How much of a data analyst's day is spent writing SQL?

Most of it. On a typical day, 3 to 5 hours go into writing, testing, and fixing SQL queries. That's the bulk of the work, not a side skill. The queries aren't glamorous—you're pulling a table, checking if the numbers match what someone expects, joining it to another table, and formatting it so a non-technical person can read it.

Do data analysts use Python every day?

Not at the entry level, and often not later either. SQL and a spreadsheet tool cover most entry-level and mid-level analyst work. Python shows up more in data engineering, machine learning, or advanced analytics roles, not in the daily grind of a standard analyst seat.

What meetings does a data analyst attend?

A typical week includes a team standup, one or two stakeholder meetings where you present or explain a number, and the occasional call where someone wants to understand a dashboard they can't read on their own. The skill that matters most in these meetings is explaining a number in one sentence to someone who doesn't care how you got it.

Is being a data analyst boring?

Some of it, yes. Most days include stretches of repetitive query writing and data cleanup that nobody would call exciting. The interesting part shows up when a chart finally answers a question someone's been asking for weeks, or when you catch an error before it reaches a director's inbox.

What's the difference between a junior and senior analyst's day?

A junior analyst spends more time on execution: writing the queries, building the dashboard, fixing the data someone else flagged. A senior analyst spends more time deciding what should get built in the first place and pushing back when a request doesn't make sense. Both still write SQL most days. The senior version just spends less time on syntax and more time on judgment.

What tools does a data analyst use daily?

In order of frequency: SQL editor, a BI tool like Tableau or Looker, spreadsheets like Excel or Google Sheets, Slack or email, and Python (occasionally). SQL editor is used most, while Python ranks lower than many expect, especially at the entry level.

How to Get Data Analyst Portfolio Feedback Before You Apply

The best way to get data analyst portfolio feedback before you apply is to put 1 finished project in front of 3 kinds of reviewers: another analyst, someone who hires, and someone who knows nothing about data. Give each a short write-up, a link that opens fast, and 3 specific questions. Fix what confuses people first, then apply.

Most applicants skip this step and use their first applications as the test. That's an expensive way to find out your dashboard link is broken or your write-up has no conclusion. This post shows where to find reviewers, what to send, the questions to ask, and how to use the answers.

I'll be honest about my own version of this. I had no portfolio site, only 3 project links on my resume: 2 Tableau Public dashboards and 1 Power BI project. I'm self-taught with no relevant degree, and I failed roughly my first 10 interviews. I rambled when I explained my work and lost my place partway through answers. Talking through a project out loud with another person is feedback too, and it fixes that problem before an interviewer sees it.

How do you get portfolio feedback before applying?

Follow this short sequence:

Finish 1 project and write a 1-page summary.
Check that the link opens in 10 seconds without a login.
Pick 3 reviewers: an analyst, a hiring-side person, and a non-technical friend.
Send each one the link, the summary, and 3 specific questions.
Collect the answers, fix the confusing parts, and ask again if the changes were big.

That's enough for most people. The goal isn't praise. It's finding the 2 or 3 things that would make a stranger close the tab. What a portfolio that gets calls looks like shows what you're aiming for.

Why get feedback before you apply

You only get one first impression with each employer. Once an application is in, you rarely get a second look at the same job, so mistakes cost you a real opportunity.

Feedback beforehand helps in 4 ways:

It catches blind spots. You know what the project means, so you can't tell what's missing from the page.
It fixes small things cheaply. A broken link, a confusing chart title, or a missing conclusion takes minutes to fix once someone points it out.
It builds your explanation. Explaining the project to a reviewer is practice for the interview.
It builds confidence. Applying with a project other people understood feels different from applying and hoping.

If you're still deciding how much to build, how many portfolio projects you need covers the minimum that works.

Make the project feedback-ready

Don't send a reviewer a pile of files and a vague request. A reviewer helps you more when the project is easy to open and the ask is small. Get these 4 things in place first:

A working link. Open it in a private browser window to confirm it loads without your login.
A 1-page write-up. It should state the question, the data source, the finding, and the recommendation. How to write a project README has the structure.
A clear question. The reviewer should know in 1 sentence what you were trying to learn.
Your 3 questions. Decide what you want to know before you send it.

If the link needs a place to live, where to host your data analyst portfolio covers the options.

The 3 reviewers you need

Different reviewers catch different problems, so ask 3 types of people instead of 3 of the same:

A working analyst. They'll check the SQL, the metric definitions, and whether your method holds up.
Someone on the hiring side. A recruiter, hiring manager, or team lead can tell you whether the project reads like the work the job needs.
A non-technical person. A friend or family member tells you whether the summary makes sense to someone with no context.

The third one is easy to skip and often the most useful. If a non-technical reader can't tell what you found, a recruiter skimming 50 applications won't get it either. Hiring managers are busy, and clarity wins.

