Beginner data analyst mistakes: how to recognise and correct them

When you start learning data analysis, it is easy to assume that the next step must be another tool. You know a few formulas, so you add SQL, then Python, a dashboard and a machine-learning model. The list expands, yet a blank file still leaves you unsure where to begin. In that situation, I would inspect how you approach a task before adding another course to the plan.

I would focus on five areas: foundations, project scope, independent work, expectations of the first job and communication. I suggest treating them as practical questions for reviewing your own learning. Each can be tested through a small task without purchasing another tool or spending months rebuilding your portfolio. Start with the area where you find it hardest to give a concrete example from your own work.

I will use a synthetic snapshot of support tickets. It shows how apparently minor decisions change a result’s meaning. We will not build an entire customer-support system. The objective is a short, reproducible explanation of what the available records tell us and what is still missing before we can assess the team’s work.

Mistake one: skipping the foundations

Knowing a command does not establish that you understand its calculation. You may be able to group data while remaining unsure whether the rows represent orders, order lines or customers. You may write an average correctly but divide by the wrong observation count. At the start of an exercise, I therefore ask what one row represents, which field identifies it and which values are actually known.

Before using a function, I want to describe its expected result on paper. With five records, that takes little time and exposes uncertainty earlier than a finished chart. I pay particular attention to zero versus missing values, row counts after joins and the meaning of dates. A ticket’s creation date and closure date produce different period-based summaries.

The Microsoft Learn description of a data analyst includes preparing data and working with report stakeholders. That is a useful reminder that operating a chart interface covers only part of the work. I do not treat one vendor’s description as a complete definition of every role, but it helps me check whether a learning plan extends beyond interface operations.

A small sample that exposes a substantial problem

The table below is our entire example. Closed means a resolved ticket and Open means an unresolved one. Hours records resolution time where it is known. The word “missing” indicates an unavailable value, not zero. The final T06 row is an exact copy of the earlier record, deliberately added to represent a repeated export row.

The raw file contains eight rows and seven distinct identifiers. Removing the specified exact copy leaves seven tickets: five closed and two open. Resolution time is known for four closed tickets. Those times total twenty hours, giving a mean of five hours for the available observations. That result does not automatically describe every ticket in the snapshot.

If you replace T05’s missing time with zero and divide by five closed records, you obtain four hours. The calculation is possible, but you have introduced an unsupported assumption that an absent measurement means immediate resolution. We also do not know how much longer the open tickets will take. With no dates in the table, the result cannot honestly be presented as a weekly trend or a comparison between months.

Practise foundations through deliberate changes

The first exercise is to reproduce those counts and the mean in a tool you already know. A spreadsheet or SQL is sufficient. Record how you selected closed tickets, identified the copy and chose the denominator. For SQL, begin with simple queries and result checks. Your choice of software does not change the meaning of a missing resolution time.

Next, give T05 a known time of ten hours. The mean for five closed tickets should become six hours, with no missing measurements in that group. Restore the missing value and add another T06 copy. The raw row count grows, but the distinct-ticket count remains seven. Each experiment examines a different assumption in the process.

Finally, change one T06 copy’s status to Closed. You now have conflicting descriptions of an identifier rather than two identical records. Without an update timestamp or a source rule, you cannot reliably select the correct state. This is a useful point to stop and document a question for the data owner. Independent work includes recognising when the available information is insufficient.

Mistake two: choosing a project larger than the problem

Instead of this exercise, you could plan an API integration, a cloud database, an automated delay-prediction model and an extensive dashboard. Each element might be worthwhile, but introducing them together makes the actual obstacle harder to identify. Configuration issues become mixed with data issues. For a first independent project, I would choose one file, one question and one recipient.

Our recipient is a support lead who wants to know whether the snapshot supports an assessment of resolution time. The output will include counts, the mean of known times, the missing measurement and a list of additional data needed for further evaluation. It will not promise a forecast of future workload. We have no arrival history, working schedules or information about case complexity.

I write down that scope before starting. Ideas such as working-hours calculations or ticket categories go into a separate list for later. An interesting idea does not automatically become a condition for finishing. Once the first version is delivered, you can deliberately choose an extension and agree on its expected result. Progress then becomes visible through completed tasks.

Mistake three: copying without making your own decisions

Reproducing a tutorial helps you learn a tool. The difficulty begins when you present that copy as evidence of independently solving every problem. The tutorial’s author selected the question, dataset and sequence, often removing inconvenient exceptions. To check your own understanding, close the instructions and try rebuilding the analysis from the objective and raw file.

You do not need to remember every function name. Consulting documentation is an ordinary part of the work. What matters is choosing an operation, explaining its result and recognising when it does not fit the data. In our example, checking conflicting statuses could be your own extension. It is small, but it requires a decision that cannot be replaced by reproducing a chart.

Clearly credit the exercise’s source, the assistance received and your changes in the portfolio. If AI generated some code, read it and execute test cases. Explaining why a missing duration did not become zero provides more concrete evidence of understanding than declaring a project “fully independent” when you cannot modify its solution.

A portfolio should make the work reproducible

Describe the question, data source, row meaning, transformations, result and limitations. Include instructions for running the project and identify the environment used. The GitHub README documentation explains its role as an introduction to a project. A repository containing a file called final does not tell a visitor where to begin.

