If you’re looking for a well-paying job that would include great career opportunities down along the road, data analysis or data science might have been the two professions that could have been recommended the most. And this isn’t without reason - the field of IT, in general, is considered to be very profitable.
All you need to do is take a single look at some of the salary report-types of sites to see almost unbelievable numbers. That being said, it does still cause a lot of confusion - many people tend to mix up and not know the difference between data scientist VS data analyst. If you have a similar problem, worry not - this Data Analyst VS Data Scientist comparison article is here to help.
Ultimately, this tutorial aims to answer two big questions - what’s the difference between the two professions and which one should you choose to learn? However, to be able to answer these questions, we first need to establish some background information.
So, in the very beginning, we’ll briefly talk about both of the specialties separately. After that, we’ll also lay down some criteria for our evaluations. Once that is done, we’ll be able to analyze, evaluate and compare data analytics VS data science effectively.
Table of Contents
Bonuses of Working in Data Field
Before we go ahead and start our descriptions, analysis, and comparisons, there is one more thing that I’d like to talk about. Whether you’re already an up-and-coming developer or analyst, or this article is your first look into the world of IT, there is something that surely everyone thinks about at one point or another - why choose data analyst vs data scientist and IT field as a career path?
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In most cases, the very first thing that you’d think of when hearing this question would be the salary. And let’s be honest - IT professionals truly do have amazing salaries. It doesn’t matter if you’re a graphics designer, a programmer or a specialist that deals with data (in one way or another) - IT-related salaries tend to sway quite above the average. This is because the industry itself is constantly developing and seeing various new opportunities and ideas every single day. That’s why there will probably never be a shortage of demand for IT-based professionals.
And that’s the other thing, too. At this point, it is almost a given that if you want to feel safe and assured about your career choices, anything IT-related is probably one of your best bets. Since the industry is constantly progressing, the job market is filled with IT-related job offers. And that’s very unlikely to change since, as I've mentioned earlier, some innovations are surfacing daily.
My whole point that I’m trying to make is that there is so much more to being an IT professional than just a great salary (although that’s already a lot). That’s worth keeping in mind moving forward in this data analyst VS data scientist article. With that said, we can now move on to talking about what does a data analyst does.
Data Analytics
When it comes to the topic of data analysts and data scientists, data analysis is surely the more popular of the two. This is mostly because it is both a bit simpler and encountered more often. So, what does a data analyst do?
Data analysts are people that work with huge amounts of information. Their job involves taking a chunk of data and then “translating” the numbers into everyday-English. This is done so that the analyst could then present the analyzed data to their employers, who would then make adequate business decisions based on the results of the analysis.
Data analysts are essential members of any team that wants to grow their business. That being said, these people are most commonly encountered in huge corporations that deal with vast amounts of data every day. While it might not seem that way from the data analyst VS data scientist discussion, data analysts have very specific and clear job responsibilities (which is a great thing!). Their absolute main responsibility is to be able to dissect the provided information and make clear statements that could then be interpreted by the company.
For example, one key skill that a good data analyst should have is the knowledge of SQL. SQL is one of the the most popular programming languages in the world, though technically it's a query language (SQL stands for Standard Query Language). If you're looking into becoming a data analyst and need an entry point, learning SQL is a good place to start. In fact, the DataCamp SQL Fundamentals Skill Track was developed as a beginner-friendly program to master the essentials of this language. If you're curious to learn more, you can find our guide to the entire DataCamp SQL skill track here.
Now, with all of that being said - why is the discussion of “data analytics VS data science” important? Or, rather, why is data analytics essential for a company's success? Let me give a quick example.
Imagine that you own a small company that sells a specific type of coffee. You follow all of the golden rules of marketing, advertise both traditionally and online, spend a lot of time targeting your audience, etc. A month passes, and you want to check out how your business is doing. It’s not as easy as simply taking a look at the revenue (which you hopefully make!). If you want to keep your business on the successful side of things, you’ll have to identify possible bottlenecks and problems. This is where the “data analyst VS data scientist” discussion comes in - data analysts will take all of that information, analyze it, and then come back to you with the results. To find out which groups of your target audience aren’t buying the product (and why), and then be able to make certain decisions based on the information given. However, all of the information that you’ll require is generated in strings of numbers - you’d have to possess some specific knowledge to be able to understand the data.
Even though this is a very simple and watered-down version (example) of the answer to the question of “what does a data analyst do?”, you should now have a pretty decent idea about the responsibilities of these professionals. With that said, let’s move on to the next part of our “data analyst VS data scientist” comparison article and talk about what do data scientists do.
