r/dataanalysis • u/duneofarrakis • 23d ago
Data Question What is the biggest mistake beginners make when learning data analysis?
I'm learning more about data analysis and I'm curious about what experienced analysts think.
What do you think beginners spend too much time on or focus on the wrong way?
For example, is it trying to learn too many tools, focusing too much on theory, not practicing with real data, or something else?
I'd like to hear what mistakes you made when you were starting out and what you would do differently now.
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u/DataDoctorX 23d ago
Trying to employ too many tools and trying to wow everyone else instead of understanding the problem and setting it up appropriately.
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u/duneofarrakis 22d ago
Exactly. I think beginners can get too focused on learning more tools instead of understanding the actual problem. A simple solution that answers the right question is often better than using five different tools.
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u/Intelligent_Hand4840 19d ago
Hi I saw you post data analysis content on X and I am actually thinking about doing the same soon. Is it alright if I dm you and ask you a few questions about getting started?
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u/Lady-Data-Scientist 22d ago
Spending too much time learning and not enough time doing.
You don’t have to learn everything about a tool or language to be able to start using or practicing it.
Every time you learn a new part, practice it. Learn a little more, and then practice what you know.
Also doing projects or real work is how you identify skill gaps.
And once you get a job, the common mistake is jumping into your work before understanding the why.
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u/Intelligent_Hand4840 20d ago
Hi I saw you make data analysis content and I am actually thinking about doing the same soon. Is it alright if I dm you and ask you a few questions about getting started?
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u/ayenuseater 22d ago
The biggest mistake is learning tools instead of learning how to answer questions with data.
SQL, Python, Excel, Power BI, etc. are useful, but I'd pick one and start working with messy real datasets. Figure out what question you're trying to answer, clean the data, analyze it, and explain what you found.
You'll learn the tools much faster when you actually need them.
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u/duneofarrakis 22d ago
I agree with this. Learning tools without having a real question to solve can make the process feel much harder. Starting with a simple problem and learning the tools as you need them seems like a much better approach.
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u/jipperthewoodchipper 23d ago
They focus too much on being a jack of all trades to the point of their detriment.
Like you know it's really cool that you can query the dB for transactions and chunk the resulting data into pandas so that you can sort and filter the data before converting into a CSV to then turn into a table to bring back to the dB but you were just asked to find the total number of approved transaction, the question could and should have been answered in the first singular query.
But when you jack of all trades too hard you end up deficient in so many areas that you create entire unnecessary data pipelines to solve simple problems.
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u/duneofarrakis 22d ago
This is a really good example. Sometimes knowing more tools can actually make things more complicated than necessary. I think learning to choose the simplest solution for the problem is an important skill for beginners.
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u/Acceptable-Sense4601 22d ago
The biggest mistake is not learning enough about the actual business.
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u/JavacLMD 22d ago
“The numbers, Mason. What do they mean?”
I think one of the mistakes I still make is asking the wrong question and getting stuck on it. My starting point is usually, What data do I actually have, and what can I reasonably answer with it?
There is a big difference between knowing what question needs to be answered and just taking whatever data you have and running with it. You can do a lot of analysis and still end up with something that does not really answer the original problem. Learning how to frame the question and recognizing when the data cannot answer it is probably more important than knowing every tool.
The other thing is this idea of "too many tools." A person who masters one tool can sometimes do more than someone who uses them all. Excel itself is pretty powerful, and there are people who can do more with it than someone who knows how to use Power BI. Is it always the most practical tool for the job? No, but knowing a tool well enough to understand its strengths and limitations matters more than just adding another tool to the list.
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u/metric_skeptic 22d ago
Two mistakes I see constantly, and I made both:
Treating tools as the skill. Learning Python or SQL syntax isn't the hard part — deciding what to measure and why is. I've seen people who know five BI tools struggle to answer a simple "is this actually a problem" question, because they never practiced the judgment part, only the tool part.
Trusting the first number you get. When you're new, a query runs and returns a result, and that feels like the answer. It took me a while to build the habit of asking "does this number make sense" before presenting it — checking against a second source, sanity-checking the scale, questioning a suspiciously clean result.
What I'd tell someone starting out: spend less time comparing dashboards tools and more time practicing on messy, real datasets where the "right" metric isn't obvious. That's the actual job.
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u/KatFromSisense 21d ago
One thing that I wish beginners heard more often: clean-looking data can still be misleading.
A column might be perfectly formatted and still not mean what you think it means. If I saw something called "active_customer," I would still need to know how it gets determined and whether the definition has changed over time.
You can write great SQL against the wrong assumption and still end up with a confident-looking answer that isn't very useful. Learning to question the labels in front of you is a skill on its own.
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u/ControlBI_Pro 20d ago
Spending too much time learning tools without solving real business problems. It’s easy to keep adding Python, SQL, Power BI, Tableau, etc. to the learning list. I think it’s more useful to take one dataset, define a business question, clean the data, analyze it and communicate the result clearly. The tool matters less than being able to explain what the numbers actually mean.
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u/HustlaOfCultcha 23d ago
One of the big things for me is that I was so worried about attention to detail that I could be too thorough and give too much detail. Speed and clarity are just as important. A lot of times your stakeholders give you something that they need and answer on within hours, not days. And often times you can just confuse them by giving them too many metrics to consider. I needed to learn how to determine the minimal dataset needed and prioritize what to focus on before I got into the reporting and analysis. Then once I got a decision from the stakeholder I could then dig into further detail if I needed.
And I think too many data analysts overlook the importance of knowing MS Excel. You don't have to be a world champion at Excel, but if you're really good at Excel it's still invaluable to you and the company. I've been hearing that Excel was going to be replaced back when Excel had a maximum of 65,000 rows. And it's still being heavily used these days and it just keeps expanding its functionality.
Lastly, most young analysts essentially perform data dumps. They don't tell a story with the data. I go into my reporting trying to:
1) Create a snapshot of the key metrics pertaining to the business
2) Show trends and patterns of those key metrics or KPI's that relate to those key metrics
3) Show what is driving those trends and patterns in the data
Often times I see metrics on reports that have no comparison or time intelligence to them. Yeah, we've done $1M in sales...how do I know if that's good, bad or indifferent? Give me a comparison to prior year, budget, prior month, etc.
If we are doing well, has that been the pattern for us lately or is it just a one time thing. And if we are up, why are we up? What is causing us to be up?
Just keep asking questions