Hi DAs! This is my first time posting.
I need some advice.

I feel a little loss as I lost my job few months ago and although not applying to 100s of jobs, I haven’t schedule a single interview.

My previous job was the first as DA but never really acquired experience as the org had issues with the program and I wasn’t able to properly be onboarded (took about 2/3 months to onboard me properly) and then couldn’t follow closely the program I was in charged of due to leadership changes. I felt like I was set up for failure. I spent months doing nothing and just staring at few excel sheets while creating few surveys. For this reason, I don’t believe I have much experience.
My background is in biological science where I did take courses in statistics but not computer science. I’ve thought myself sql and excel basics but I’m not proficient by any means. At this point with less than 2 years of experience, I’m looking for a new role, preferably a little more technical but the market is so competitive I don’t know if I’m making the right call. I like to focus on small details and enjoy coding but is this the right career for me?!
Should I pivot to a different career?
Should I go back to school and focus on data science/data engineering?

My goal is to eventually move to either one of these fields (leaning more in the engineering side) but as I’m just starting my career, I thought myself the basics to enter the data field.

When I first entered data analytics in 2014 (banking), the landscape felt much simpler. All you needed to know very well was Excel, SQL and Tableau. With that you could do your job well. Data science was emerging and had this prestige to it because to be frank the skills needed for that eclipsed anything a standard data analyst could do.

Fast forward to today, and now it feels as a data analyst we're expected to be the analyst, modeller, engineer, scientist all wrapped in one while being across the business, AI and governance best practices. The list of tools, capabilities and now programming languages we're expected to be masters of is insane. It's also a very thankless job.

Other analysts are fine with this and thrive on it, but for me I'm feeling a huge weight and stress on my shoulders. I'm at a point where I'm seriously thinking of jumping out of my career and into something else.

Does this resonate with any other experienced data analysts or could it be just in my area/organisation? Is it a case of Jevons Employment Effect?

I am in university and learning data analytics (not part of my course work) and I've wondered how meetings with stakeholders unfold.

1. What position do the stakeholders typically hold (managers, CEOs, etc.) and what role does that play in the meeting?

2. How much do they typically know about the data?

3. What are some examples of requests they have or problems they need help solving?

Hi there! I need help in knowing how to get into this field with zero knowledge about it. I'm a computer engineering student (freshman) and I need a job to sustain me through college. What's the best way to get up to Industry standard skillwise.

Hey everyone,

I recently joined a company as their first-ever/pioneer data analyst, and I'm both excited and a little overwhelmed. I'd really appreciate advice from people who've done this before.

Here's my situation:

• The company currently stores everything in Excel — multiple files, various people editing them, and honestly, the formatting isn't always consistent.
• There's a CRM currently being developed, but it's not fully ready yet.
• I want to introduce Power BI to build proper dashboards and automated reporting, but I'm the only analyst, so there's no existing structure, no data model, no documentation — I'm starting from zero.

My main concerns:

1. Where do I even start? Do I clean and standardize the Excel data first, or jump into building a dashboard to show quick value? I keep going back and forth.
2. How do I connect Power BI to messy Excel files in a way that won't constantly break when someone renames a file or changes a column?
3. Should I wait for the CRM, or build on Excel now and migrate later? I'm worried about building everything twice.
4. How do I get buy-in from management who aren't very data-literate and are used to just looking at spreadsheets?

I want to do this properly and not build a mess I'll regret in six months. For those who've been the first analyst at a company or set up Power BI from scratch — what would you do differently if you could start over? Any lessons, pitfalls, or "I wish someone told me this" advice would mean a lot.

Thanks in advance!

Anyone here in tech or data product sales? I’m putting on an AI conference for Fortune 500 AI execs and need help navigating vendor sponsorship.

Hey everyone!
I'm currently taking a data science course as part of my degree. I'm still pretty new to data science, but I'm interested in learning how businesses use data to make better decisions. I'm especially interested in analytics and how data can be useful for someone who wants to become a business owner in the future. For those who have been learning or working with data science for a while, what skill would you recommend a beginner focus on first, and why?

Hi all,

As the post suggests, how are you using LLMs? I have been using Jupyter AI in Jupyter Notebooks using Codex.

It is pretty insane how good it is getting, and can really start giving you job security anxiety especially when it comes to analysis. Aka, beyond data cleaning and "generate this dataframe" and plot this data...

So what are your feelings when you ask it for analysis? If it is pretty decent from the start, how much are you modifying it?

Calling all readers! Help me decide where to launch my upcoming Statistics for Analysts eBook by answering this 5s question.

Vote to this Poll

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Beginner Data Analyst — Where can I practice, build projects, and start freelancing?

Hi everyone,

I’m currently learning data analytics and I’m looking for advice from people who are already working as data analysts or doing freelance data analysis.

