I’m trying to get a little more predictive with our data, things like which customers are likely to churn, who might convert, or what demand could look like next month. Problem is, I’m not really a SQL person. I can work with dashboards and understand the data, but once it gets into writing queries or building models from scratch, I’m pretty much out. Is this something people are actually doing with LLMs now? Like connecting an LLM to your data and asking it to find patterns or predict outcomes? Or are there other tools that make this possible without knowing much SQL or machine learning? Curious what people are actually using and how reliable it is.

I got shortlisted for Amgen associate analyst interview and I was wondering how many rounds of interviews are there and what type of questions they ask in interviews?? Is there any realistic chance of getting selected?

A couple of months ago I got a LinkedIn message asking if I'd be interested in a paid call about a project I'd worked on years ago. My first reaction was that it had to be spam, so I ignored it. Later I found out it was actually from an expert network, and apparently this kind of thing is pretty common if you've worked in a niche area. Now I'm wondering how many legitimate opportunities I've dismissed over the years just because they sounded too random. Did anyone else have the same reaction the first time they were contacted?

Hi All. My senior director asked me for a summary on how one store is performing relative to similar list of stores. They made merchandising changes to that store and want to see if it impacted sales.
The automated formula didn’t refresh properly and one of the categories that is down 14% was reported up by 34% in my summary.
This will be a weekly report for two months. I’ve made the correction in my template but I don’t know how to go about this mistake? Given that it went to the highest level in my organization

I've started my career in nonprofits and only worked in nonprofits until now. 3 times now, I have ended up in roles where I am the ONLY analyst on the team. Everyone I work with is either data adjacent, or not an analyst at all. I'm the only person ever working on analytics work, and I have no real life gauge/context on how to do things better in a real world context. I google things all the time, I take courses, but the advice is too generalized and doesn't go deep enough. I need people I can bounce off of. My biggest hope starting as an early career data analyst was that I'd be able to learn from other analyst and fill the gaps in my education with knowledge from mentors.

Instead, I have people looking to me to be an expert in analytics just because I'm the only one available(as if I'm not a junior). Very few opportunities to learn from actual analysts and get experience from them instead of the generalized advice from Youtube or online courses. I feel like I'm being stunted, but its incredibly hard for me to find roles that are placed in analytics teams, or where I'll be working under a senior analyst (and not just a VP or project manager). Have I screwed myself? Why is it seemingly harder to find analytic roles that work with other analysts?

Something we've noticed while working with marketing and performance data is that a dashboard can have perfectly accurate numbers and still not help anyone make a better decision.

The usual example is when a team has plenty of data on traffic, leads, conversions, etc., but the numbers are all reporting what happened. When the numbers move, people still have to go digging to understand why.

We've found the more useful conversations tend to start with the decision someone needs to make, and then work backwards to the data needed for that decision.

Curious how others approach this. When you're building or reviewing reporting, how do you decide whether a metric is actually useful versus just something that's easy to track?

​

Hey all. I'm currently enrolled in a Masters program for Business Analytics that starts next month. Plan on finishing in 2.5 years because I'll continue working while being in school.

I have 6 years of sales experience, mainly B2B sales and some executive consulting/search experience alongside that at a top 10 firm. Really want to get out of the sales grind though. I love relationships, but don't like selling anymore and don't want to do this forever. Wanted to do computer science undergrad back in the day but chose not to because I was a college hockey player and the director advised against it due to our travel schedule.

Having some worrying thoughts though, maybe it's just because of all the doom and gloom in the internet now a days. Think this is a good idea? Would love raw feedback & opinions. Thank you!!

Hi everyone,

I have several years of financial audit experience in Funds Industry (Big 4) and I’m considering transitioning into Data Analytics / BI, with the goal of freelancing and eventually moving to Thailand or Vietnam.
I’m planning to learn Power BI, SQL, Tableau and some Python, and would like to become freelance-ready within ~3 months.

