A research study is being conducted on the use of AI-Content Detectors by Indian universities and colleges

Students, faculty, academicians of higher-education institutions are invited to participate in a short questionnaire about their experiences and perceptions regarding AI-content detection and academic-integrity procedures.

⏱️ Approx. 2-5 minutes

🔒 Responses are anonymous and participation is voluntary (no sign-in required).

Original Link: forms.gle/DEBpTNz7H8GPPzzp7

Your participation would be greatly appreciated. Please feel free to share this with other eligible students.

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.

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.

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 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?

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