or not…? Three years on
Imperial is challenging. Every Imperial student can tell you that. But some people REALLY struggle with their degrees. I am one of those people. Imperial was not what I expected and I have had a love-hate relationship with this degree. Before I leave Imperial, I want to share my up and down journey during the course of this degree because it really hasn’t been perfect, but I have almost made it and I know you will too.
I really loved maths. There is no other way to describe it. Between my four A-Levels in Maths, Further Maths, Physics and Chemistry, I spent over 70% of my time doing maths or calculations of some kind.
Data Spark is a student placement scheme at Imperial designed to uncover insights and solve real-world business problems. It lasts around 6 weeks and you’re able to work with students from different study programs. Throughout this journey, you get great advice from academic and industry mentors.
Having finalised the program last week, I wanted to share what I enjoyed the most:
Applying new skills
One of my favourite parts of this program was that I was able to apply several skills learned during my current study program (MSc Business Analytics). I was able to run different models, work on network analysis and and apply several visualisation techniques.
I recently used a R script from Keith McNulty to analyse my Facebook data. I was curious to know how much I had been posting for the past 10 years, but I also wanted to know much information Facebook had about me.
I was able to download over 4,000 days of data and more than 30,000 posts. These posts were mine, but also from friends that were posting on my timeline.
In the process, I learned these three points:
1. I have 1000’s of posts per year
I remember when I initially joined Facebook, my friends I would basically communicate and organise everything by posting publicly on our walls (no sense of privacy!).
The MSc in Business analytics is an intense year of rigorous technical and quantitative training. It prepares students to solve business problems using a variety of statistical, operations research and machine learning techniques.
What you learn in class is usually just a small part of what you end up doing in group projects and homework. There is a huge amount of good resources you can use to learn new material or enhance your knowledge in a topic.
In this blog, I wanted to share the most useful sources I found in case you’re planning to pursue this program at Imperial.
Before you start the program: the basics
Statistics and probability
Start by revising your math skills in statistics and probability.
A day at Imperial can involve lectures, homework, trainings and much more.
On a recent “normal Tuesday” at Imperial, I realised the interesting activities I was doing, which I’ll be sharing in this post:
Morning: Data Visualisation for Network Analytics
For our first homework in Network Analytics, we were asked to visualise correlations between stocks. As an investor, you’re interested in diversifying risk by selecting different types of them. You therefore want visualise which stocks behave similarly (positive correlations) or very differently (negative correlations).
Learning a new library is always demanding, but at least the results looked fascinating (spoiler alert: I’m a data visualisation fan).
Yesterday I attended my first ever hackathon, held at the Natural History Museum in London.
A perk of having a NERC funded studentship is priority attendance on the NERC Advanced training courses, and I was fortunate to gain a place on the Systematic review and meta-analysis for environmental sciences held at Royal Holloway University. Meta-analysis is a statistical technique used to combine results from different studies to identify patterns among studies, the strength of this is a higher statistical power is achieved than that of a single study. It was originally developed in medicine to gauge the effectiveness of treatments but is increasingly being applied to ecology.
We started off with lectures on the different types of reviews and an introduction to meta-analysis.