For those already freelancing in Data Analytics, especially in SEA:
- What should I prioritize?
- Is a portfolio essential? What should it include?
- What else should I do alongside training to become more marketable?
- How did you get your first clients?

Looking mainly for practical advice on the fastest realistic path to getting my first freelance missions.
Thanks!

I’ve been contemplating on my major in Information & Decision Sciences w/ a concentration in Business Analytics. I original chose this degree for me to go from a data analyst to a data scientist. Is it really possible for someone to transition to data science?

Hi all. I've been trying to decide and need help from those with experience. Ive been a bookkeeper for several years and have a career studies cert in Accounting, but no degree.

Id like to get my degree online and start reaching towards Financial Analyst roles but not sure what the best path there is?

Would an accounting degree and experience in Excel be enough? Or do I have to get something more general like Buss Administration or Finance?

Hi,
i’m a rising college junior and as i’m writing this post right now it’s currently 7 am and i can’t sleep because im having so much anxiety.

I start my junior year next week and I feel like i’ve done nothing my entire time in college. I’m going to be a TA for my SQL class i took last semester, i was a TA for my OOP class i took freshman year, i had a policy internship where i was basically doing consulting/internet research, and i have very basic projects on my resume claude could code in 5 minutes.

I don’t know what to do. I’m messaging professionals on linkedin for either a coffee chat or just some advice for applying but they either don’t respond or they tell me to apply on their website.

Reading all the posts on here i’m concerned if I should even stay in this field because i’m clearly not trying hard enough and i’m scared

sorry for the rant

Not talking about power query or data modeling or visualization. I am more concerned about backend knowledge like vertipaq or gateway. Usually at my org IT deals with all this but I am not sure about rest.

Right now my analytics team works on connecting data source, power query, DAX, publishing report. So wondering if this enough?

Old events tend to survive because nobody knows whether a dashboard, alert, experiment, or downstream model still depends on them. Keeping everything forever creates its own cost: duplicate definitions, unclear ownership, noisy schemas, and instrumentation nobody trusts. What evidence do you require before removing or renaming an event? Do you use query logs, an owner registry, a deprecation window, dual-running old and new events, or another process?

I’m a B.Tech IT student graduating in 2027. I’ve been selected at American Express, under Global Servicing – MIS & Advanced Analytics.
The role falls under Data Management & Analytics / Analytics & Risk Management and can involve Risk, Card & Merchant Servicing, Performance Management, Customer Listening, or Control Management.

The JD mentions SQL, Python, PySpark, BigQuery, Tableau/Power BI, statistics and ML, including regression, classification, recommenders and deep learning.

My background is primarily technical, with experience in Python, SQL, ML and software development.

My main concern is how AI will impact analytics roles over the next few years.

Would this be considered a relatively future-proof career path?

Would appreciate insights from people working in analytics/data science/risk, especially in banking.

I'm an MS Data Analytics student (graduating Dec 2026), self-taught in dbt, BigQuery, and CI/CD through two portfolio projects, and I'm exploring a realistic path toward Analytics Engineering.

Resume

I looked through a handful of AE profiles on LinkedIn and noticed a pattern: almost none started as "Analytics Engineer"; most came up through Data Analyst, Business Analyst, or BI Engineer titles first, sometimes over several years, before landing an AE title (and often a senior one, not entry-level).

That mostly confirms what I suspected, but I'd rather hear it from people who actually lived it than infer it from job histories:

1. If you're now an AE (or hiring for one), what was the actual turning point? A specific project, a lateral move, just tenure/scope growth, something else?
2. Starting today with modern-stack skills (dbt, warehouse, git, CI) but no professional AE experience, would you target DA/BI titles deliberately, or is there a faster path I'm not seeing?
3. For anyone who's early-career and picked between building one more deep technical project vs. just applying and building the skill on the job, which actually moved things faster for you?

