As I reached the Friday of my first week at The Data School, we were assigned our first “mock” client project and our first real opportunity to put everything we had learned into practice. Going into that first project can feel daunting especially as your coaches (now acting as clients) remain tight lipped about key details throughout the week. Looking back here are some of the key lessons I learned and the recommendations I would give to anyone about to tackle their first Friday project.
Lessons Learned
Read, Read, Read
It may sound obvious but when you first receive the project brief it can feel overwhelming to take everything in at once. Chances are, you won’t absorb it all on the first read, I certainly didn’t. After heading in the wrong direction once or twice during my first project, I quickly learned the value of re-reading the brief whenever starting a new part of my project, just to make sure I wasn’t sending myself on a wild goose chase. Writing out key points from the brief when you first receive it can also help cement the objectives in your mind.
Ask questions early on
At the beginning of my project, I found it difficult to clearly separate my coaches from my “clients.” As a result, I delayed asking key questions around background and objectives until I had already started building things. While luckily I had been heading in the right direction, it would have been easy to waste valuable time or have to scrap work entirely. Asking clear questions early on can help you understand what success looks like for your client, so gather your courage and ask them as soon as possible.
Live with the uncertainty
This is something I have often struggled with in the past: being given a task without knowing what the final outcome should look like. The uncertainty can be very stressful, but I try and remind myself that no one expects you to know what you haven’t been taught yet. While vague client requirements can feel uncomfortable, they also give you the freedom to explore ideas, try new approaches, and challenge yourself in ways a rigid brief might not. Learning to sit with uncertainty is a skill in itself, and one that I’ve found to be increasingly valuable.
Recommendations
Take the time to plan
You may feel like you don’t have the time to plan out everything, but even making a rough plan at the start can save you a world of pain later on. Having a loose structure can help prevent you from spending too much time perfecting one area of the project while neglecting others as your deadline quickly approaches. So while you might not think you have the time to plan, I promise it will make up for itself in 10-fold down the line
Believe in yourself and what you are saying
One of the key points of feedback I received after my first presentation was to sound more confident in my recommendations. Phrasing suggestions as “this is something you could maybe do” feels very different from saying “I recommend this because…”. While both communicate the same idea, the latter is far more likely to be implemented by a client. Even the best solution will fall flat if you don’t believe in it yourself, so present your ideas clearly, confidently, and with conviction.
“There’s no such thing as stupid questions”
This is something you will hear countless times during your first week at The Data School, yet it can still be difficult to fully embrace. It’s easy to worry about appearing less knowledgeable than your peers, but not asking questions is far more likely to hold you back. If you’re stuck or unsure, ask. Whether that’s your coaches, your cohort, or someone else at TIL, everyone is incredibly supportive and genuinely wants to help, even if that means pointing you to someone who knows the answer better.
Final Thoughts
Your first Friday project is as much about learning how to work with ambiguity and feedback as it is about technical skills. It won’t be perfect, and it’s not meant to be. Treat it as an opportunity to experiment, ask questions, and build confidence, and you’ll come away with lessons that will carry through the rest of your time at The Data School and beyond.