It feels like someone is always telling you to learn to code. That’s crazy talk but coding can open new opportunities. Code helps users optimize their workflows and makes dealing with data more manageable.
I often receive questions about where to start or what someone in my role uses. It’s not always clear where to start with coding if you work in a marketing role. This article will cover a handful of options, why they matter, and where to look next.
Is Coding Right for You?
Let’s get this out of the way, coding will not make you magically better at your job. It’s not a secret power that will do everything for you if you. There is no magic key.
Coding is more problem solving than magic. Writing code is like writing instructions or creating a recipe. You take inputs, modify that input, and create an output.
Because of this, coding is better suited to analysts and those who work directly with data. This could be anyone who does A/B testing to someone creating a purchase prediction model for promos.
If you’ve ever thought, “I like turning data analysis problems into solutions”, or “I bet we can create a method for compiling this data each time”, coding might be for you!
Picking a Language
Knowing the basics and how to interact with a webpage can greatly enhance your skills in Google Tag Manager. It’ll allow you to create custom variables, track specific actions (like scrolling) on pages, and help build custom variables.
Getting Up and Running
Python is a strong language for general programming. Python uses a readable syntax and straightforward style. The language features an extensive community across industries and job roles. This means there is a tremendous amount of help out there as well as examples.
Python features many libraries (bundles of code that add features and functionality) for advertising platforms and services. Many platforms and services feature python examples in their support documents. This means you’ll spend less time figuring out how to get started and more time working on your projects.
The community aspect combined with Python’s innate robustness creates a great multi-purpose language for beginning coders. It can be used to work with spreadsheet data, connect to reporting APIs, scrape web data, or upload automated account changes to an ad platform.
Getting Up and Running
The only downside of Python is that some people find it hard to install. The most common recommendation is to install via Anaconda. This installs Python as well as a huge collection of the most common libraries.
I’d work in Python via Jupyter Notebooks. Notebooks allow you to write and test code in chunks. Rather than run a full script file, you can write snippets, execute them one by one, and easily add notes to your files.
I’d also recommend utilizing the Pandas library. It’s extremely popular for a reason and part of the Anaconda installation. This library helps convert data sources into rectangular formats with columns and rows. Trust me that this will make your life much easier and makes coding more accessible.
R is another popular language for analysts. It’s actually most popular for analyst type roles and the language’s roots lie in statistical programming. Many statistical methods and modeling tools are built into R from the ground up.
This isn’t what make R shine though. R features an engaged community of contributors in the analytics and data science community. This can make R feel more directly applicable to marketers who may code for analysis rather than building apps or programs.
In terms of writing code, R is arguably the best due to RStudio. RStudio is a program that helps manage scripts and interact with the language. It helps with all aspects of development and is a huge asset to getting beginners up and running. RStudio features panes for running scripts, the terminal, managing/inspecting data, and displaying visualizations.
As a tool, R boasts extremely useful set of packages called the Tidyverse. These packages are all built around the idea of tidy data. Even if you don’t do anything with R, I recommend reading more about this concept. This set of packages make it easy to import, clean, modify, visualize and model data in a consistent easy to write format.
Since R’s roots lie in analysis and modeling it feels like it can fall short in terms of robustness. While Python makes it easy to upload changes to Google or Bing Ads, the common R packages for these platforms focus on the reporting side. If you are building tools to manage ads, R might not be a great first choice.
Getting Up and Running
I’d start with installing RStudio and loading the Tidyverse in your scripts. R has a built-in data structure called a dataframe so no need to install anything like Pandas. Load up a spreadsheet and start playing along!
The short version of this article is,
We didn’t cover the basics of coding, how to write code, or what to write. That is a huge topic on its own and way outside the scope of this article. If anything, this article should help you understand the most applicable languages for your position and point towards the next step.
Along the way, remember it’s a journey. You’ll get stuck often but Google searches are your friend. Someone has probably asked the same question you have (yes, even thy specific ones).