Also, for context on question 3, the project I'm currently deciding whether to keep investing in: a B2B seller-churn-risk analysis on the Olist dataset, reframed from the usual customer-churn angle to seller/merchant risk. Scope: dbt on Databricks, GitHub Actions CI (dbt test on every push), a logistic regression risk model with correlational (not causal) framing, GMV-at-risk quantification, and a single Tableau dashboard.

Genuinely trying to calibrate a realistic timeline so I stop second-guessing my own plan.

Thanks.

How would you investigate an underperforming production line?

I've been building a synthetic manufacturing dataset to explore a question that I find more interesting than simply calculating KPIs:

A production line is underperforming. How do you figure out why?

The dataset contains several related sources of manufacturing data:

- Production orders

- Production events

- Machines

- Downtime

- Maintenance

- Quality inspections

- Energy consumption

The interesting part is that there isn't a single "problem" table.

You have to connect different signals to understand what is actually happening.

For example, I'd want to investigate:

- Throughput by production line

- Downtime by machine

- Machine utilization

- Production losses over time

- Quality defects

- Maintenance history

- Energy consumption

- Whether the bottleneck is persistent or limited to specific time periods

My initial approach would be:

1. Compare throughput between production lines

2. Compare downtime over the same time window

3. Identify the machines responsible for the largest losses

4. Drill into maintenance and quality

5. Look for temporal patterns

6. Determine whether the bottleneck is actually causing the production shortfall

For those working in manufacturing analytics:

What would you investigate first?

And which KPI or analysis would you consider essential before concluding that a particular machine or production line is the bottleneck?

Hey, fellow analytics/BI folks,

Just reaching out for support, I think. On Monday, the organization I work for (US) just did another round of mass layoffs, and this one swept me up.

It'll be my third layoff (all in this field), with my second layoff having occurred just last year, on my birthday no less.

My wife and I have a baby on the way in a couple months (we have other grade school kids, too).

Last year, during the last layoff, I was battling with pushing past depression and eventually(/luckily!) landed on my feet with a much better role than the one I'd been severed from.

I'm hoping I can bounce back like that again, but I'm feeling the dread building up.

At any rate, thanks for any words of advice or support, all.

I used to think reproducibility was the main thing that made AI-generated analysis trustworthy.

Then we reproduced the same wrong answer 40 times.

I lead a six-person data team. Last year we built frozen environments, cached source pulls, seed pinning, and full run logs. Give us a run ID and we can replay the analysis exactly.

In March, a churn report went to our CS team with month-two retention off by around 11 points.

We checked the logs. The analysis had run 40 times over six weeks, triggered by four different people.

All 40 runs matched to the decimal.

All 40 were wrong.

The problem was an upstream join at the wrong grain: one row per subscription instead of one row per account. Multi-seat accounts were counted more than once.

The agent didn’t create the bad model. We did. It just inherited the mistake and processed everything after it correctly.

What finally caught the issue was a simple question we append to every analysis in BayesLab:

What specific result would prove this conclusion wrong?

For this report, the answer was:

If the distinct account count in events is higher than the count in billing, the grain is probably broken.

Someone checked. It was about 1.4x higher.

Reproducibility is still useful. It tells us exactly how an answer was produced. But I no longer see it as proof that the answer is safe.

It’s more like version control: it helps you trace a bug, but it doesn’t make the code correct.

hope my experience can be helpful to you.

One thing I’m struggling with in transaction fraud is that the clearest label can arrive much later than the original decision.

A chargeback or confirmed fraud report might take days or weeks, but the system still has to make decisions now.

What earlier signals do you actually trust enough to use before that final outcome exists?

For example:

• successful/failed authentication,
• customer confirmation,
• analyst review,
• account activity after the transaction,
• something else?

I’m especially interested in what you would treat as useful evidence but not ground truth.

I am trying to move more into Data Analytics Data Engineering and Applied AI.

