![]() ![]() It produced a really interesting result, similar industries were seemingly being clustered together. ![]() I then used hierarchical clustering to group the industries. ClusteringĪfter switching to the industry representations I was able to effectively use PCA to visualise them and remove an outlier. It also remained applicable to customers as customers are labelled with industries. #Postico redshift how toWith some help on how to convert the data, this made for a much more easily manipulated dataset and considerably easier to interpret results. My manager then let me know about some work that had been done on grouping industries, this inspired me to shift to clustering industries. A member of the Data Science team showed me a better technique, but it was still difficult to interpret the results due to the sheer number of data points. I faced quite a few difficulties at first, for example, some of the techniques I wanted to use weren’t quite suited to the data. My project was focussed on clustering customers, finding larger groups which customers belong to that are then easier to analyse or reveal trends. I think it really improved my confidence with SQL. My queries were also not without bugs! But with support from my team I was able to get the correct data and run some interesting analyses. It was exciting to be able to apply SQL skills I had learned throughout my education to industry scale data. ![]() My first task was grabbing the data I wanted to analyse from our Redshift data warehouse. Meeting members of the other teams was also easy, as I could join the weekly intern sprint demos or one of the several forums, such as the accessibility forum, which let me get a good view of the company. Our daily morning stand-up meetings meant that I was in constant contact with them which was great for feedback and feeling connected, even as our team was working mostly remotely. Having an assigned “buddy” was also very useful as I felt I had someone that I could ask lots of questions to (even though the other team members assured me that I can ask them as many as I want to!) As the team was close it felt a bit tough at first to break into the flow but after a bit I was also able to chat comfortably with them. As I was in a smaller team it let me get to know all of my teammates well and meant that it felt like I could turn to them when I needed help. It was great meeting and working with the team. Post set-up I was able to quickly settle into my workflow and rarely had any issues. The Notion page was really helpful in getting it set-up and was written in a comprehensible way. I was pleased by how much I ended up liking the Python package manager Poetry as this was my first time using it. I’ve had much more difficult times setting up dev environments in the past but the IT team was really helpful and had everything moving smoothly. The technical set-up also was surprisingly painless. Having the CEO meet with our small onboarding group and be open to questions made a great first impression. I was particularly impressed by the event run by the CEO, Roan. There were a lot of onboarding events (especially on the first day!) but I think they mostly provided some interesting insights on FreeAgent, especially valuable were the sessions explaining the app and its users’ motivations. I thought I would write a bit about how it was getting set up, working on my project and communicating my findings to the rest of the company. I was really excited to begin interning at FreeAgent and after 9 weeks in the Data Analytics team I feel I’ve learnt a lot about working in a team inside a company, and about the culture here. ![]()
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