Hi everyone, my name is Luiza and I've just finished my first week at the Data School. I'm part of Cohort 48, which started on December 2nd, and having finished my first week - I thought to write a blog and give a run down for any new Data Schoolers who are eager to know what goes down during the first week. Firstly, if you’re due to start, Congratulations! And if you’re thinking of applying, I highly encourage you to because this is one of the best opportunities you’ll probably ever come across.
Now for a glimpse of what the first week has been like.
Monday
The first day was a very exciting day. I was able to attend 2 TIL/DS events before starting - a meet and greet and the Christmas Party. This calmed my nerves and actually made me feel excited to start and see everyone I had met, because everyone here is super friendly and welcoming. The day was split up into different sections, where the first session was a brief introduction to my Coach and the rest of my cohort. We were then introduced to the CFO of the company who gave us a detailed run-down of who TIL, their structure and the overall services we provide as a company. Then, we had 2 Data Schoolers from different cohorts come in and talk to us about how to make the best of our time at the Data School and give us some tips and advice for the upcoming 4 months of training. After lunch, we met with Craig who helped us get set up and on to all of TIL’s socials, servers and software. It definitely felt like a heaps of information to retain, but after a good night’s sleep, everything felt much easier and intuitive to use the next day!
Tuesday
This was the first day of training. Tuesday covered a lot of data terminology, structures, and architecture, which left my brain pretty fried by the end of it (the coaches say this is a good thing). I will say though, I loved it. I learnt a whole lot of different concepts about data which were relatively new to me. After lunch, we had a much more chill session on discussing how to ask the right questions to answer a client’s data problems. Ruth, our coach, also emphasised the importance of documentation and planning during client projects, which ensures clear communication, maintains consistency across projects, and provides a structured approach for analysing data, ultimately saving time and making sure the project is deliverable within the specified time frame. It was a nice and eye-opening session, pushing me to think outside the box in a way I hadn’t considered before.
Wednesday - Thursday
These 2 days is where it got a little intense. Wednesday is where we learnt more terminology about data rules, structures and pivoting, and Thursday is where we put the knowledge into action. We used Carl’s Data Preppin’ challenges to consolidate our new-found knowledge, and even attempted challenges prior to learning how to do it, just to challenge our brains a little more. This was honestly super fun because not only did it involve applying the theoretical concepts learnt Wednesday into an actual data cleaning software, but also because we first tried to figure out the software before being shown how to actually do it. Figuring out how to do this felt super rewarding and the whole learning process was really enriching. Of course, there was moments where we were all really stuck, but Carl helped us through the process and explained everything so well, so by the end of it, the session felt like a breeze. Thursday, we also took our Data School pictures - I'm looking forward to uploading them on all my social medias.
Friday
Today, Friday, was the day we got to apply all the information we were taught this week into practice. We received our first practice project, where we had to plan and document our process meticulously for the next consultant to pick up and complete everything, and clean and prepare a data set for analysis. Ironically, the most difficult part of the process was the planning and documentation. It was difficult because I wasn't used to documenting everything as I went along, so I thought taking notes, which I could type up later, would help. But by the end of the data prep session, I forgot what my notes meant... So I had to go back into Tableau Prep and retrace my steps, and rebuild my documentation after I finished cleaning my data, as opposed to doing it during. It caused a little time constraint, so definitely a lesson learnt for next week's project!
Final thoughts
Whilst this week was a little challenging, it was super fun and enriching. Before my first week at The Data School, I assumed that being a data analyst was mostly about crunching numbers and writing code for large datasets, however one of the first things I’ve learnt is that while numbers and code are a part of the job, a bigger part is interpreting the data and asking the right questions to make sure we deliver our clients what they need. I also underestimated how important planning and documentation is in this role, as having seen for myself on Friday, without detailed planning, documentation and time management, projects can become super stressful.