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I have reviewed a lot of CVs over the years - when I was hiring for my team, for a data consultancy or as a paid service. There are mistakes that I see a lot, and it is not about qualifications, they are about how people present what they already have.
Today I want to give you specific advice based on where you actually are in your career - a graduate, a career changer or a seasoned professional.
Find your section. Read it. Then go and apply this to your CV.
Before we proceed - a small ad. As always, clicks on the ad links make me $1 and it helps to cover hosting fees as well as pay for matcha drunk while writing this newsletter. Thank you.
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If you have no experience yet
The biggest mistake: treating your CV as proof that you have not done anything yet.
You have done things. You just have not written them down properly.
A university project is experience. A personal dataset you analysed out of curiosity is experience. A spreadsheet you built to track something is experience.
The question is whether you wrote it up in a way that signals analytical thinking — or whether you buried it in one line that says "group project for marketing class."
Here is the formula for writing up any project:
What you did → how much data → what you found → what it means for the business.
"Used Python to analyse customer data." - is correct, but not strong enough. Try to rewrite it this way:
"Analysed 30,000 e-commerce orders using Python to identify high return rate drivers. Found one product category accounted for 40% of all returns despite making up 12% of sales."
Same skill, but presented differently.
A few more things:
Please stop calling yourself "Aspiring Data Analyst." You are a junior data analyst. Be proud of it.
Remove "Teamwork", "Time Management" and other buzzwords from your skills section. Those are implied. Use that space for actual tools — SQL, Python, Power BI, Excel.
Build three portfolio projects before you apply. One EDA (exploratory data analysis), one visualisation project (SQL + Excel, PowerBI/Tableau), one that shows a different skill. Put them on GitHub. Link the GitHub on your CV.
Your CV cannot show experience you do not have. But it can show that you went and built it.
In this YouTube video we are working on CV for fresh graduates → https://youtu.be/HtH4KqKxZ0E
And this video talks about GitHub portfolio → https://youtu.be/xXIIPsjMHI4
If you are transitioning into data from another field
The biggest mistake: starting from zero when you are not starting from zero.
I moved into data from finance. I had five years of real analytical experience — building financial models, explaining changes in numbers, presenting to stakeholders. But because none of it was called "data," I almost treated it as irrelevant. It was not.
Your past experience is not a liability. It is a differentiator. A former nurse who transitions into healthcare data analytics understands the domain better than any fresh graduate. A marketer who moves into data already knows what questions the business is asking.
The job is to translate, not to erase.
Go through your past roles and find everything that involved:
Working with numbers, spreadsheets, or reports
Identifying patterns or trends
Presenting findings or recommendations to anyone
Building or improving a process
Then rewrite it in data language.
"Managed budget reporting for a team of 20" becomes
"Built and maintained monthly financial reporting across a 20-person team, tracking variance against forecast and presenting findings to senior leadership."
Same job. Same tasks. Written for the role you are going for.
One more thing: your domain knowledge is valuable.
A CV that says "five years in retail operations, now specialising in data analytics" is more interesting to a retail analytics team than a generic data CV. Lead with what makes you different, not what makes you the same as everyone else.
If you already have data experience
The biggest mistake: listing responsibilities instead of outcomes.
Most experienced analysts write CVs that describe what their job was, not what they actually achieved.
Every data team has someone who writes SQL queries and builds dashboards. The question is what happened because of your work.
"Built dashboards and reports for the marketing team." is generic, rewrite it to →
"Built a weekly marketing performance dashboard used by 12 stakeholders across three regions, reducing manual reporting time by 6 hours per week."
If you do not have numbers, estimate.
"Improved" is vague. "Improved by approximately 30%" is a claim someone can engage with. Also numbers on your CV always capture attention, this is pure psychology.
A few things to check on your CV right now:
Does every bullet point start with a strong verb? Analysed, built, identified, reduced, automated, presented. Not "responsible for" or "involved in."
Is your most impressive work at the top? Within each role, reorder your bullets so the strongest one leads. People read the first line of each section and skim the rest. Also, re-arrange bullet points based on the job description of the job you are applying to.
Are you showing progression? If you have been in data for three or more years, your CV should show that you have grown. More senior projects, more complex problems, broader impact. If it reads the same across every role, that is a problem.
One final thing for everyone: tailor the last 20% of your CV for each application.
Not a full rewrite — just the summary, the skill order, and the project or bullet you lead with.
Mirror the language of the job description wherever it genuinely matches what you did.
If the posting says "stakeholder communication" and your CV says "presented findings to professors" — update the wording. Make them do less translation.
Keep pushing 💪,
Karina
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Data Analyst & Data Scientist
