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

Learnt :
INNER JOIN
LEFT JOIN
RIGHT JOIN

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?

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.

If someone is working as a Data analyst or scientist, can you suggest me how to prepare for placements OA.

Like what type of questions they will ask?

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!

Hi! I’m a fresh Computer Science graduate, and I’m currently trying to land a Software QA role. Job hunting has been quite challenging, so I’m considering enrolling in Eskwelabs’ Data Analytics Bootcamp to gain additional skills, certifications, and something valuable to add to my resume.

I also heard that Eskwelabs has partner companies where graduates may have opportunities to apply after completing the bootcamp?

I’d really appreciate any thoughts, advice, or honest feedback, especially from Eskwelabs graduates. Was the bootcamp worth it? and did it help you with your career or job search?

Thank youuu

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

Hi everyone, looking for some honest career advice.

I have around **3 years of total work experience**, but I don't have formal Data Analyst/Analytics experience in my current role. My strongest technical experience is in **Python**, and I've been trying to transition into Data Analytics.

I've been applying to Data Analyst/BI/Reporting roles for quite some time, but I'm getting **very few callbacks and essentially no interviews**.

I'm currently building skills in:

* SQL
* Python/Pandas
* Power BI
* Excel
* Statistics/business analytics
* Data visualization
* Basic data modeling/warehousing

The problem I'm running into is that many Data Analyst jobs ask for **2–5 years of actual analytics experience**. So I'm wondering if I'm approaching this transition incorrectly.

**I'd really appreciate advice on:**

1. **Is Data Analyst actually the right next step for my background?**
   
2. Should I apply to **fresher/0–2 YOE Data Analyst roles**, despite having 3 years of total experience?
   
3. Should I instead target **BI Analyst, Reporting Analyst, Power BI, Business Analyst, Data Operations, or Analytics Engineer** roles?
   
4. Would moving toward **Data Engineering** make more sense given my Python background?
   
5. If your priority were **getting a better-paying job relatively quickly while building a long-term career in data**, what route would you take?

I'm not looking for the perfect role immediately. I mainly want to **get into a role where I can gain genuine analytics/data experience and increase my salary**, then build from there.

Would especially appreciate advice from people in India who have made a similar transition.

Sorry if this is the wrong tag. But looking for advice from data analysts.

So currently I’m on the job hunt. I have my Bachelors of science for agribusiness systems management and in plant and soil science (dual majored) and currently I can’t seem to get any further than an interview for some jobs.

So with the job market becoming even more competitive. (Especially in the ag industry) I’ve been looking at ways to bolster my skills and make myself a more valuable prospective employee. So i stumbled across the data analysis course from boot.dev and has seen some good reviews on it.

It got me thinking will becoming a certified data analyst with projects beneath my belt help me get employed? I’m not too picky on what I want. I just love agriculture and the business and research side are both very interesting to me.

I am scared of AI making entry level data analysts obsolete so is getting the certification worth it over just changing careers and shifting into IT for more job security?

Just looking for some advice and info if you all could help out! Greatly appreciate it!

Is there anyone here who works as data engineer / analyst or any data related jobs in construction?

I am wondering if there are such opportunities in the industry. What does your day look like? Are you involved in the construction itself? And where can I find these opportunities?

I am currently a project scheduler and I use P6 for most of my work and I feel like there is much potential behind the database of P6.

I tried exploring sqlite and use sql queries. I know power query would be enough for sqlite but I want to practice sql.

I feel like construction has messy data compared to other industries. But it is somehow exciting to architect data pipeline for it.

Anything you share is much appreciated!

Hi I’m interested in working a job in data analytics. I have no background in this area and actually have a bachelors in fashion merchandising and minor in marketing. Any recommendations as to how to break into the industry. I’m open to learning just don’t know where to start. Thank you!

After working IT for 3-4 years mainly warehouse, so I figured “Why not double down on warehouse and logistics and use my skills in IT to become a WMS analyst?” I’ve been doing research into the market, I believe it’s a good path to take, almost every major warehouse needs them and it pays well. Why shouldn’t I try to move into it? It’s better than being stuck doing help desk.

The issue I’m having is, how are you supposed to get experience since no one will hire you without it? Which I understand since you’re messing with live data and that could cause disaster. Do I need to get certifications for it, if so which ones? There’s a handful and I don’t know which ones are worth getting or if it’ll help me get my foot in the door. I know that learning SQL is gonna be important so if yall have any recommendations or resources to learn it I’ll be grateful!

Thank you so much for taking the time out of your day to read this!

I recently started working as a junior/student data analyst in a large manufacturing company, mainly using Power BI for staffing analysis.

The data include only historical values such as volume, worked hours, employees, shifts, zones, overtime, roles/skills etc. But the data is from multiple sources

The main questions are:

• How much staffing is needed for a certain volume?
• How many units are handled per labor hour?
• Which shifts/zones are most productive?
• When were we historically overstaffed or understaffed?
• How does overtime change with higher volume?
• Can historical productivity be used with next month’s forecast to estimate staffing needs?

As a junior, how should I approach this assignment from the start? Should I first focus on understanding the data, granularity, validation and data modelling before building the Power BI report?

For the data model, how should I decide what should be a fact table versus a dimension table, and when should I split data into multiple fact tables instead of keeping everything in one table?

Is this a reasonable junior assignment, or are parts of it considered advanced?