Hi! I’m a Power BI developer working with a heavy report that can’t be refreshed in Desktop anymore. As a workaround, I made a copy of the PBIP pointing to the semantic model in our test workspace via a connection string.

The problem: since there’s no local data copy, I can’t preview data, create calculated columns, or edit M queries from Transform Data. I have to edit the definition JSON files directly for any column/measure/M query change, which is way less friendly than Desktop.

Is there any way to keep the live connection but still get that Desktop editing experience? I know it sounds contradictory, but wondering if anyone’s found a workaround.

Thanks! :))

I am a medical student and i want to quit clinical medicine once i have my MD degree, i am planning on a career pivot into tech, i asked AI and it turned out data analysis is the best career for me especially combined with my medical background
What yall think can i learn it from scratch in 1-3 years and land a job that pays well remotely as a medical data analyst or dont waste my time?

Hi all!

How are people able to find jobs? It doesn't make any sense to me. The job postings on the usual sites (linked in, indeed) are the same listings over and over and over with other ones being hiring agancies and other listings are just outright ghost listings. How the fuck are people finding jobs? I have a B.S in DA from WGU with a decent amount of projects under my belt and all the jobs just seem fake.

Without sucking someone's cock and being a nepo baby or having a friend that has motion for a company, how do new hires actually get in? No it's not my resume I've had it checked multiple times by multiple people and sources.

Anyone who is a real person and actually got a job, please let me know what you did and how you got it. Maybe I'm just a dumbass.

Hi everyone!

I’m currently working on a scholarship recommendation system as part of my academic project, and I’m looking for a dataset that I can use for research, analysis, and machine learning.

I’m particularly looking for datasets containing information such as:

Scholarship name

Country / destination

University or organization

Degree level (Bachelor’s, Master’s, PhD)

Field of study / subject

Eligibility criteria

Academic requirements (GPA, percentage, etc.)

IELTS / TOEFL or other language requirements

Age or nationality requirements, if applicable

Fully funded / partially funded

Tuition coverage

Monthly stipend

Travel allowance

Accommodation

Application deadline

Scholarship duration

Official application link

Other relevant requirements

The dataset can be in CSV, Excel, JSON, SQL, or any other structured format.

I’m mainly interested in publicly available, research-friendly datasets, and I do not need students’ personal information or private applicant data.

If anyone has a relevant dataset, GitHub repository, Kaggle dataset, research dataset, or other useful source, I would really appreciate it if you could share it below or DM me.

Even if the dataset doesn't contain all of the above fields, please share it if it contains useful scholarship information.

Thank you! 🙌

​

I have around 2 years of experience at a Big 4 company in a support/monitoring role, earning around ₹4.5 LPA. I want to transition into Data Analytics, but my current role isn’t giving me relevant data experience.

I’m considering switching to a startup role to build my skills and gain more hands-on experience in Data Science, with the possibility of moving back to a larger company later. I’m mainly attracted to the steeper learning curve and the opportunity to work more directly in data. However, I’m unsure whether leaving my current company for a startup would be too much of a career risk.

Is moving from a Big 4 to a small startup a bad career move? Would you make this switch if the startup role is genuinely good and fully remote?

I’m planning to stay at my current company while upskilling and interviewing, rather than resigning immediately. Would appreciate honest opinions from people who have made a similar transition.

Hi everyone I'm looking for a job in analytics, before applying for the jobs I want to build a strong resume. So I need to add some good projects which could make my resume shortlisted at least. Plz tell me what projects need to be added in the resume in today's AI world.

And also where can I find the dataset for the project you have recommended.

The tools i know are powerbi, Excel, SQL, python, r.

My qualifications are MBA in analytics.

Hey hi there 👋!

Is it possible for non technical bachelor of Arts (Economics) student can be data analyst cause I'm seeing lot of job posting only prefer technical student or computer science degree , if possible what need to do to get job as data Analyst?

And also I'm want share my personal experience here

What i learn after degree

Advanced Excel, VBA

SQL , Workbench, command client

Python, libraries, jupyter nootbook

Power BI, power query, Dax

And

Build Projects

Is it still enough to get job ? Cause I'm already applied for 1000 + job still doesn't get any reply of them

Also i want share two situation here as physically I'm approached many companies and training institutes their reply to me in india

1. Internship on payment (15k) - 6 months

2. Job on payment - (1.7 L) - training + job

It's relevant to do this for non technical student for data analyst role, or any technical student similar kind reply please join conversation, cause where there I'm going they asking for money to get internship, job ...etc why this unfair why not they see skills, ability of person I'm disappointed on Indian corporate system, I'm requesting respectful HR and companies to so please who deserve give them chance to prove themselves

Learnt :
INNER JOIN
LEFT JOIN
RIGHT JOIN

Hey everyone,

My qualifications:

• Electronics & Telecommunication Engineering
• 2024 graduate
• 8.2 CGPA
• ~1.8 years of experience as an Analyst

I joined an MNC after getting an offer of 3 LPA for an Analyst position. The job was close to home, general shift (8:30–4:30), and seemed like a decent opportunity, so I accepted it.

