The SEO Primer to Learning Data Science Basics
For years, SEOs have faced multiple paths when it comes to career development options.
For some, the general options involving web development or traditional marketing roles have dominated the conversation, leaving out the data wonks out in the cold.
While there’s a massive benefit in mastering Excel for SEO, you may want to complement your current career with something a bit more challenging like data science.
Let’s dive into the basics of understanding how data science works for a search marketer’s workflow and how using R can help you on your career development path.
It’s one thing to be able to copy and paste code and run scripts – many of the greats began their career that way by deconstructing pre-existing work and learning along the way. But in order to level up to greatness with data science, you’ll need to focus on the whole package over time.
The three big supporting avenues I recommend dedicating time to include the following, which we’ll discuss as we go along:
Remember: When it comes to learning something as monumental as data science, take it one step at a time and enjoy the ride.
Now, in order to begin basic data science principles, let’s cover general guidelines that you’ll want to master over time.
Data visualization is exactly just that: being able to show the data in a way that speaks volumes in a visual manner.
With Google Analytics, many SEO professionals use the traditional line chart to explore data for insights and communicate to clients the cause and effect associated.
Line charts, however, are horrible at showing relative proportions (as opposed to pie charts) or comparing data (as opposed to bar charts). Knowing this, in general, is critical in our line of work.
Far too often, conveying a simple message turns into wasted effort in having to verbally explain the situation when a proper chart could have done it in seconds.
For data visualization in R, I recommend installing the ggplot2 package first. When learning ggplot2, not only do you receive the benefit of working with a structurally sound syntax, but you also have the added benefit of learning about visualization principles simply by coding.
Simply put, if you can’t visually speak to the site’s performance, be prepared to vocally speak to the confusion and objections.
Data visualization is nothing new to a search marketer. On a daily basis, we deal with pie charts, line charts, and on occasion, let out a collective groan when we try and explain to a client or a colleague the merits of scatter plots.
Understanding when to use a specific chart or graph is quite simple once you get the gist of why each one has its benefits in communicating the story clearly.
The best part about beginning to understand the basics of data scientist as an SEO is that you likely have access to an exorbitant amount of data at your fingertips.
Being able to draw from Google Analytics, Google Search Console, YouTube, Twitter, and even Spotify can be a blessing, but given the different processes that require each data pull, being able to align multiple streams in a manner that remain accurate, relevant, and free of “bugs” can make or break both trust and any decisions that come from the data.
But let’s not get ahead of ourselves. Before we report, we’ll need to follow these general steps:
Using a tool like R and RStudio, we can pull in data from a number of sources using pre-made packages developed for integration with Google tools such as Search Console, Analytics, AdWords, and even Sheets. But it doesn’t stop there!
If you have access to a database such as CloudSQL, PostgreSQL or MySQL, there’s a package for you too.
Odds are, anything that you can dream of collecting, it can be done using R.
Once you’ve imported your data, now it’s time to turn it into something useful, in a format that works for your needs.
Once you’ve imported your data, now it’s time to sort it all out in a manner that tells the story.
