Doubling also as my introduction to both The Data School and to publishing blogs more widely, this post will act as a summary of my first week and highlight the most critical lessons I’ve learnt so far. Being a recent STEM graduate my perspective is likely most useful to others with that background. Having experience with Python before I assumed working with Alteryx for data processing would come naturally, but that I might struggle when it came to making dashboard designs and presenting them.
The first week was an introduction to Alteryx. Having had coding experience through my degree, the terms and procedural elements of the program were intuitive and clear. The ways in which tools are used and interact with each-other made sense even if the specifics of exactly which tool to apply for different scenarios will take some time to develop. Most importantly this week has taught me not to take my prior knowledge for granted or make assumptions on how tools and syntaxes work without investigating further.
Our Alteryx skills were put to the test on our first project day on Friday, also requiring us to apply what knowledge of dashboard design we had before presenting our work to a collective of previous Data Schoolers. In practice I found that recognizing when a process could be solved in Alteryx came slower than I had hoped. Under the time constraints this led to a rather puzzling solution - importing two very similar datasets into Tableau separately instead of including an identifying field on each and combining them before exporting to Tableau. However, that intuition takes time and experience to build up, so I will have to make good use of the Alteryx Challenge Index to help with that.
When the time came to present our work, I was given the dubious honour of going first. Having to explain my work to a dozen more people than I had expected was tough, but I certainly learnt some things from the experience! In future I need to make sure I include a clear introduction to the topic as well as goals and outcomes for the project. I also need to set aside some time to rehearse! One benefit of going first though was being able to pay full attention to the rest of DS26’s presentations without the distracting nerves of waiting for my turn. I learnt a lot from their approaches, mainly that I need to slow down the pace to help my audience engage more.
To close out on our first week, we were then able to listen in on the presentation of a client project as a window into our future. While not reflective of how the presentation went, I learnt that when working on a project I must do my best to identify the business requirements from the outset. Without thorough and detailed communication of how the data was developed and what the desired outcomes are, a team may end up losing valuable hours on developing solutions that are insufficient for a client. I need to make sure that when it is time to begin our first client projects I am prepared to establish a clear and productive exchange of ideas.
So, how reasonable were my expectations coming into The Data School? While the practical application of Alteryx gave me more trouble than expected and the dashboard design and presentations were difficult to manage, I have found it relatively simple to identify where my shortcomings lay. While not immediately succeeding can be frustrating I’m glad to have been able to discover what I can work on and develop in the coming weeks.
In summary, some of my most valuable lessons so far are:
- Avoid making assumptions about how different tools work. Make sample workflows to investigate instead.
- Develop experience with different data tools using the Challenge Index.
- Consider how familiar your audience is with the topic. Remember to clearly state important background information, goals, and outcomes.
- Listen to and learn from how others present.