Originally hosted on the Growth Tribe website (which has since undergone several updates.) The initial interview with the client was conducted by me, as well as writing up the case study.
Turning data skills into career progression with Growth Tribe
Daniel has a technical background. A trained engineer, he travelled the world learning new languages and working technical jobs. A lot of people might assume that someone like Daniel would have cutting edge data skills, but in reality, data analytics hadn’t been a big part of Daniel’s work at all before finding Growth Tribe.
We asked Daniel what made him interested in becoming more data-driven. Through small data tasks in his more technical jobs (like robotics 🤖) he started to realise he had an affinity for data.
“There were small data-related tasks in my work, but not something I worked on all the time. I decided to take this step towards data because it's what I liked and what I realised I was good at.”
Daniel saw Growth Tribe pop up on LinkedIn, and decided to take the plunge after multiple friends who had taken GT courses recommended it to him.
“Nowadays you can find any training and all the information you want on the internet, but most of the time, it’s too overwhelming. What you actually need is really concise information and to be able to say: Okay, this is what I need to know.”
Since Daniel already knew he liked working with data, learning with Growth Tribe helped him really understand what data skills can mean for career development.
“In the end, data alone has no value but the insights are the valuable stuff. I like doing a good job and having things done right. That's why having the skills to really understand what is going on through data is helpful.”
Growth Tribe’s curriculum was what originally attracted Daniel, but once his learning journey started, he also began to appreciate the less technical aspects of the course, like the trainers:
“I really liked how the trainers combined different areas of expertise. Some people were really technical, some were more people-oriented. In the end, I think this is what you actually need in data: to have different people from different backgrounds to learn from all of them.”