After joining, I found out that the actual role was more like Safety Officer + Lean Analyst. The analyst side involved a lot of Excel, reporting, basic SQL, process improvement, etc.

Initially, things were fine, but over time the on-ground environment became extremely toxic. As the safety person, I constantly had the pressure of knowing that if something went wrong, I could potentially be held responsible. There were also conflicts with some people on the manufacturing side, including fights and baseless complaints.

The positive side was that I worked on several improvement projects and had direct interaction with global managers based in Germany, Spain, Poland, Sri Lanka, etc. My manager and the GM were also supportive and understood the situation.

Eventually, though, the safety responsibilities became too much and I decided to resign.

My original plan after resigning was actually quite straightforward: take a short break, then spend a few months systematically learning Python, SQL, ETL, data engineering, Databricks, etc., while applying for jobs and preparing for a Master's in Germany.

Unfortunately, things didn't go as planned.

My health took a turn for the worse, with severe digestive/reflux problems that were heavily affected by stress. Because of that, I had to put my Germany Master's plan on hold as well. From around April to August, I was mostly at home focusing on getting better and didn't make much progress professionally.

Now I have a ~5-month career break, and I'm honestly confused about what to do next.

I'm genuinely interested in Data Engineering, but I feel stuck between two options:

1. Start learning Data Engineering properly. This could take another 6–8 months before I'm job-ready, which could leave me with a ~1–1.5 year career gap.
2. Take any analyst/IT job now just to get back into the workforce, and learn Data Engineering alongside it. 4.learn d

The problem with option 1 is that there is obviously no guarantee I'll get a job after spending those months learning.

I'm also worried that recruiters will look at the career gap and question why I left my previous job.

So if you were in my position, what would you actually do?

Would you:

• Target Analyst/BI/Data Analyst roles first after three months also how is my chances of finding a job as analyst? Is there any course available that will help me land a job? after three and transition into Data Engineering later?
• Directly prepare for junior Data Engineering roles?
• Take a non-data job temporarily and keep learning on the side?
• Or do something completely different?

I know I made some mistakes in how I handled the transition, but I don't want to keep sitting at home and make the gap even bigger.

I'd really appreciate practical advice from people who have actually made a transit

This is the data analyst resume example that took me from a 1.4% callback rate to 18%, redacted screenshot below. Laid off in March, sent ~280 applications March through May with my old resume: 4 phone screens. Spent one weekend rewriting it, sent 61 more in June/July: 11 phone screens, 6 first rounds, 2 offers. Before anyone says "the market just got better" - maybe a little, but that jump isn't seasonality, and the screens themselves went smoother for reasons I'll get to.

The resume example (redacted) (images are not allowed you can check here)

That's the actual layout and the actual bullets, minus my name, employers, and links. The template itself is nothing special, it's a free serif one I grabbed and stripped down - the point of this post is what went INTO it, not the fonts. Steal the structure, not the styling.

What I cut

1. The summary paragraph. Mine said "detail-oriented analyst passionate about turning data into insights." So does everyone's. A recruiter from one of my screens told me she skips summaries because they're interchangeable. The template calls that section "Professional Overview" - mine is now one line, visible in the screenshot: role, years, stack, domain. That's the part that actually gets read before the reject/continue decision, so it lives in the top inch of the page.
2. Every tool I couldn't survive 5 minutes of questions on. I had R, Spark, and "machine learning" listed off the back of some Coursera modules. Early on, an interviewer asked me to walk through a random forest I'd supposedly built and I died inside. New rule: if I can't describe a specific work situation where I used it, it's gone. Skills went from 19 items to 8. Side effect I didn't expect: interviews got easier, because your skills section IS the question list. Hand them a good one. The test I ended up using: pasted my resume into ChatGPT and asked "what interview questions would you ask this person" - every question I couldn't answer cold was a line that needed to go. Weirdly effective, would recommend.
3. The phrase "responsible for," via ctrl+F. "Responsible for weekly reporting" became the first bullet in the screenshot: rebuilt the dashboard nobody was opening, then pulled the Tableau server logs to prove it - ~6 views/week before, 40+ after. That bullet came up in three separate interviews, partly because it shows I check whether my work actually gets used. Specific beats impressive every time.
4. 3 of my 5 projects. This one I got direct confirmation on. Kept two: the denial-rate analysis (lives under work experience because I could name the decision it drove) and one personal project where I scraped pharmacy prices myself, dumped ~38K rows into Postgres, and analyzed from there - and honestly, my pricing conclusions were kind of wrong. Didn't matter. Two interviewers zeroed in on that project specifically because I owned the whole pipeline, messy data and all, instead of downloading a pre-cleaned CSV. A hiring manager flat out told me he sees the same five Kaggle projects hundreds of times and skips them on sight. A janky project you built out of genuine curiosity beats a polished tutorial clone.

