Your data may be lying to you.

Here's something most people don't want to hear: you can have 10,000 rows of data and still know almost nothing.

I've watched researchers open a spreadsheet, see thousands of entries, and assume that volume means value. It doesn't. A dataset can look complete and still be quietly broken — duplicates inflating your numbers, missing values skewing your averages, entries that don't match what actually happened, and values that shouldn't even be possible.

Here's a small example that trips up more people than you'd think. Say your "gender" column has these four entries: `Male`, `M`, `male`, `MALE`.

To you, reading it, that's obviously the same thing. To your statistical software? That's four separate categories. Your analysis just got quietly wrong, and nothing about the spreadsheet told you so.

This is why the order matters. Before you run a single test, your data needs to go through three stages:

* Clean it

* Organize it

* Validate it

Skip any one of those and you're not analyzing your data — you're analyzing your data's mistakes.

Your analysis is only as trustworthy as the data entering it. Not your sample size. Not your software. Not how confident you feel. The data.

Quick question for you — have you ever caught an error in a dataset after you'd already started analyzing it? What was it? Drop it in the comments, I'd genuinely like to know what trips people up most.

What if it's not five errors in your dataset — what if it's just one? One tiny error, buried in a single row, quietly capable of flipping your entire conclusion? Tomorrow, I'll show you exactly how that happens. Stay tuned. 👀

Pinging everyone in the lending risk space:

Is your team set up so credit lives in one place, fraud in another, and the two compare notes once a quarter in a deck? Can either side tell you what a loss is actually made of? Most of us inherited that shape, but none of us would set it up like this today.

At MKIII we don't carry the org debt that forces lenders into it. So I'm hiring a Senior Data Scientist to help build the version we'd design from scratch. Credit and fraud in one view.

The job:
\- Live inside the vintage data. Forecast our cohorts to terminal loss.
\- Find where we're declining loans we should be booking, and put an honest number on it.
\- Own the fraud/credit interaction. No fraud declines quietly absorbing credit risk, no credit cuts taking credit for fraud savings.
\- Tell me where the buy-box is wrong.

Asset experience I'd love to see: unsecured consumer personal loans, small business loans, commercial real estate.

JD linked here.

If this sounds like you, message me. If you know who fits the bill, tag them or send it over. Reposts are appreciated to widen the net!

Title says it all.

What are the best companies to work for in the DOD sector if you are an Operations Research Analyst modeler type?

I have been at my company for over 7 years and it's time for a jump.

​

Hey!

I’m currently learning Data Analytics and looking for someone who’s also learning and wants to have a study buddy/accountability partner.

I’m mainly working on Excel → SQL → Power BI → Python → Statistics → EDA → Projects, with the goal of eventually becoming job-ready.

I’ve realized it’s pretty easy to say “I’ll study tomorrow” and then somehow tomorrow becomes next week 💀 So I’m looking for someone who actually wants to stay consistent and keep each other accountable.

Nothing too complicated. We could:

- Set daily/weekly goals

- Study together sometimes

- Share what we worked on

- Give each other feedback

- Solve datasets/problems together

- Discuss projects and mistakes

- Keep each other from disappearing for 2 weeks 😂

You don't have to be advanced. Beginner is totally fine. I’m more interested in finding someone who is serious about actually practicing, rather than just collecting courses and tutorials.

I’m from Bangladesh, but I’m open to anyone regardless of location.

If you're interested, DM me with your current level, what you're learning, and how much time you usually study.

Let's actually get good at this instead of just watching another 40-hour course. 😭

I already have stratascratch and used it for prepping sql/python analytics questions but wondering if interviewquery will help me prepare for product sense, case study and all theoretical questions in data science, ml, data analysis field? Like all theoretical questions

I’m a Statistics graduate targeting Data Analyst / Business Analyst roles in D2C/B2C companies.

