This week on our social channels we’re looking at how Monzo customers have been travelling so far this summer – through a combination of spending data and memes.
We’ll look at where you’ve been going, the airlines you’ve been flying with, and what you’ve been eating and doing while you’re abroad.
We know how much you love charts and numbers. So all week on the community we’ll be digging into the data behind the memes
We’ve asked @chrisdoughty , who’s a Product Data Scientist here at Monzo, to come visit. Chris did all the data analysis for the campaign, looking into the trends and patterns around international travel we’ve been seeing so far this year.
Chris’ll be doing an AMA all week – and will even share some snippets from his data analysis that you won’t see on social
Here’s a little bit from Chris:
I’m one of our data scientists in personal banking, a group of teams who work on the core retail account functionality. In my first year at Monzo I’ve worked on several projects; improving existing features, understanding customer behaviour, and strategic projects on what we should build next. I’ve worked in data science and analytics roles for over 10 years, and have an educational background in behavioural biology. Some examples of personal data projects I’ve done:
Chris will be around all week to chat and we’ll close his AMA off this Friday (11th) . So if any of you have questions about travel trends, about Chris’ work in the personal banking team, or anything else – get them in!*
*Anyone who just wants to ask weird stuff is also welcome to
Oh and also – I’ll be around to test your prediction skills with a few quizzes and polls based on the data.
How are you manipulating the data? I presume Excel is a no go at this level?
Would you rather your house was under 4ft of water with 6 baby sharks (do do dooo) swimming around it, or completely dry but there was 50 seagulls loose?
morning! All of the data manipulation/transformation for this project was performed using Python. Writing functions and SQL queries into Python allows the analysis to be flexible and reproducible .
I live close to water and seagulls so already get a bit of both , but would opt for the former. I’ve SCUBA dived with sharks in the past, and found it great fun, although that was in an aquarium tank
Hi! All of our insight projects are written up in a bit of software called Notion. This allows us to summarise the findings of a project, explain the methodology and link to any dependencies. We typically get these peer reviewed.
I use Machine Learning models, often for classification or clustering tasks, and have done lots of text analysis (NLP) in the past - are there any specific questions you have on those topics?
Interesting! I’ve heard of this but not seen it used all that much in reality. How do you find it compared to, say, confluence or sending out insight decks. Do you find more interaction and questions?
No specific questions, I love big data but more specifically, teams taking actionable insights from them. So interested to see how the data models help to reduce the time between the two.
I’ve found it has a good balance of documenting work and allowing others to comment. A personal favourite is being able to toggle code or more detailed technical information for certain readers. In the context of some projects we also present insight decks internally as it allows for a good conversation dynamic
I asked some of these questions in another data science thread, but never got an answer…
What’s the difference between data science and data analysis?
What’s the difference between a data scientist and a statistician?
What pathways can one follow to get into roles in these fields?
These are some great questions, I’ll give my interpretation of the differences:
I think there is a grey area between the two. I’ve found analyst roles are more focused around interpreting data and creating business reports. Whereas data scientists bring elements of software engineering, and a deeper level of maths/statistics to both understanding data and predicting future actions from that data.
I think of a statistician as more of a data scientist without the software engineering component and sometimes a much deeper knowledge of statistics
There are many pathways into data related fields, and I’ve worked with data scientists from lots of different backgrounds. I enjoy problem solving when there are loads of unknowns, and learning obscure bits of maths, so have found it a fun career choice
I found it interesting to see how far and wide Monzo cards have been used, as well as the product features are customers have used. One example being bill splits and which countries we’re more likely to see them being used; Cambodia, Indonesia, Viet Nam and Costa Rica
Spending time with my kids and dog (middle-aged Golden Retriever) takes up a large portion of my time. I like to explore outdoors, living in Scotland means a lot of great walks are close by.
morning! All of the data manipulation/transformation for this project was performed using Python. Writing functions and SQL queries into Python allows the analysis to be flexible and reproducible 
.
, but would opt for the former. I’ve SCUBA dived with sharks in the past, and found it great fun, although that was in an aquarium tank