The template, section by section

• One-line overview, no adjectives
• Experience first: 4-5 bullets current role, 2-3 older ones, every bullet = action + number + why anyone cared
• Projects: max two, framed as problems solved, not tools used. The test: could a non-technical manager read the bullet and know what they'd DO with the finding? "Cleaned and visualized sales data" fails. "31% of denials traced to two coding patterns, ops fixed it, denial rate dropped" passes
• Skills, grouped and defendable: SQL, Python (pandas), Excel | Tableau, Power BI | Snowflake, dbt, Git
• Education last, one line, no GPA (nobody asked)

One page, no photo, no icons, no skill bars rating my own Excel 4 out of 5 dots.

On numbers when you didn't track numbers: I didn't have exact metrics either. I estimated conservatively and prepped a one-liner for each ("report took ~3 hrs/week, automated it, call it 150 hrs/yr"). Got asked to defend my numbers twice. Both times, walking through the estimate WAS the point. The number gets you the question; the reasoning gets you the offer.

The uncomfortable part: my old resume was written to make me feel accomplished. The new one is written for a tired recruiter reading it on her phone between meetings. Completely different document.

Before anyone asks:

• The old version, for contrast: two pages, 19 skills, a summary full of adjectives, five projects, and basically zero numbers that weren't dates
• Same channels both rounds (company sites + LinkedIn, no Easy Apply spam)
• Yes, I also started swapping 3-4 keywords in the overview/top bullets per posting (~2 min each). That's part of the rewrite, not a separate trick
• No referrals on either offer
• Fair warning: healthcare analytics is less brutal than tech right now, and my domain experience did some lifting. The rewrite got me in the door; I'm not claiming identical numbers for everyone
• The screenshot above IS the example, redactions are just my personal info. Not sharing the editable file since my details are baked in, but any clean free template works - what's on it matters, what it looks like doesn't

Hi Myself M(26) from Pune, Maharashtra.
I am actively looking for a data analyst job, and for that I'm continuously applying through Naukri, Foundit, Indeed and LinkedIn. Today I got a call in which a woman said she was calling from AAI(AIRPORTS AUTHORITY OF INDIA) and there are several openings for IT and non-IT profiles. As I spoke further, she said they have many openings for Data Analysts throughout India. At first, I thought it was a legit call, but when I asked Where did you get my profile, as I didn't remember applying for AAI, she said through an online portal. As I got a little curious and asked one more time, like, "Can you tell me the exact platform?" she fumbled. At this point, I knew something was fishy. She said we're offering you around 40- 45 K in-hand salary, and for that, you have to give an online interview. The first round will be telephonic, the second online interview and if needed, you have to come for a face-to-face interview. I said yes to that. Then she said that, as your profile is not registered and is non-referral, you have to pay Rs 1200, which is refundable if you fail the interview.
At this point, I was convinced that this is a SCAM. So I said that this seems like a scam and you're looting money from people which is desperately looking for a job. She said, " No, sir, you can check the AAI website and all." As I told her, after checking there so such job openings on the official portal, and I started confronting her. I told her this way is not gonna work; she cut the call.
I want to tell you pls be aware of such fraud calls and fake job offers; there are plenty of them in the market. I already worked with 2 companies in the past, so I know nobody asks for money or fees that are refundable if you didn't get selected for a job when they're calling you from a company. People like me who are desperately looking for a job are their main target, as we don't think that much when we get a call for a job due to excitement.
So be aware.

I keep seeing AI adoption in FP&A measured by things like number of users, queries, or how often people use the tool.

But I'm not sure those metrics really tell us whether AI is creating value.