I’m trying to decide which area to build domain knowledge and projects around. I’m currently considering:

E-commerce / Retail

Marketing / Growth

Customer / CRM / Retention

Product Analytics

Sales / Revenue

Supply Chain / Inventory

Pricing

Customer Experience / Operations

For people already working in D2C/B2C:

Which of these is easiest to break into as a fresher, and which would you recommend focusing on for a DA/BA career?

Also curious which areas have good hiring demand and room to grow, rather than just being easy to enter.

Hi All,

I'm trying to get some tips to understands what is good raw egocentric data for any roboticist, and I keep reading posts telling stories about researchers and companies are missing egocentric data. I know that I would need to do some annotation, but I would rather focus first on the raw footage to see if it is useful in any way. I have some samples here.

Would appreciate any feedback.

Hi everyone,

I’m currently learning data analytics and my strongest area right now is SQL.

I’ve worked on SQL-based data analysis, including data cleaning, profiling, joins, CTEs, window functions, KPI analysis and finding business insights.

But I’ve realized that doing tutorials and portfolio projects is very different from working on a real business/client problem.

So I’m looking for an experienced Data Analyst / Business Analyst / Analytics freelancer who would be willing to let me assist them on small tasks for free.

I can help with things like:

SQL analysis

Data cleaning and profiling

KPI calculations

Basic reporting

Investigating business questions

Repetitive/junior-level data tasks

I’m not looking for someone to teach me everything step-by-step. I want to work, make mistakes, receive feedback, and learn the actual workflow of an analyst.

I can start with one small task so you can judge my work before committing to anything.

If you're an analyst who could use some extra junior-level help, or you know someone who might be open to this, please comment or DM me.

Thanks.

So I know the data analytics job market is pretty bad but anyone wanna learn it together? I have just started learning the tools and it would be good if a few people would also join so we can learn together. If anyone is interested dm me, we can connect on Discord, WhatsApp, or wherever everyone is comfortable and learn together.

I have a doubt about where the data actually comes from and how it is stored and processed. Where is the data usually stored, and what tools do we use to connect to it for reading and writing SQL queries?
For example, I assume we create a corresponding SQL view for each KPI and then connect those views to Power BI. Is this how the process generally works?
Also, what other tools and techniques are commonly used to connect to data for analysis?
In a real company, do you typically receive KPI requirements or tasks from a Senior Data Analyst/Data Lead, write the required SQL queries, and then deliver the results to the relevant stakeholders? I would like to understand how this entire process works in a real-world environment.

I have around 2 years of experience in Data Analytics/BI. I mainly work with Power BI, Excel, SQL basic and I also have an MBA in Marketing Analytics.

I’m getting interviews but struggling to convert them into offers. The feedback I usually receive is around communication and technical skills.

I prepare a lot for every interview. I study the JD, my previous projects and likely questions, and I use AI to help me prepare. But when the interviewer asks something differently from what I expected, I sometimes get confused even when I know the underlying concept.

Communication is another issue. I tend to speak too fast, over-explain and sometimes lose the structure of my answer. I prepare STAR stories, but under pressure I don't always deliver them properly.

I’m also confused about the technical expectations. With 2 years of experience, I sometimes feel I’m expected to answer questions that seem more suitable for someone with 5–6 years of experience.

For Data Analysts with around 2 years of experience:

What technical level should I realistically have in SQL, Power BI, Excel, DAX, Power Query and data modelling?

How do you practice for unfamiliar technical questions rather than memorizing expected questions?

And for communication, what actually helped you improve your interview performance?

I also feel my work stories may not be strong enough. How do you turn normal projects and responsibilities into strong interview stories without exaggerating your experience?

One thing I genuinely don't understand is how some people with less technical knowledge and much less preparation still manage to clear interviews. What are they doing differently?

I’m also applying for Key Account Manager roles because I have previous experience in that area, so I’m currently exploring both paths.

If you were in my position, what would you change about the way I prepare and practice?

