New to sports analytics?
These are the programming languages and tools to learn & the order to prioritize learning them in.
A thread 👇
These are the programming languages and tools to learn & the order to prioritize learning them in.
A thread 👇
Priority 1️⃣: Get comfortable with Excel
Most high-level modeling is not done in Excel, however, it’s still important to know your way around a spreadsheet.
If you don’t have access to Excel, Google Sheets (which is free) will do the trick.
Most high-level modeling is not done in Excel, however, it’s still important to know your way around a spreadsheet.
If you don’t have access to Excel, Google Sheets (which is free) will do the trick.
Priority 2️⃣: Learn either R or Python
R & Python are the core languages of sports analytics, & most roles will require that you know at least 1 of them.
Don’t worry about learning both at first. They’re similar languages and if you know one, it'll be easy to learn the other.
R & Python are the core languages of sports analytics, & most roles will require that you know at least 1 of them.
Don’t worry about learning both at first. They’re similar languages and if you know one, it'll be easy to learn the other.
Which of R/Python should you learn first?
You can’t make a “bad” choice, but I’d put it like this:
- If you see yourself as more of an analyst, start with R
- If you see yourself as more of a programmer, start with Python
Some resources to help:
brendankent.com/2020/09/15/cod…
You can’t make a “bad” choice, but I’d put it like this:
- If you see yourself as more of an analyst, start with R
- If you see yourself as more of a programmer, start with Python
Some resources to help:
brendankent.com/2020/09/15/cod…
Priority 3️⃣: Get even better at R and/or Python
My point is that anything beyond being rock solid at R/Python is a nice-to-have, but not a game-changer.
My point is that anything beyond being rock solid at R/Python is a nice-to-have, but not a game-changer.
You’ll probably do more for yourself by spending time improving at R or Python (learning to create better visualizations, more advanced models, or learning whichever of the 2 languages you haven’t already) than you will by moving on to the next items in this list.
Priority 4️⃣: Learn SQL
In an organization with lots of data, SQL (Structured Query Language) is usually the language you’ll use to access that data.
SQL is relatively easy to learn on the job & employers will often assume that entry-level analysts don’t know SQL already.
In an organization with lots of data, SQL (Structured Query Language) is usually the language you’ll use to access that data.
SQL is relatively easy to learn on the job & employers will often assume that entry-level analysts don’t know SQL already.
Priority 5️⃣: Learn Tableau, Power BI, or other viz tools
Some organizations lean on Tableau or Power BI to create interactive dashboards and visualizations.
While these products require subscriptions, you can play around with Tableau Public for free.
Some organizations lean on Tableau or Power BI to create interactive dashboards and visualizations.
While these products require subscriptions, you can play around with Tableau Public for free.
In summary:
1. Make sure you know your way around a spreadsheet
2. Learn either R or Python
3. Get even better at R or Python and/or learn whichever one you didn’t to begin with
4. Learn SQL
5. Learn visualization tools like Tableau
1. Make sure you know your way around a spreadsheet
2. Learn either R or Python
3. Get even better at R or Python and/or learn whichever one you didn’t to begin with
4. Learn SQL
5. Learn visualization tools like Tableau
For a slightly deeper dive into this prioritized list, check out my recent blog post (same list, a bit more commentary):
brendankent.com/2020/12/16/lan…
brendankent.com/2020/12/16/lan…
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