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Data Science
In the “data analytics VS data science” discussion, data science is considered to be the more complex and difficult one of the two. This is mostly because this profession when compared to data analysis, involves some extra complex tasks. But let’s take it one step at a time - what do data scientists do?
At first glance, data science is very similar to data analytics. Both of these specialties deal with the same thing - huge amounts of information presented in numbers. The main difference between the two lies in the extent of their responsibilities, however.
We’ve already established the fact that data analysts (as the name implies) extract and analyze information using tools like SQL or Tableau and then present it to the company. The responsibilities of data scientists extend in both of those processes. First of all, while a specific problem is given to a data analyst, data scientists are expected to formulate the problem on their own. To give you an example, we could go back to the before-mentioned coffee shop.
If you were to hire a data analyst, you would have to provide them with a specific question that you want to be answered. An example of such a question could be “does the X group of people buy more coffee than the Y group?”. The data analyst would take your question and find the answer based on your company’s performance. However, in the debate of “data analyst VS data scientist”, you wouldn’t need to formulate any questions for a data scientist. Rather, it would be the responsibility of that person to take a look at your company's business model, deduct possible (and potential) issues and raise the question on their own.
These people also have extended responsibilities when it comes to the processes that happen after they have presented you with the analyzed information. While a data analyst would finish his or her job there, a data scientist has to also draw certain conclusions from the presented data, and come up with a further business plan of action for the company.
Knowing programming languages is essential for data scientists. They work using special-purpose libraries, such as NumPy that allow them to efficiently handle data. If you want to learn more about how this job works in practice, you might be interested in our guide to the DataCamp Python courses.
So, with all of that said, you now know not only what do data scientists do, but also the main differences between the two professions. Now, before we start discussing the actual “data analyst VS data scientist” comparison, let’s briefly go over the criteria which we’ll use to analyze both of the professions.
Criteria of Analysis
Most jobs that are at least somewhat similar to one another can be analyzed by applying various criteria. Since this would both be somewhat counter-productive and would take up a lot of time, we are going to be using only a couple of the mostly-referenced points to make the distinction between data analysts and scientists.
There are three points that we’ll use - popularity, difficulty, and salary.
Popularity refers to how many companies are looking for the specific kind of specialists, at any given time. If a job is popular long-term, you can expect it to be a pretty safe career choice. However, you should also keep in mind that popular jobs have higher amounts of competition! This is also an important point in the data analyst VS data scientist discussion.
The difficulty is rather self-explanatory - we’ll take a look at just how difficult the jobs are when compared to each other. This point does, however, directly correlate with the last one - salary. Jobs that are more difficult and require more time and effort to perform successfully are often those that pay a higher salary (often - not always!). This is also true in the realm of data scientist VS data analyst.
Which One Should You Learn?
Worry not - I won’t go too in-depth with the comparisons. The information is presented clearly and concisely as possible. With that said, let’s start our data analyst VS data scientist comparison from the very first point - popularity.
Which is More Popular?
Popularity can be a tough point to deduct. However, a great way of looking at it can be to simply go to a search engine (i.e. Google), type in both of the job positions and then compare the results of the first couple of pages that come up.
Admittedly, when it comes to comparison, data analysis seems to be the more popular one that people search for. Even though there might be a whole variety of reasons for why that's the case, the most prominent one seems to be the fact that some people don’t even know that such a thing as “data science” even exists.
Which Is More Difficult?
Needless to say, data science takes this point without question. Data scientists have the same responsibilities as data analysts - and then some! Since both the amount of work and its complexity are higher for data scientists, it is only natural that their job is that much more difficult when compared to the data analyst’s one.
Which Has a Higher Salary?
As I’ve mentioned a bit earlier in this data analyst VS data scientist comparison, more complex work usually means a higher salary. Data science is no exception. However, just how much more do data scientists get paid when compared to data analysts?
According to Glassdoor.com, the average annual salary of a data analyst is around $67,400. This would come out to be $5620 per month. That’s not a bad salary! However, the average yearly salary of a data scientist is estimated to be around $117,400, or almost $9800 per month!
That’s a huge difference! That being said, if we take into account the difference between both of the job’s complexities, it does make a lot of sense.
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Conclusions
As you can probably see by yourself, even though both of the jobs share similarities, they are rather different when it comes to certain job criteria. That being said, in the end, it all boils down to your personal preferences. Both of the jobs vary in their complexity and workload and will suit different people with different wants and needs.
With that said, we have reached the end of our “Data Analyst VS Data Scientist” comparison article. If you’ve found the information useful, don’t hesitate to check out other articles as well!
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