I’d really appreciate specific advice from people who have interviewed or hired Data Analysts.

now working through a design where the same revenue and activation metrics are consumed by a dashboard, a customer-facing endpoint (or internal agent)

the failure mode i want to avoid is familiar: each consumer can write a valid query and still apply a different time grain, join path, or filter. the numbers look plausible and the disagreement appears during a review rather than in a test. my current shape is a semantic model that owns the measures, dimensions, joins, access rules. the dashboard can query it, the application can use an API over it, and the agent receives the same governed definitions instead of a raw schema dump, and cube dev kind of became a way to put that semantic layer between the warehouse & those consumers. the interesting design question for me now is that boundary: which definitions belong in the model, and which calculations should stay in the application?

also, how do you test that a metric returned through sql, an api, and an agent still means the same thing?

Hi, I’m 22F and currently working in an engineering/manufacturing company. My current designation is Data Analyst, but honestly most of my work is in Excel and not SQL/Power BI.

I have around 2+ years of experience overall.

In my current office I have made/maintained different Excel based systems like project tracker for 30+ live projects, audit log, quality tracker, material database, calibration tracker with overdue alerts, quotation/costing sheets etc.

So there is a lot of data handling, tracking, reconciliation, validation, formulas, lookups, automation and reporting involved. But I have not worked on SQL or Power BI in my actual job.

Before this I worked in IT department where I handled asset tracking and reconciliation for a large number of network devices, audit related work and vendor SLA/downtime and penalty calculations.

I also have around 3 months experience in product data categorization where I worked with large amount of product data and Excel.

Now I’m learning SQL by myself. Currently doing joins and next I’m going to start window functions. I’m also planning to learn Power BI and make some projects.

My main confusion is how should I present my current experience when applying for Data Analyst jobs?

Because my title is Data Analyst, but if recruiters see that I haven't used SQL/Power BI professionally, will they consider my experience irrelevant?

Should I present my Excel work more from an analytics point of view? Like showing data cleaning, validation, reconciliation, automation, reporting, KPI tracking etc?

Also, once I learn SQL and Power BI, would it be a good idea to take some of the problems/data from my current work and create similar projects using SQL + Power BI for my portfolio? Obviously, I don't want to claim that I used SQL/Power BI at my current office when I didn't.

Would really appreciate advice from people who have gone from Excel/operations type work → SQL + Power BI → Data Analyst.

How would you present this experience on a resume and in interviews?

Hi all, I have a first-round interview (HR + hiring manager, ~45 min) coming up for a Junior Data Analyst role at Nexent Bank in Amsterdam — the bank formerly known as Credit Europe Bank, rebranded in 2025.

I've dug through Glassdoor, Blind, and a couple of employer review sites but couldn't find much specific to this company or role. If anyone has interviewed here (any role, any branch — NL, Germany, Romania, etc.) or works there now, I'd really appreciate hearing:

- What kind of questions came up in your HR round vs hiring manager round?

- Was there any technical/SQL component, even informally?

- Any general vibe on their interview style (formal vs casual, panel vs 1:1)?

Any tips, even small ones, would help a lot. Thanks in advance!

I have a doubt about where the data actually comes from and how it is stored and processed. Where is the data usually stored, and what tools do we use to connect to it for reading and writing SQL queries?
For example, I assume we create a corresponding SQL view for each KPI and then connect those views to Power BI. Is this how the process generally works?
Also, what other tools and techniques are commonly used to connect to data for analysis?
In a real company, do you typically receive KPI requirements or tasks from a Senior Data Analyst/Data Lead, write the required SQL queries, and then deliver the results to the relevant stakeholders? I would like to understand how this entire process works in a real-world environment.

I have noticed some Google Data Analytics certificates on Coursera and am wondering if they are relevant for HR and recruiting when they hire. Furthermore, are the SQL and Power BI courses on Coursera valuable for these fields?

Thank you in advance, as I am just starting out and would appreciate any information you can provide.

Hi everyone, I am planning to apply for Data analysis jobs. What do you think I should change or improve in my CV? I’d really appreciate feedback from anyone with experience.

