Each year, participants of EADA’s International Master in Finance attended electives in the strategic specialisations in the third trimester. The specialisations include 12 electives divided into three areas focus aras: corporate finance, investment banking and FinTech. Here we talked to visiting professor Luke Smalley, who led the course Python/Tableau.
Professor Smalley joins us from Lisbon, Portugal, where he is Senior Data Analyst at Uber. He holds a master’s degree in International Political Economy from the University of York and has held senior commercial positions in tech companies ‐ including as a director of a software development company ‐ before joining Uber. He currently teaches data analytics at Ironhack, an international tech school. In this interview, he shares his ideas about how business analytics software is changing the world and how he integrates his extensive experience into his classes at EADA.
Many companies are sitting on hoards of data, with no idea what to do with it, or how to extract any value from it.
How did you get into business analytics?
I spent the first part of my career working in commercial roles at tech companies. In doing so, I saw how strong analytical processes could impact business success – but that those processes were often done poorly. I saw an opportunity and decided to specialise as an analyst. I taught myself how to code and the rest – as they say – is history.
Why is software such as Python and Tableau so revolutionary?
For the last decade, company execs have been told that investing in ‘Big Data’ was going to give them the competitive advantage. So they’ve spent the last 10 years investing in big data infrastructure, collecting data from every possible source. But now many companies are sitting on hoards of data, with no idea what to do with it, or how to extract any value from it. Tools like Python and Tableau enable companies to start extracting value from that data.
Over the next few years, I believe that we’ll see more data science functions and processes considered as the responsibility of the data analytics department, particularly in the realm of predictive analytics.
Python can be used to automate so many tasks that the productivity gains alone should be enough to motivate financial professionals to learn the language.
Why is it important for finance professionals to have an understanding of these tools?
Python is widespread across the finance industry, particularly in hedge funds and investment banking. It’s a fast and simple programming language, and contains several libraries that are ideal for working with financial data. Python can be used to automate so many tasks that the productivity gains alone should be enough to motivate financial professionals to learn the language.
What is the most rewarding aspect of using tools such as these? And the most challenging?
The most rewarding aspect is seeing the tools that you build have a demonstrable impact on business. The most challenging aspect is that often the data analytics team is a bridge between technical and non-technical departments, and learning to communicate to these two different audiences can be difficult.
How do you bring your experience into the classroom?
I learned to code in Python late into my career, so I know what it’s like for someone from a non-technical background to start learning a programming language for the first time. I believe the best way to learn to code is by coding yourself, rather than watching someone else code, so the focus of the lessons is on practice rather than lectures.
The most rewarding part of teaching is seeing people learn how to do things that they didn’t think they could do. I recommend students find an area of fintech that piques their interest and work on it every day.
What do you know now that you wish somebody had told you when you started your career?
Learning to code is easier than you think and the benefits are greater than you think.
