Product analytics requires a mix of product sense, business sense, and a technical skills, and developing those technical skills is a significant time investment. Whether you’re still in school or looking to make a career pivot into product analytics, you want to devote your time to learning the languages and tools that will offer the most leverage in the interview process and on the job.
With that in mind, below is a "prioritized list" of languages and tools to learn for product analytics. While not every product analytics role requires the same set of skills, this list illustrates how I would approach developing my core skillset if I were starting from scratch today.
This should go without saying for most any job in tech these days, product analytics or otherwise. Whether or not you’re regularly conducting analysis in a spreadsheet, significant amounts of information is passed around companies via spreadsheets.
Don't worry about getting too fancy. You don't really need to learn how to code in VBA (Excel's programming/automation language), but you should know how to write basic formulas, build charts, and create pivot tables.
Either Excel or Google Sheets will do. Both applications have similar functionality and it’s easy to adapt to whichever your company uses.
In an organization with lots of data and decent data infrastructure, SQL (Structured Query Language) is usually the language you’ll use to access that data.
But SQL usage can go far beyond simply accessing data. In some cases, you’ll write the code that defines key metrics in SQL.
SQL is relatively easy to learn and it’s possible to figure it out on-the-job, but for product analytics roles you might well be asked to write SQL queries during the interview process, so best to prepare ahead of time.
Python is a powerful, versatile language for analysis. It’s great for building models, creating visualizations, and (depending in your organization) interacting more deeply with a product’s codebase. In short, the ability to code in Python broadens the scope of projects you’re able to take on.
The catch is that the learning curve can be steep if you’re a new to programming. It’s certainly worth the time investment, but be prepared to put in some work.
Some organizations lean on Tableau and/or Power BI to create interactive dashboards and visualizations for decision-makers.
While these products require subscriptions, you can play around with Tableau Public for free.
In a lot of ways, R is interchangeable with Python. It’s great for analysis and modeling with large sets of data. It’s also fairly easy to learn R once you’ve already mastered Python, and vice versa.
So why is R lower on this list than Python? While R is comparable to Python in a lot of ways, it’s not as versatile as a programming language and usually can’t as easily plug into a product’s codebase.
That being the case, if you’re going to prioritize learning one of the two, start with Python. I’ll even take that a step further: If you already know Python, don’t worry about learning R yet unless a role specifically requires it.