Into the Deep End: Should you transition from Data Analysis to Data Science?
In 2012, Data Science was dubbed the sexiest job of the century by the Harvard Business Review. While I can’t vouch for Data Science being sexy, it has certainly become an appealing role for folks who enjoy working with data, as it promises high salaries and interesting work.
As someone who has worked professionally as a data scientist, I can say that I have no regrets about my choice of field, but should mention that much of the day-to-day work looks very similar to that of a traditional data analyst. We spend most of our time cleaning and visualizing data, with a smaller portion of our work dedicated to building and deploying machine learning models, which is what gets folks excited about the field.
So, if you’re a data analyst looking to transition into data science, or someone interested in a data role but not sure which path to choose, I’m going to lay out what distinguishes a data scientist from a data analyst and raise a few questions you should ask yourself when deciding between the two fields.
Data Analyst
Role:
Data analysts focus on examining and interpreting data to answer specific questions to help the business make informed decisions. When businesses are trying to evaluate past performance or the success of given initiatives, they turn to data analysts to help get the answers they need.
Skills:
Requires some combination of SQL, Excel, PowerBI and basic math skills.
Compensation:
According to Salary.com, the average salary for a data analyst in the US is $83,500, generally ranging from $75–94k. Salaries can get much higher at tech companies, which often require stronger technical skills than most firms.
Data Scientist
Role:
Data scientists tend to work on a wider range of activities than data analysts. In addition to being called on to perform traditional data analyst work from time to time, data scientists spend a lot of time thinking about how to automate or optimize the decision making process with the help of predictive modelling. In order to do so, data scientists spend a lot of time collecting, cleaning and preprocessing data before building models and deploying them.
Skills:
In addition to traditional data analyst skills, data scientists usually need to be proficient with a coding language like Python or R, have a strong statistics background, and understand the inner workings of machine learning algorithms. They also often need some basic software engineering chops in order to get their models into production.
Some data science roles also require a STEM degree, like Statistics, Computer Science, Math or others. It is definitely more of an uphill climb to land a data science role without a STEM degree, but it is not impossible,
Compensation:
The average data scientist salary in the US is $142,800, ranging from 127k-158k. Again, for skilled professionals in tech, these salaries frequently top 200k, of course, the expectations and stress can be much higher too.
Questions to ask yourself when deciding between the two roles:
Now that I’ve laid out the basics of each role, let’s look at what analysts should ask themselves when wondering if they should put in the work to get those eye-watering salaries.
Do you like mathematics and statistics?
At the end of the day, data scientists command a higher average salary than data analysts because of their specialized skills in quantitative subjects. I do believe that with the right motivation, most analysts can learn the necessary math and statistics, but if you found yourself sleeping or really struggling through your statistics courses in college, you might get burnt out studying these topics or realize you were happy in a traditional analyst role once you have a few months on the job.
Do you like coding or want to learn how?
Another love/hate topic for many folks is coding. Some people are allergic to coding, while for others, it’s their passion. Personally, I’m somewhere in between, I appreciate the power and preciseness of code, but it did take me several tries to get really comfortable with it.
Python and R are where most data scientists build machine learning models, and you will need to be comfortable with one of these languages to land a data science role.
The good news is that data scientists tend to need a much more limited set of coding skills to get started than a software engineer. But better coders will find they have access to more opportunities and higher salaries, and you must continually seek to improve your skills to stay competitive.
Do you want to be viewed as a technical expert/ nerd in your organization?
One slightly rude awakening I had when entering the field of data science, is that I wasn’t really viewed as a “business” person, but rather a number monkey. While I think this does depend on the team and organization you join, I found that I was viewed less as a business subject matter expert and more as a technical specialist that came to help the business when they needed it.
For many data scientists, this can mean being pigeonholed into a technical track, and less opportunity to move across roles like operations, strategy, etc. This is more of a anecdotal view, and I’d love to hear if others think this is true.
I appreciated the respect I got for having a more quantitative role, but I also found that it reduced me and my peers’ “leadership potential” in the eyes of others when compared to traditional analysts, who benefit by being deeply involved with tactical decision making.
Do you want to hit the books again?
Pretty self-explanatory, but it will take you several months at a bare minimum to learn and develop the skills necessary to transition roles. And several months applies to those who have a solid foundation in math and statistics. While the salary increase is tantalizing and will usually a positive return on investment in the long run, this isn’t a transition that can be completed by taking a single online course in two weeks. You will also need to successfully convince employers you’ve upskilled sufficiently, which is no small task.
In my next article on this topic, I will dive into how to transition from data analysis to data science, and lay out several common pathways as well as their pros and cons.
In the meantime, if you’re curious or want to dip your toe into the water, check out our Python and Data Science courses on the Maven Platform or Udemy.
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