting into an adventure every time, with an attitude of “I’ve never done anything like this before, let me give it a try”. You have to feel an unceasing gluttony for new things and energized from embracing the most recent developments.

After Young Mavericks

As Young Mavericks keeps re-inventing themselves, the training sessions get better with every class. This is essential for an organization to keep up with the times, especially in the field of data science.

Young Mavericks Behind the Scenes: Interview with Trainer Jelmer

One year ago, Jelmer van Nuss joined the Young Mavericks team to provide training to Data Engineers trainees and to help coordinate the content of guest lectors. “Recently I also have been involved in developing new assignments for the Data Engineers.” What made him join Young Mavericks initially and how does he look back on his first year? Read all about Jelmer’s experiences at Young Mavericks.

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Young Mavericks Helps Automate Dutch Customs and Border Protection

Today, companies can no longer evade the increasing automation trend. The automation revolution has not just touched commercial companies, but government agencies like Customs and Border Protections (CBP) who must now also keep up with in data technology advancements. In cooperation with Taxsample and TB Customs Consultancy, Young Mavericks has developed a data tool to meet a range of CBP challenges – from growing commodities trade, increasingly complicated importation and exportation processes, new import duties, trade-restricting measures to the possible No-Deal Brexit scenario.

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