No, Data Analysts aren’t “Data Scientists in Training”
I love the analytic community on Medium. Knowledgeable people share cool projects in areas they are drawn towards. The Medium analytics community is full of helpful tutorials and insightful op-eds about current issues of the art.
Medium and other blogs have helped foster a thriving and kind data community.
Despite the thriving community and advances in the field, one thing I see less of are proud Data Analysts. After all, one of the flagship publications in the space on Medium is Towards Data Science. People not in the industry often forget or don’t know that Data Analysts play a key and sometimes distinct role in the art of data science.
Because of this, there’s sometimes a misconception that Data Analysts are just Data Scientists in training. While a Data Analyst can eventually become a Data Scientist, the skill sets and tasks can often be more distinct.
Some of the key differences that immediately come to mind for me are:
- Data Scientists are usually more statistically oriented
- Data Analysts are usually more business domain oriented
- Data Analysts are sometimes Data and Dashboard Janitors
- Data Scientists are sometimes Model Janitors
- Data Analysts care more about precise speed
- Data Scientists care more about repeatability and validity
Before I go much further, I should position myself.
I’m far from an expert. I’m about 1-year removed from my Google Data Analyst certificate and stepping into my role as a full-time analyst. A lot of what I’m going to present will be collated conversations that I wish I had when I first started my journey.
I hope that if you’re considering a career in data, this brief introduction will help you better understand and decide which of these equally rewarding paths you’d like to walk.
How Data Scientists and Data Analyst Differ
I’ll be the first to admit that I was confused when I got into the field. My confusion was partly because employers are also confused and use the terms loosely. One of the voices that helped me start solidifying my understanding was Cassie Kozyrkov.
I found this video especially helpful in increasing my understanding:
For another, more comical take on the difference between the two roles, I would also recommend Luke Barousse’s video:
Lastly, if you’re looking for a little less philosophy and a little more of “nuts and bolts” regarding each role, then Alex the Analyst has a great and in-depth breakdown:
To summarize the key takeaways from these three videos:
- Data Scientists/Statisticians are usually model and future-data oriented
- Data Analysts are usually existing-data oriented
- Data Scientists/Statisticians are about making the right decision consistently
- Data Analysts are about drumming up inspiration and lines of questioning
My Experience as an Analyst vs my Statistics Major
In my job as an analyst, I can certainly relate. I’ve slowly won the nickname “Alice” from Alice in Wonderland. Often the data that I’ll go and find or the questions I’m asked to address lead to, you guessed it, yet more questions.
That isn’t to say I never use statistics. Recently, I have used a simple OLS regression to help decide how many services should be scheduled to obtain the desired number of services on average. I also consistently look at distributions and perform basic hypothesis tests when appropriate to see if the results between groups of interest are statistically different.
I certainly relate to Data Analyst Luke, though. If you decide to pursue Data Analytics, you’ll use a lot of Excel, Python, and Tableau/Power BI. Most of my work tasks consist of cleaning, extracting, and staging data so it can be turned into dashboards or reports.
Depending on your org structure, you may also be in more of an “advisory” role. After you get data to illuminate trends and ask questions, you’ll often be asked, “what should we do with this?”
I think one of the marks of a senior analyst (which I strive to be someday) is knowing how to take the descriptive analysis that is common and parlay that into prescriptive analysis.
My work as a Data Analyst is in stark contrast to what I went to school for. My Master’s degree was in Applied Statistics (aka Data Science.) If you go down the statistician/data scientist road, you’ll be much more concerned with measures of model performance and how to productionize models so they run consistently and accurately, even on new data.
What Data Analyst Content Would be Helpful
So, I hope that your confusion has lifted a bit. I also hope I have convinced you that analysts play an equally exciting and important role.
Based on my needs as an analyst, I have a wish list that I illustrate below. I’ll also leave some additional blogs that I’ve found helpful for your further research.
I would love to see more articles about dashboard design decisions. Dashboards and data visualization are a huge part of the work.
I would also love to see effective (and sometimes hacky) ad-hoc SQL queries analysts used to get data to senior leadership quickly. SQL is like Swiss army knife of an analyst — stylish and dependable.
Another thing I’ve had a hard time finding are articles about developing technology integrations (like when I combined Microsoft Forms, Excel, Power Automate, an External API and Power BI for a real-time Ticket Form Dashboard.)
Lastly, I would selfishly love to see more articles about developing a small-scale data architecture. I still think there’s a believe and barrier to data for smaller companies, but would love to see more discussion about tearing barriers down.
As I still growing, I recognize my knowledge and awareness are incomplete. If more of these conversations are happening and I’m just missing them, please send me some links!
Wrapping Up
So if you made it this far, you probably wonder, “what’s the point?”
To summarize, we explored how Data Analysts and Data Scientists have different focuses and priorities despite a similar skill set. Data Analysts are like scouts while Data Scientists are like researchers.
I also identified where I see some potential content gaps from a beginner’s perspective. I would love to see big publications like Towards Data Science pick up the torch and encourage more Data Analysts to write for other Data Analysts.
In short, I would love to see more articles from Data Analysts for Data Analysts. With time, I’m hoping that big publications like Towards Data Science will also have strong data analysts that present their tricks to get insight faster and clarify the business domain problem.
So no, I’m not just a Data Scientist in training. I’m in a professional role that, while adjacent, has different needs and priorities. As I grow in my craft, I’m focused on better presentations, quicker speed to insight, and better technological integrations to allow a more holistic view of the current state.
Additional Helpful Publications
Based on my reading and consistent Googling, I’ve found the following websites as helpful resources and sources of inspiration:
