Last weekend I’ve participated in an event taking place at the data school, called DataDive.

The event was organised by DataKind, a non-profit organisation which aims to help other charities using data.

To do so they recruit volunteers which participate in a Hackathon style event, where they clean, analyse and visualise data.

This particular event was organised to help the charity guys and at Thomas, a charity which aims to improve the health of people in the London boroughs of Lambeth and Southwark.

The event started with a presentation that explained what the charity expected as a project outcome. Soon after that, we divided into different groups, each with different tasks.

The event had a great turnout of volunteers, mostly composed of experienced people in the data science field. Most of them were really experienced in coding languages such as Phyton and R, and they carried out their analysis coding with these tools.

Due to this, I felt quite out of place at the beginning of the session, all these people seemed to be way more able to deliver content then me. On top of that, I was one of the few to use tools like Tableau and Alteryx as opposed to coding. This didn’t help to start conversations since everyone was using a different jargon then me.

Therefore I decided to produce something that could be useful to the entire group, in order to show the potentiality of our tools. After hearing that many participants found the process of transposing the data lengthy (the main file had over 300 columns), I started to build an Alteryx analytical app that would take care of that dynamically. 

After finishing it, I have presented it to my group, which was really impressed by the potentiality of Alteryx, and I started to create different outputs to tailor their needs.

This helped me to start various conversation about the potentiality of the tool we use, and I was positively surprised to notice how even hardcore coders were praising the speed of execution and UI of Alteryx.

Later on, when most of the data prep seemed done, I’ve started to produce dashboards in Tableau. While doing this, I grabbed the attention of many participants, which were blown away by the simplicity and speed of execution of Tableau.

Some of them asked me to visualise their complex mathematical calculation coded in Python. This really made my day. It was such a good feeling being in a room full of experts in this field without feeling out of place.

I believe that many times us DSers tend to feel to be ‘average’ in our data skills. This experience really helped me to understand that we couldn’t be more wrong. In fact, what I learned at the data school in these last 4 months has proven to be a solid foundation to kickstart my career in this field.

There is, of course still a lot to learn, but being exposed to this experience gave me a great boost of confidence in my abilities. This will certainly help me to start my first placement with a positive and excited attitude.