Python is a powerful and incredibly productive language. You can get a lot of stuff in a very short amount of time.

On the other hand, it can be good to learn a language like C, and master the data types and algorithms topics through that, where you need to deal with a lot of memory stuff (manually)

 
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Depends on how you want to define "coding":

You want to automate something? Python.

You want to go Quant? Start with R, but be aware that you'll have to pick up more languages as you go (e.g. C++, Python)

You want to be good at your job? VBA, then SQL.

And, as someone working through my technical masters in an interdisciplinary Analytics program, I'll echo the above that "coding" is getting pretty cheap and you can find plenty of avenues to teach yourself. The analytical skills (e.g. math/stats/derivation of strategic implications) aren't so easy to pick up on your own...unless you like apples. I'd focus on developing those skills than try to focus on "coding".

Director of Finance and Corporate Development: 2020 - Present Manager of FP&A and Corporate Development: 2019 - 2020 Corporate Finance, Strategy and Development: 2011 - 2019 "An investment in knowledge pays the best interest." - Benjamin Franklin
 

Coding =/= software engineering or data science. Anyone can be a script monkey but understanding software architecture, system design, data structures and algorithms or in the case of data science advanced statistical modelling techniques is a whole other kettle of fish.

Coding is like reading.. everyone can read.. but not everyone can understand.

 

As someone that has dabbled with VBA / Python I agree. Sometimes writing the code is easier than identifying the problem / trend / insights you want the code to solve for.

Would you have any tips / advice / recommended self-study paths for getting better at the math behind data analytics? Beyond basic probability / statistics?

 

Understanding basic probability and stats are key. Another area once you want to get into multivariable stuff: Linear Algebra (Understand how to manipulate a matrix and what those manipulations mean). From there, you can develop some basic intuitions into time-series analysis (data-based analysis of moving series like financials), regression (more prediction), and simulations.

As far as self-study paths, you can find some data analytics paths on edx/coursera. If you want to start with LinAlg, check out the LAFF series from UTx on edx (Linear Algebra: Foundations to Frontiers).

Director of Finance and Corporate Development: 2020 - Present Manager of FP&A and Corporate Development: 2019 - 2020 Corporate Finance, Strategy and Development: 2011 - 2019 "An investment in knowledge pays the best interest." - Benjamin Franklin
 

Python, followed by R, SAS, and SQL. SAS is somewhat going out of style due to its high cost and the fact that Python and SQL can do much if not all of what SAS does and for much less money, but SAS is still popular, especially at larger organizations. SQL is only at the bottom of the list because it's the easiest to learn and you need database access to use it at all (while you can do anything you want on Python or SAS with just Excel files for inputs). There are plenty of good programs for learning SQL though.

"There's nothing you can do if you're too scared to try." - Nickel Creek
 

JavaScript (including the React.js and Node.js), PostgreSQL, and the *nix command line (e.g. bash).

If you are proficient in those you can build anything.

EDIT

Some clarification / elucidation given the tools / languages referenced in the above statement might seem a bit cryptic for those not in tech:

Why JavaScript? Because JavaScript is everywhere. Want to build a web app? Has to be in JavaScript; your browser will accept nothing else. Want to write a server? You can do it in JavaScript (with Node.js). Want to write some scripts to automate your daily drudgery? You can do it with JavaScript -- it plays nicely with your file system and OS, just like Python. Need to do some machine learning work? There's a JavaScript library for that. Want to build a desktop app? Oh hey, you can use JavaScript! Want to write an Excel Plugin? JavaScript. I think you get the picture.

Given its near ubiquity, I think JavaScript is a pretty good candidate for "the one language to learn".

Any non-trivial software is going to require a database, which is why I suggested PostgreSQL as well. PostgreSQL (called Postgres for short) is in my opinion the best SQL option out there by far. If you are proficient with it, you can handle the data / analysis requirements for virtually any application.

Lastly I suggested getting comfortable with basic commands in a *nix (i.e. Unix, Linux) shell environment (bash being the most common shell environment). After all, you're going to need a way to run and maintain all this code.

Of course, there are other languages and tools out there, and they have their applications and uses. It would be silly to dismiss them. But the question was "if you had to learn one thing," and the languages / tools I listed above give you the most bang for your buck in my opinion, hence my answer.

 

I'm a professional developer. Knowing basic commands in the Unix shell (bash being the most popular shell and the default choice for Macs and many Linux systems) is pretty fundamental. Can't really get anything done if you don't know basic commands.

 

I will suggest you JavaScript because it is another incredibly popular language. Many websites that you use every day rely on JavaScript including Twitter, Gmail, Spotify, Facebook, and Instagram according to General Assembly.

 

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