Why Python Beats R for Analysts

In today’s issue of Football Progression Path I’m going to talk through why you should choose Python over R when it comes to learning a coding language.

If I were beginning my journey as an analyst again, I would choose Python over R every time. With the rapid advancement of AI, the landscape of data work is evolving, and Python offers more transferable skills that can be applied across various industries.

The actual problem that most people have isn’t choosing which programming language to learn, it’s just getting started.

People get caught up with indecision. They don’t want to invest their time learning something which isn’t the best option. It’s understandable.

Ultimately it doesn’t matter which language you choose, as long as you start learning. Once you've grasped the understanding of a coding language you develop the transferable skills to switch between them both if you want anyway.

However when I'm asked which one should I learn I always say:

Python has the upside of being more transferable to other jobs and industries. So start there.

It has:

• AI integration
• Lots of free learning resources
• Easy to use packages
• Transferability across industries

Why Python?

• Transferability: Python is a versatile language that can be used in business, finance, and technology sectors. This makes it an excellent choice for those who may not secure a job in football immediately or wish to explore other fields in the future.
• Integration with AI: Python excels in areas like AI integration, building language models, data engineering, and machine learning algorithms. These skills are increasingly in demand across industries.
• Data Visualisation: While R is known for its data visualisation capabilities, Python also offers robust packages for creating visual data representations.

Considerations for R

There are some benefits of using R, so here's some food for thought.

• Data Journalism and Academia: If your focus is on data journalism or academic work, R might be more beneficial due to its strong data visualisation tools.
• Football-Specific Packages: R has some off-the-shelf data visualisation packages that are particularly useful in football analysis. But this is also the case for Python too.

Learning Curve

The learning curve for Python is similar to R, with numerous tutorials and courses available online. Neither one of the other is harder to learn. This accessibility makes Python an attractive option for new analysts looking to build their skills efficiently.

For aspiring, new and current football analysts, learning Python can significantly enhance your career prospects. It allows you to build applications, create software, and utilise database structures effectively. Many companies are seeking individuals with coding skills, particularly those proficient in Python.

Start your journey today by exploring Python tutorials and building your online portfolio. Some of my favourite resources for learning Python are McKay Johns’ YouTube, Friends of Tracking YouTube, Alex the Analyst YouTube and Codecademy free courses.

Sharing your insights and projects online can help you develop your knowledge, grow your network, and increase your opportunities in the football industry and beyond.

Catch you again soon.

Liam

Whenever you’re ready there are 2 ways I can help you:

1. Recruitment Room - My online membership community helps aspiring and new football professionals secure jobs working in recruitment. Master the four pillars of scouting, analysis, online portfolio, and employment. Learn from industry experts through our workshops, hot seats, and live sessions.
2. Pro Scout Edge - My scouting tool has been created by professionals and tested by experts in football. It’s the ultimate online scouting database you need for your scouting journey. It forms an all-in-one system for player and team scouting, with templates, dashboards and more.