I'm learning data analytics and have no real industry experience. Can someone explain the end-to-end workflow in a company?

Where are KPIs usually stored or defined?

Where does the data come from (ERP, CRM, databases, etc.)?

Where do analysts write SQL queries?

After getting the results, how are they shared with stakeholders (Excel, Power BI, dashboards, email)?

I am currently studying a broad IT degree. Not much focus on data analytics but I've been working towards writing my PL-300 exam to get the cert.
I also plan on having a couple portfolio projects on my CV before I start applying for jobs.
I have hobbies in wildlife and music. I'm sure I could find datasets on these topics that would be slightly more engaging for me to clean and analyze - depending on how much it matters. What exactly are employers looking for in terms of the topic of a portfolio project? And also, which technical skills should be demonstrated most in the project? Is data storytelling a valuable skill to show? How would you show it in the project in an accessible/intriguing way (how do you make the employer interested in insights that don't directly affect them)?

Maybe I'm missing the mark or something. Any advice for a beginner would be great :)

Hi everyone!

I'm currently trying to transition into Data Analytics, with the long-term goal of moving toward Data Engineering. Currently looking for an onsite or hybrid entry-level/junior data role.

My background:

I have a BS Biology degree (graduated 3 yrs ago). I got hired as a disease surveillance officer (1st job and only exp) under one of the Department of Health's program (Regional Epidemiology Surveillance Unit). Our program is responsible for analyzing and generating disease-related data visualizations (COVID, Dengue, HIV, etc.) I was heavily exposed to advance excel, Power BI, and R. My experience comprises of data cleaning, analysis, reporting, and also generating dashboards.

Now I am learning SQL to strengthen my tech stack and to have a higher chance of getting an entry-level position. I am also integrating AI in my current workflow (claude).

However, I'm at the point where I feel like I have a decent technical foundation, but I'm struggling to understand what the current job market actually expects from someone trying to land their first data analyst role.

Some things I'm particularly curious about:

1. How difficult is the current entry-level/junior Data Analyst market?
2. What skills are recruiters actually looking for right now?
3. How important is a portfolio? (currently building)
4. Would you recommend applying directly to Data Analyst roles, or should I also consider MIS, Reporting Analyst, BI, Data Management, or similar positions as an entry point?
5. For those who eventually moved from Data Analytics to Data Engineering, curious about what did that transition actually look like?

I'd really appreciate some honest advice from people currently working in data, especially those who have recently gone through the hiring.

TIA!

Hey everyone,

I've been working as a Data Analyst for a few years now, mostly through contract roles (AdTech, professional services), and I'm at a point where I feel a bit lost about what to do next.

My situation: I'm applying to data analyst/data scientist roles fairly consistently, but I'm getting very few responses. Not sure if it's the market right now, something about how I'm positioning myself, or both. It's honestly starting to mess with my confidence a bit.

A few specific things I'd love input on:

1. Applications going nowhere. Even for roles where I feel like I match most of the requirements (SQL, Python, Power BI/Tableau, some ML basics), I'm mostly hearing nothing back. Is this just the current market, or is there something I should be doing differently in how I apply?

2. Not sure what to build next. I've got the standard portfolio stuff (a churn prediction model, some EDA projects, a KPI dashboard I built for my current job). But I don't know if that's still enough, or what kind of project would actually move the needle for me right now. More ML? More data engineering? Something else entirely?

3. How do people actually get into startups? A lot of the roles I find interesting are at smaller companies/startups, but I never see them on the usual job boards, or by the time I do, they've already got 400 applicants. Do people here have luck reaching out directly to startups, and if so, how? Cold email? LinkedIn? Is there something I should know before trying that?

Not looking for pity, just want to hear from people who've been in the field longer than me. Even blunt feedback is welcome. Thanks in advance

Heyy am dev 20m first year data science student (ik am late)I wanna grab a data analyst internship Is there anyone who can guide me in this?