A day in the life of a data scientist intern – Kevin Minors

Day in the Life – Kevin Minors

Over the last decade, I’ve lived in Bermuda, Oxford, Bath, and now Leeds. My daily schedule has never known consistency. Secondary school in Bermuda meant rigid routine, undergraduate studies in Maths at Oxford meant late nights and early mornings, PhD research in mathematical population modelling at Bath meant longs hours staring at a problem with little progress. Now, working as a Data Science Intern at the Leeds Institute for Data Analytics, my schedule has changed again. A typical day looks something like this:

6 AM – Morning Routine

My alarm gently fades in. I smash the snooze button anyway. Still sleepy, I make breakfast while trying to avoid stepping on my cats. I’m not always successful. After checking my phone to see when the next bus is coming, I plan the optimal time to leave the house. Despite this fact, I am not willing to admit how many times I’ve missed the bus. Then a tough session at the university gym to really wake me up and get the blood flowing.

9 AM – Supervisor Meeting

The day starts with a meeting with my supervisor Nicolas Malleson to discuss the progress I’ve made last week and the goals I’ve set for this week. This normally includes references to upcoming conferences I should attend and academic papers related to our research that I should read. I always leave these meetings inspired and focused.

10 AM – Coding

Then the work truly begins. I sit at my computer and begin writing code. On a good day, I will be able to write some code while only looking for help online a handful of times and then set the code running while I focus on another task. On a bad day, I don’t write any code because I am fundamentally stuck or none of the code runs because there is an extra open bracket somewhere. Nightmare!

My work is exclusively in Python, writing in Spyder and Jupyter Notebook. We are researching how data assimilation can be applied to an agent based model using a particle filter. In other words, we have a model with a lot of individuals in it, for example, people walking through a train station. This model is receiving new information about where these individuals are from different sources in the train station, like video cameras and footfall sensors, and the model updates to reflect this new information. This model can then be used to predict where individuals will be in the future.

12 PM – Lunch

Lunch with the other Data Science Interns never disappoints. On the one hand, we have enlightening conversations about all the different projects we are working on. We use lunch time to mention the successes we’ve had and also to ask for help if we’re stuck on a problem. On the other hand, we also have hilariously wild conversations. For example, we once asked ‘If you could be a character in a film, who would you be?’ and then the conversation descended into analytical chaos trying to understand what it meant ‘to be’ a film character. Would you have to live out the whole movie or only one scene? Would you be the actress playing the character with the behind-the-scene moments included or the actual character in the universe of the film? I don’t think we found a suitable answer.

1 PM – Results / Presentation

Now that my code has finished running, I can look at some of the results. This process involves both making sure the code is doing what I want it to do and making sure the results make sense. If either of these is not true, we have a problem.

Once everything is up to standard, I will have to give a presentation with the other interns on our projects to an audience of academics and external partners, including my external partner Improbable. We have numerous opportunities to present our work, which have a variety of benefits including increasing our understanding of our work, enhancing our presentation skills, and improving our network of contacts within the field.

4 PM – Training Session

To end the day, there will be a training course applying a programming language to a new application, such as ‘Geocomputation and Data Analysis with R’ and ‘Introduction to Python for Data Analytics’. We have a healthy budget and a range of opportunities for training. Some training may be relevant to the project you are working on while another one may just be something of personal interest. There’s truly something for everyone.

5 PM – After Work

The Data Science Interns are quite a sociable group. We enjoy a post-work drink (both alcoholic and non-alcoholic) to end the day. It’s a great way to build team chemistry, vent about any issues we’re having, and continue any hilarious conversations from lunch. I generally don’t stay too late to make tomorrows 6 AM start a little easier.

Find out more about the Data Scientist Internship Programme here.