Where to find reviewers

You probably know more potential reviewers than you think. These are the places to look:

Your current job. A coworker who uses data, even if their title isn't analyst. Using your current job as a portfolio project covers what's safe to share.
Analyst communities. Subreddits like r/dataanalysis, Slack and Discord groups, and the forums for tools like Tableau and Power BI.
LinkedIn. Analysts you've interacted with or who post about hiring will often answer a short, specific message.
Local meetups and events. A short conversation in person can turn into a 10-minute review.
Learning communities. Other people working toward the same goal make good reviewers, and you can return the favor. The Analyst Hive community is one place people work through the same steps together.
Friends and family. Perfect for the non-technical read.

Don't ask a stranger for a full audit. A narrow ask gets answered. A broad one gets ignored.

6 questions worth asking

Specific questions get useful answers. "What do you think?" gets "looks good." Pick 3 from these 6:

"What do you think the main finding is?" If they get it wrong, the project isn't clear.
"What was the first thing that confused you?" This finds the friction point fast.
"Would anything in the analysis make you doubt the result?" This tests your method and caveats.
"What would you want to see added?" This surfaces missing context or a missing recommendation.
"Does this look like work an analyst would do on the job?" This tests the business framing.
"How long did it take you to understand it?" If the answer is more than a minute or two, simplify.

Ask the first one before you explain anything. What a reviewer understands without your help is what a hiring manager will understand.

How to ask without being annoying

Keep the request short, make it easy to say yes, and respect the person's time. A message like this works: "Hi, I'm putting together my first data analyst portfolio and I'm applying soon. Could you spend 10 minutes looking at 1 project and tell me what's unclear? Here's the link and a 1-page summary. I have 3 specific questions."

A few things make the ask easier to accept:

Say exactly how much time you're asking for.
Send everything in the first message, so they don't have to reply to get started.
Offer something back, like a review of their work or a thank-you note.
Follow up once, politely, if you don't hear back in a week.

Say thank you afterward, and tell them what you changed. People remember the ones who act on their advice, and they're more likely to help again.

How to use the feedback

Don't change everything a reviewer says. Sort the feedback into 3 buckets before you edit anything:

Confusion. Anything that more than 1 person didn't understand. Fix this first.
Errors. Wrong numbers, broken links, and typos. Fix these immediately.
Taste. One person's preference about colors or layout. Note it, and only change it if a second person agrees.

When 2 or 3 reviewers point at the same problem, that's real. When feedback conflicts, go with the reviewer who matches your target job most closely. An analyst at a company like the ones you want to join counts for more than a general opinion.

After a big change, send the updated version back to 1 of the original reviewers. A 2-minute second look confirms the fix worked. Then practice explaining the project out loud in 2 minutes. If you ramble the way I did, tighten it now, because what to do after failing a data analyst technical interview is a lot easier to read before it happens to you.

Mistakes to avoid

Most people who collect feedback make the same few mistakes:

Asking too late. Feedback after you've applied can't change the first impression.
Asking only friends. Friends are kind, which makes their feedback less useful.
Explaining too much. If you talk the reviewer through it, you hide the problems you need to see.
Defending your work. Argue with feedback and people stop giving it.
Waiting for perfect. Collect feedback on a finished project, not a flawless one. Good enough to review is good enough to start.

That last one traps a lot of people. A project can improve forever, and the job search can't wait. Portfolio projects that don't look like tutorials helps you judge whether yours is ready.

The 90 days at Analyst Hive follow a clear order, with project work and interview prep built in, so you apply with something you've already tested.

What people ask about getting portfolio feedback

How do I get feedback on my data analyst portfolio?

Send 1 finished project to 3 types of reviewers: a working analyst, someone on the hiring side, and a non-technical person. Give each a working link, a 1-page summary, and 3 specific questions. Ask what they understood and what confused them before you explain anything.

Where can I get free portfolio feedback?

Try analyst communities on Reddit, Discord, and Slack, tool forums for Tableau and Power BI, LinkedIn connections, local meetups, and learning communities with other job seekers. Offer to review someone else's work in return, which makes people more willing to help.

How many people should review my portfolio?

Three is a good number, if they're different types. A working analyst checks the method, a hiring-side person checks the fit, and a non-technical reader checks clarity. More reviewers help only if they raise new issues instead of repeating the same ones.

What should I ask a reviewer about my portfolio?

Ask what they think the main finding is, what confused them first, whether anything made them doubt the result, and how long it took to understand. Specific questions get useful answers, while "what do you think?" usually gets a polite "looks good."

Can I use AI to review my portfolio?

It can help with typos, unclear wording, and structure. It can't tell you whether a hiring manager would care about your project or how a real reader reacts. Use it as a first pass and still get a human to read it before you apply.

When is a portfolio ready to apply with?

When 1 project has a clear question, a working link, a short write-up, and a recommendation, and at least 2 people understood it without your help. It doesn't need to be perfect. It needs to be clear enough that a stranger gets the point in a minute or two.

If you want a day-by-day plan that builds the project, the write-up, and the interview prep in order, join Analyst Hive and work through it in order.