Whether you’re looking to change careers or simply add to your skillset, in the age of “big data”, data analysts and data scientists have gotten a lot of attention. Though the titles may seem similar, and they are in some fundamental ways, in this article, I’ll walk you through the key difference to know when exploring a career path here or looking to add a skill to your portfolio. Read on!
Data Analyst vs Data Scientist
If you’re here, you probably have an interest in pursuing a career as a data analyst or data scientist, or you’re looking to add to your skill set. In both cases, there’s a great need in the job market for both and the pay is often quite attractive. You may have seen job postings that list one or the other, and you may be tempted to think they’re the same, or you may ask yourself what the differences are. If you were to look this up, you’ll get a lot of different answers, but based on what I know and have seen, I’ll give you my take.
Now, there are differences, but there are also fundamental similarities in both titles that I’d like to talk through first.
What do Data Analysts & Data Scientists have in common?
For both a data analyst and data scientist, you need to have at least a general understanding of how the data is:
Depending on the size and needs of the organization, a data analyst for example, might be involved at every level from collection to reporting, or they may just be responsible for one part of that chain (typically, as the name might suggest, in the analysis and reporting). Either way, a data analyst and scientist would want to be familiar with all to an extent, regardless if they’re responsible for just one part.
Keep in mind, the goal for a company to enlist the services of a data analyst or data scientist is so that they can do a better job of making good data-driven decisions faster.
This, in turn, means better business outcomes, whatever that may be for the business.
Key difference between the Data Analyst & Data Scientist
Now here’s where the major difference is between both roles:
- Data analysts collect and report the data, perhaps in the form of some dashboard, and extract insights to explain why the data is the way that it is.
- Data scientists, however, use the data as a foundation upon which predictions about future outcomes are made.
One analogy I like to make is if you’re familiar with some of the more common job functions in business, accounting and finance are two of the big ones that come to mind, and those two fields happen to correspond well here; data analysis is to accounting as data science is to finance.
In accounting, you’re effectively recording and reporting out company financials, and there are insights to be extracted from that.
In finance, you’re looking at a number of business metrics, data, to make predictions about future business outcomes. Take an investment firm for example, you might think of certain financial models that predict stock price in some future year based on many variables like historical stock price, volume, moving averages, etc. This then yields a forecast.
Now that’s the main distinction I draw, but I do want to stress that this isn’t a hard line in the sand. As a data analyst, it certainly wouldn’t be out of scope to make predictions about the future. You are, after all, gathering insights from data, and really one of the purposes of having those insights is to anticipate what might happen in the future.
Skills & Tools Needed for Both
Where I may further distinguish between the two are in the tools used and skills needed. Data analysts should be familiar with databases, spreadsheet software, and data visualization applications. Data scientists should be familiar with those as well, in addition to knowledge of some programming languages (Python, R, among the most popular for data science), and have a strong understanding of statistics.
Also becoming more and more relevant, experience with machine learning/deep learning to create complex models that then lead to deeper insights not easily found otherwise, and lead to even better and faster decision making.
Bottom line is this: If you’re starting from scratch, going the data analyst route will get you going with the fundamentals and practical day-to-day business use. If you want to take it a step further and really expand your technical expertise when it comes to organizing and using data to make predictions, you can ramp your way up to data scientist as your career or interests take you there, because like I said, programming will likely be a big part of it, and it arguably has a steeper learning curve.
So those are your main differences between data analyst and data scientist as I’ve observed and researched. If you feel differently or have had a different experience when it comes to taking on these roles, I’d love to hear from you, so leave a comment down below.
Thanks for reading – I’ll see you around!