Our exercise needs only a compact control table: eight input rows, seven tickets after exact-copy removal, five closed tickets, four known times and a five-hour mean. Place the missing-measurement explanation beside it. Extensive testing infrastructure is unnecessary for demonstrating that this result was checked. The checks should cover the actual ways this task could produce a misleading answer.

For public portfolio work, choose data you can share or create synthetic records. A workplace export does not become public material simply because you remove a name column. If you are unsure whether publication is permitted, select another dataset. A synthetic example also lets you deliberately include the duplicate and missing value you want to analyse without revealing real customers’ information.

Mistake four: expectations that do not fit the role

A first analytical job may involve maintaining reports, reconciling definitions and repairing existing files. If you imagine it exclusively as open-ended experimentation with new models, inspect the actual responsibilities before deciding. A title is insufficient. Two teams hiring a data analyst can have different sources, working rhythms and expectations of independence.

During an interview, I would ask for an example of a new starter’s task, how results are reviewed and what help is available. I would want to know who explains column meanings and what happens when two reports disagree. These questions concern everyday work. They help assess the learning environment more precisely than a list of technologies in an advertisement.

I would not assume that several projects guarantee employment or that silence from an employer proves a lack of aptitude. Record the roles you apply for, their requirements and where the process ended. Interpret patterns cautiously, especially with few observations. Improve what you can influence: clear descriptions, relevant examples and specific explanations of your contribution.

Mistake five: reporting a number without its meaning

“The average is five” leaves too much room for assumptions. Five what, for which group and over what period? For our example, I would say: “The mean known resolution time for four closed tickets is five hours. The fifth closed ticket has no recorded measurement. The snapshot also contains two open tickets and cannot establish a trend.” The recipient can see the scope immediately.

I would not add that the team performs well or poorly. We lack an agreed target, case types, working hours and comparable history. I can recommend a next step: obtain T05’s missing time and collect creation and closure dates. A recommendation should follow from the finding, with confidence proportionate to the available evidence. That is an analytical skill too.

The Government Data Quality Framework discusses quality in relation to intended use. I find that perspective useful: a tiny sample may be adequate for practising deduplication while being inadequate for assessing a department. When the question changes, I check again whether the source contains the information required to answer it.

Request a specific review and respond to it

Rather than asking only whether a project is good, ask someone to reproduce the result and identify an unclear assumption. You can also ask them to explain in their own words what the average includes. If they understand it as a result for every ticket, improve the explanation. That reveals a communication issue even when the technical calculation is correct.

I separate feedback into a result problem, an explanation problem and a proposed extension. Conflicting counts need correction. An unclear caption needs editing. A suggestion to introduce another tool may wait. This distinction helps finish the work instead of treating every comment as a reason to restart. After making a correction, rerun the check that addresses it.

If you discover an error after showing the project, record what was wrong and which result now applies. Keeping the correction history supports a conversation about your working method. You do not need to pretend that the first version was perfect. The recipient should not be left with two conflicting results and no explanation of which is current or why it changed.

When stuck, reduce the problem to a checkable sample

Imagine joining the tickets to a team lookup and obtaining nine records instead of seven. The first step is to investigate the extra rows. Choose a duplicated identifier and inspect every match. If the team code appears twice in the lookup table, the join may be executing exactly as requested while the assumption of a unique reference key is wrong.

A focused question is easier to discuss: “I expect one team for T03, but the lookup contains two entries for its code. Should we also use an effective date?” You have supplied an expectation, an observed result and an attempted diagnosis. The person helping need not reconstruct the entire project. Avoid sharing confidential records; a small synthetic sample can reproduce the same mechanism.

For a code problem, run successive stages separately. Record counts after import, filtering and joining. Find the first point where output diverges from expectation. A definition problem may require no code change at all: perhaps the report needs the current state, or perhaps it needs status history. I direct those questions to someone who understands the process.

Record a lesson you can use again

After resolving a problem, make a short note of its cause and the check that would prevent recurrence. “Fixed the JOIN” explains little. “The lookup contained two entries per code; added a uniqueness check and agreed a version-selection rule” records a decision. In the next project, you can apply that check before building the report. This creates a personal working method based on understanding specific mistakes and their consequences.

Not every mistake requires another course. Forgetting a filter once may call for a count check. Being unable to explain any join may justify returning to that part of the foundations. Distinguish an isolated oversight from a gap in understanding. Your learning plan can then respond to the actual issue instead of expanding after every failed query or difficult interview question.

After collecting several notes, review which problems recur. Choose one and build another exercise with different data but the same kind of difficulty. If you merely memorised the previous solution, the changed sample will expose that. If you understand the rule, you can explain the new result. That is a more useful progress check than reopening a finished project and observing that it still looks correct.

Define when the exercise is finished

I would consider this project complete when someone else can reproduce five hours, identify the missing measurement and explain why no trend is reported. They should also find the T06 copy-removal rule and the conflicting-status case. The input, transformation method and short findings note should be available without guessing the execution order.

Choose the next exercise from a specific difficulty. If joins remain unclear, add a small lookup and check the record count. If explaining the result is difficult, write a shorter recipient-facing note. If repeated manual steps are the problem, explore Power Query. Every extension should have its own result that can be verified.

I find this question useful when learning: what can I now do and explain without being guided through each step? The answer may be modest: detect a duplicate, reconcile a denominator or describe missing data. Independent analysis is built from these abilities. Once they are dependable, new tools have a concrete purpose, and the portfolio shows the reasoning behind the finished image.