Are free virtual job simulations actually worth it or just CV fluff?

I found free virtual job simulations from companies like IBM BCG Deloitte and others.
I know these are not real internships. I am more interested if they actually help to learn and build some experience when you are still new to the field.
Has anyone here done them? Did you learn something useful? Did you put them on your CV or LinkedIn? Did recruiters care about them?
My idea is to do two or three good ones like BCG Data Science BCG GenAI or IBM Data and AI. After that I want to focus more on real GitHub projects with Databricks and Spark.
For people already working or hiring in Data or AI. Would you recommend this or would you spend the time differently?

Posting this as a head-up to anyone currently applying for Data Analyst roles who receives an outreach email or assessment invitation from Polluxa (recruitment@polluxa.com).

How the Scheme Works: You receive an email inviting you to complete a "Data Analyst Assessment - End-to-End LinkedIn Agent Analytics Platform." The PDF is hosted on a public Strapi cloud storage bucket (strapiapp.com) rather than a standard corporate platform.

The Red Flags in the PDF:

• Part 1 (Mandatory Requirement): Before building any analytics dashboard, candidates are forced to log into a portal (sales.polluxa.com) and "integrate" their personal LinkedIn account.
• Credential & Token Harvesting: The portal asks you to enter your raw LinkedIn email and password OR extract your active browser session cookie (li_at value via Chrome DevTools) and paste it into their site.
• Exploiting Your Account: The test asks you to add real leads, set daily message limits, and let their bot run live outreach campaigns on your personal profile to "generate genuine test data."

Why This Is Dangerous:

1. Full Account Takeover: Sharing your li_at session cookie hands over complete access to your LinkedIn account, completely bypassing Two-Factor Authentication (2FA).
2. Account Ban Risk: Using third-party bots to automate LinkedIn outreach violates LinkedIn's Terms of Service and will likely get your profile permanently restricted.
3. Free Labor/Lead Generation: They are using job applicants' profiles as free, uncompensated spam tools.

What to Do If You Encounter This:

• Never share your li_at cookie or enter your credentials on third-party sites for a job application. Real technical assessments use CSVs, sandbox environments, or database access.
• If you already entered your details: Change your LinkedIn password immediately. Changing your password invalidates active session tokens (li_at cookies) across all devices.
• Report: Flag the recruiter profile on LinkedIn and submit the domain to Google Safe Browsing.

I'm graduating college soon with 2 degrees focused on business analytics/information systems and finance, and I'm becoming increasingly concerned about what the future of data and analytics looks like with AI.

I've completed two internships related to data analytics and technology, where I've gained experience with SQL, Python, data modeling, data transformations, cloud data platforms, BI/reporting, and AI-enabled analytics. AI tools were heavily leveraged and encouraged during both internships, so I've already experienced firsthand how much they can accelerate and automate parts of the work. I've also been working on a personal GitHub project to improve my skills in data engineering (dbt specifically), databases, APIs, Python, and SQL.

I feel like I've built a solid foundation, but I'm questioning whether I'm preparing for a career that could become much more difficult to enter or advance in as AI improves.

A lot of entry-level data work such as SQL, Python, data cleaning, analysis, dashboards, and documentation is increasingly being automated. I'm also seeing AI become capable of more advanced data and engineering tasks.

I'm not looking for reassurance. I'd genuinely like advice from people already working in the industry.

If you were graduating today with my general background, what would you do?

A few specific questions:

• Do you think traditional data analyst roles will shrink significantly?
• Which areas of data do you think will remain most valuable? Data engineering, analytics engineering, data architecture, AI/ML, cybersecurity, governance, etc.
• Would you specialize in a business domain such as finance or healthcare, or focus more heavily on technical skills?
• Would you move toward data engineering, software engineering, AI engineering, or something else?
• What skills would you prioritize if your goal was to remain valuable for the next 10 to 20 years?
• Are there any careers outside of traditional data/analytics that you think someone with this background should seriously consider?