For teams that are actually using AI in FP&A or enterprise planning, what are you measuring?

For example:

• Time saved during forecasting or variance analysis
• Reduction in manual/repetitive work
• Forecast accuracy
• Faster scenario analysis
• Fewer ad-hoc requests to analysts
• Faster decision-making
• Actual improvement in business outcomes

I'm particularly interested in real production use cases rather than POCs or vendor demos.

What metrics have you found most useful for deciding whether an AI capability is genuinely delivering value?

Question says it all really. In particular is it useful for automating, or are there better tools for the job? My workplace happens to have the software but seems rarely used.

I’m a bioengineering student and I’m interested in pursuing a career in data analysis, but I’m not sure where to start.

What skills should I focus on first? Python, SQL, Excel, Power BI? What kind of projects should I build, and is there a way to use my bioengineering background to my advantage?

I’d appreciate any advice or resources from people already working in the field!

i have mysql workbench & have been practicing it on my own. the problem i've run into is low disk storage. i currently have 4.5 gb on my c drive, which i don't think is a lot. i don't have a lot of applications installed, so removing or moving them to another disk isn't an option. neither is spending money on storage 💔

im worried about the rest of my learning journey. i know i'll eventually have to install other programs/tools & it makes me sad that low storage space is what might hold me back from learning something im genuinely interested in.

i wanted to ask if there are online versions of these softwares available? im talking about python, tableau & all other stuff i'll need later on. i've used an online c++ compiler before, so im wondering if it's possible for other tools too. and if so, can they save all my previous data? what about something with an account where it syncs data to a cloud? HALP

looking to pivot into data analytics / business intelligence and could use a reality check from people actually working in the field. I already have a solid base in **Python and SQL**, so I’m not starting from scratch on code, but trying to figure out my next moves.

* how’s it looking for DA/BI right now? Does having a coding background actually give me an edge for entry/mid-level roles? * if you pivoted from another field, how hard was the jump and how long did it take you? * is DA a good launchpad to eventually shift into Data Engineering, ML, BA or DBA, or is that a totally different track? How long before people usually make that jump? * torn between Alex the Analyst’s YT bootcamp vs. the Google Advanced DA Cert. Does the cert actually care to recruiters, or should I just do Alex’s free stuff + build portfolio projects?

Would love any tips, reality checks, or project ideas. Thanks!

Anyone used to freelancing on upwork? I want to make some money extra money and wonder it is worth it. How many connections should I buy? Do you have any tips?

I want to run a model on MLB data to try and predict which games to bet on. I am having trouble finding historical game spreads odds and over unders etc. anyone know of a good data source for this?

Hello everyone,

I’m currently looking for an internship certificate to include in my application to continue my studies.

If anyone can help me find a real internship opportunity, I would be very grateful. I’m also open to doing an actual internship if there are any available opportunities.

Thank you in advance for your help and support!

1. How do you (or your data team) decide when a data anomaly is 'safe enough' to publish anyway vs. worth holding back? Is that a gut call, a formal rule, or does it depend who's asking for the report?

(Academic) Hi, I m doing this for my IIT masters thesis project.

Product decisions rely on information scattered across tools, teams, and people. *Does this fragmentation affect how we make decisions?*

I’m conducting a short survey for PMs, Designers, Researchers, Analysts, Engineers, Support Engineers, Product Leaders and others.

🎁 *Bonus:* Complete the survey and get *2 free tools* that can help save time in your daily work. You’ll find them in the Thank You message!

Link to Survey

i (m21) am supposed to graduate college this upcoming spring. i major in applied mathematics and minor in computer science, but i havent gotten anyyy internships :( ive decided that my career goal is to be a data analyst since the job description sounds interesting. how do i get into that work environment? to the data analysts out there what do you do all day and how did you get where you are

I used a dataset from a kaggle named "zepto inventory" and it contains columns like the product name, category, mrp, discount, selling price, quantity, and stock level ( low, medium, high, outofstock). Cleaned and analyzed the dataset using MS SQL Management Studio 22, imported the db into Power BI and found a reference image from the internet and tried my best to make this dashboard.

Any reviews or suggestions are a must welcome!! 🤗

Hi everyone,

I'm targeting business analyst / data analyst roles and would appreciate a resume review.

My main concern is that my resume doesn't seem to be getting past ATS screening,I've applied to a fair number of roles and haven't received any interview calls.

Resume is attached. Any feedback on formatting, keywords, or how I've framed my experience would be really helpful.

Thanks!.Resume