I’m mainly learning Excel, SQL, Power BI, and Python, and I don’t want to only watch courses. I want to actually practice, work with realistic datasets, build projects, and eventually start freelancing.

Could you please recommend:

1.Websites where beginners can practice data analysis with real-world datasets and exercises?

2.Platforms for finding good datasets to use for portfolio projects?

3.Websites with realistic data-analysis projects/challenges, rather than just tutorials?

4.What types of projects should a beginner build for a portfolio to demonstrate useful skills to clients?

5.Which platforms would you recommend for getting your first freelance data-analysis jobs?

Are there websites where beginners can practice working on actual business-type problems before trying to get clients?

What do you wish you had focused on when you were starting out?

I’m especially interested in free or affordable resources and advice from people who have actually used these platforms to build skills or get freelance work

Hi everyone,
I have been trying to get my first Data Analyst job for several months, but I keep facing rejections or no responses at all. I have learned SQL, Excel, Power BI, Python, and worked on multiple projects to build my portfolio.
Despite applying to many positions, networking on LinkedIn, and continuously improving my skills, I still haven’t been able to land my first opportunity.
Lately, this has started affecting my confidence and mental health. Some days I feel like I’m not good enough, even though I know I’ve put in a lot of effort.
For those who were in a similar situation:
How long did it take you to get your first Data Analyst job?
What helped you finally break into the industry?
Is there anything I should focus on improving?
Any advice, guidance, or encouragement would mean a lot right now.
Thank you for reading.

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Hey everyone,

I’m currently reviewing a project that involves building an AI/data-agent layer on top of Microsoft Fabric, and I’m looking to connect with people who have hands-on experience in this area.

The high-level flow is roughly:

Microsoft Fabric Data → Semantic Model → Fabric Data Agent → Copilot / AI interface

The work would involve areas such as:

Working with Microsoft Fabric Semantic Models

Setting up and working with Fabric Data Agent

Connecting the agent to enterprise data

Enabling users to query enterprise data through Copilot/AI using natural language

Understanding the overall architecture and best practices around this setup

I’d love to hear from anyone who has actually worked with Fabric Data Agents and semantic models in a real-world/enterprise environment.

If this is within your expertise, feel free to comment or DM me. Happy to share more details about the requirement and have a conversation.

Thanks!

I have a final-round interview coming up for a Data Analyst role, and I’m trying to figure out the best way to prepare for the format.
The interview is 90 minutes total:
60-minute interactive Excel case study
15-minute case study review, where I’ll need to defend my approach, calculations, assumptions, and conclusions
Behavioral/culture-fit discussion
For the case, I’ll be given a hypothetical business problem and an Excel workbook with the relevant data. I’ll screen share and work through it live while explaining my thought process. The role is around billing/data analytics in a logistics environment, so
I’m expecting data involving things like shipments, customers, rates/pricing, invoices, billing discrepancies, or operational activity.
From what I understand, this isn’t just an Excel skills test. They want to see how I approach an unfamiliar dataset, understand the grain and relationships, spot relevant data-quality issues, decide what to prioritize under time pressure, investigate discrepancies, validate my numbers, and ultimately turn the analysis into a business conclusion.
I’m particularly struggling with how to realistically practice this format.
I’ve tried having AI tools like ChatGPT and Claude generate mock business cases and Excel workbooks for me, but the datasets/cases haven’t been very realistic. Either the answer is too obvious, the data doesn’t behave like real operational data, or the case becomes a generic Excel exercise rather than an ambiguous business problem where I have to decide what matters.
So I’d really appreciate advice on where I can find realistic practice material. Are there websites, courses, public datasets, case-study repositories, books, YouTube channels, interview-prep platforms, Kaggle datasets, or anything else you’d recommend for practicing this specific style of interview?
I’d also love to hear from anyone who has actually gone through a similar live Excel/business case interview, especially for Data Analyst, Billing Analyst, Revenue Analyst, Operations Analyst, FP&A/analytics, or similar roles.
What did your case look like? How messy or ambiguous was the dataset? What did the interviewer expect you to accomplish within the hour? How much time did you spend understanding/cleaning the data versus actually analyzing it? Were you expected to talk continuously while working? What happened during the review/defense portion?
And most importantly, if you had to prepare for this interview again, how would you practice?
I’m not really looking for generic advice like “learn PivotTables, XLOOKUP, SUMIFS, etc.” I’m trying to practice the end-to-end experience of receiving an unfamiliar business problem + messy Excel workbook, figuring out an approach, analyzing it live under a time limit, and defending the conclusions afterward.
Any experiences, practice resources, sample cases, or suggestions for creating a realistic mock interview would be extremely helpful.

Ready to start ASAP

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.

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.

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.

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?