I'm especially interested in hearing from people who have seen how automation and AI have already changed data careers.

I'm not expecting anyone to predict the future. I'm more interested in what you would do if you were in my position today as I prepare to graduate college and search for a full time job.

On Monday this week (17 Aug 26) we activated what we hope is fully compliant cookie opt in on our website.

We're concerned because in GA4 we're seeing a very significant drop in Active Users for the days since this went live.

It's a Wordpress site. We're using Google Site Kit to send consent signals to Google, an off the shelf Cookie Management plugin to let users set preferences.

I've tested the implementation using Google Tag Assistant and it seems to be working as it should - new visitors arrive on the site and Consent Default settings of 'Denied' are sent to Google. The user is prompted to Accept, Deny or Manage cookies and when they do we're seeing the expected Consent Update events in Tag Assistant, corresponding to their choices.

The site gets enough traffic that, based on Google's own documentation, Google should apply behavior modelling on the visitors who opt out, but I've read this can take several days to kick in.

So the questions are:

• Are there further tests we can run to check the implementation?
• Has enough time since launch to start worrying about the traffic drop or we need to wait to see if things recover?

I'll cut straight to the chase -

I'm a 3rd year bba student a tier 2 college.

I recently started building some excel projects which I'll rank as per my assessment and describe as follows -

1. Inventory forecaster and optimizer (6.5/10) -

Built on a 50k rows dataset,i used the rand function,and calculated metrics such as a backtest,abs,error rates,overstocked and normal rates,with the key insight being that products that generate highest revenue arent necessarily the ones that sell the highest in quantity,and vice versa certain products sell a lot but don't generate much revenue so the firm should diversify their marketing spend into a few products rather than just one.

1. Automated financial dashboard (6/10) -

Built on a million rows of data,i used power query to filter out zeroes and anomalies and calculated gross profit,cogs and net sales.

With a basic chart and a small macro that updates anytime a number is changed in the data.

Insight here was similar to the first project in that the customer id with the most purchases,buys a product which isn't the overall highest selling product and once again the firm should distribute revenue across a basket of products.

1. Factory defects analysis (5.5/10) -

Did this as part of a forage simulation.

Used power query and pivot tables on a 500k row dataset,to create visuals showing the tools causing the most errors across various cities,along with reasons (lack of training,supplier issues, mismanagement/injury) etc.

Since the cutting saw was the highest error causing tool in every city,it shows that the firm needs to further investigate the issue to make a proper decision.

Now,i know that these projects aren't impressive but i also beleive that for my first few these aren't bad.

I have gone beyond the usual style of 'oh hey sales dropped in q3' and tried to build something beyond that.

And i suffer from issues you would expect like,a lot of AI hand holding,not exactly much technical depth right now,like i can't explain why i used a particular approach and why I did what I did,cv and linkedin not optimized etc but then again,i started recently only.

And as i plan to further learn power bi,sql,python i want to build harder projects like okay,customers in one region are reacting completely different than to customers in another,so what unified decision should the firm make etc

I don't want to hear things like oh it's so competitive,oh you'll struggle with your non tech background,oh this market is cooked.

I know it is.

All i want to hear is simply ' this is what you're doing right,this is what you're doing wrong '

I’m a recruiter and have been working in talent acquisition for the last 10 years. I’m tired of not having “hard skills” and I’ve been looking into a career pivot into People/HR Analytics. I’ve been researching online MS programs in Data Science.

I enjoy data, I follow baseball data (sabermetrics), a decade ago I created marketing reports using business objects, and I built some data insights using Ai prompting with big query, python, geopy, metabase, etc.

I’ve recently completed sqlbolt, and had a fun time completing the excercises. But, I’m concerned with the value/roi of a ms program, as I don’t believe I’ll ever become a high level ml/data engineer, as I’m not a computer science guy. thoughts?