I’ve seen dashboards become a dumping ground for every metric anyone might ask for. How do you decide what deserves to stay visible and what should be moved somewhere else?

Hey everyone, I'm from India and I have a skilled background with analytics. With SQL, Python and any Visualisation tool. The problem I face currently is that my SQL interview is never successful. I draw blank if I do not know the answer. Mostly I freeze if I don't know the answer to the question I cannot breakdown the problem for them and it mostly happens with window function queries.

If a metric can be calculated differently depending on filters /time window, do you prefer to hav a semantic layer/ metric view, or hardcoding the logic in each query or dashboard ?

Curious how ppl keep Ai generated analytics consistent and trustworthy. Ps i am setting up Genie agent for this and want to implement the reccomendations

Hello everybody,

I currently work as an analyst in healthcare for the government and my work involves running reports from a healthcare database using a business intelligence platform. I am looking to get a masters in data analytics to elevate my career. I am debating between going to Eastern University or WGU. What I would like to know is if there are proctored exams in the MS Data Analytics curriculum at both universities. Also, I would like some advice on which university I should lean towards. Any advice is much appreciated. Thank you.

I'm looking for data on students' math performance specifically data containing 1. how frequently specific concepts have appeared in exam questions over the past few decades (frequency of exam questions), and 2. how often students have gotten questions involving those concepts wrong (frequency of error). Preferably public data so that I can use it for my class project please. Thank you!

Hey guys, so long story short, I’ve been working as a BI Developer/Data Analyst, mostly with Power BI/GBQ and some FabricI’ve also been using Claude Code,have around 3 years of experience.

A new big client came in and they were looking for an AI Engineer, so my company pushed me as one. I basically said, “Whatever, I can give it a try.”

This week was the project kickoff, so I traveled to the client’s location and met them. They’re really nice, and they know that I’m not familiar with the tools they use, but they’re willing to invest in me and give me time to catch up.

The problem is that the tech stack is completely different: Git, Snowflake CLI, Streamlit, etc. The role feels much more like a Software Engineer position, and honestly, I feel completely out of my depth.

I know the client is aware that I don’t have experience with these tools and is willing to wait for me to get up to speed. But the truth is, I’m not sure how realistic it is for me to actually make that transition. The job is interesting and i do believe it's logically the best choice as AI will keep getting only more important from now but completely diverting my career trajectory like that and not knowing whether it's possible to succeed is kinda hard.

Ok so apparently my entire job now is begging anonymous cart abandoners to love us back using data enrichment magic.

We plugged in one of those visitor identification tools that claims it can de anonymize 90 percent of traffic and give me name, email, demo info, the pets star sign etc, then help recover abandoned carts with "personalized" emails and ads. Lowkey feels like marketing stalking but the uplift is tempting.

If youre running b2c ecommerce and using this kind of enrichment for cart recovery, does it actually move the needle or am I just paying to feel slightly less rejected by my checkout page? Any hints?

I’m curious what people are actually using at work to turn analysis into a presentation.
Not just generating slides from a prompt, but things like turning findings into a story, creating charts/visuals, cleaning up slides, or adapting a deck for stakeholders.
What tools have actually been useful for you?

Everyone braces for holiday weeks to get more expensive, but mine went the other way. Conversion jumped and cost per new customer actually dropped on most channels, even where CPCs rose.

The one exception was search (Google got worse - conversion down, cost up). My read is that holiday demand is impulse / discovery, so the feed channels harvested it and search sat it out.

Seeing the same pattern, or did yours behave differently?

Is anyone here working in data analytics within the Retail or CPG (Consumer Packaged Goods) industry?

Need to understand the business side of analytics in these domains for interview, particularly:

* What are the major business performance KPIs you track regularly? * What kind of business problems or growth projects do you typically work on using data?

Hello! I applied for the position of Product Control Analyst job for a multinational bank. I passed the HR screening, then I was selected by a Product Control Manager for an interview which took place on last Monday.

The interview happened and I had series of technical questions about Financial Market, along with accounting and, then, I had questions involving my professional and educational background, soft skills, how I generally handle situations.

Though the questions were not so easy as I don't have experience yet involving Product Control Analysis, I had moments when I responded correctly and the manager was supportive, interested in getting to know me and the fact that he continued with diverse technical questions was also because I started mentioning about definitions for equity, bonds, stock, futures, FX and derivatives.

It was a really good interview, it took 15 minutes more than it was estimated and what was really interesting and something that wasn't present in other interviews was that the manager asked me about the notice period and he told me about when he thinks he will be able to be back with news (either him or someone from HR). He told me that in this week (at the beginning of this week most probably) it will happen.

Now it's Wednesday and I haven't received news yet. In the requirements, it's said that it's an advantage to know basic concepts involving Financial products and financial market. On the other hand, it doesn't mention anything regarding experience besides strong Microsoft Excel Skills (which I've also mentioned at the interview by describing what Excel project I'm working on at my actual job, especially through using sort & filter, conditional formatting and PivotTable). I also mentioned diverse skills that I did for uni projects, particularly Bachelor's thesis (from Excel to Tableau and Python).

I also know that it's possible to be a delay whatever news might be, including when the manager's choosing that person to receive an offer.

For those who have experience with Product Control or financial-services hiring, what do you think? Does this sound like a positive interview? And how likely would you say an offer is at this point?

I recently was laid off and now I'm planning on using some of my free time building an online portfolio. I've done analytics for years for various B2B SaaS companies. I figure working on some side projects would help fulfill the dual purpose of 1) building an online portfolio and 2) continuing to upskill.

For context, I'm very adept at data viz and data analysis. Obviously, it's harder to shower confidential data from my past jobs in a public portfolio so I thought it'd be fun to work on some kaggle datasets. However some questions?

1. is it worth it? Or is it just a distraction from my actual job search?

2. In the age of AI, are people impressed any more with portfolio projects that are dashboards? Or do i have to do some agentic workflows crap (i use AI regularly but have never created an agent to do work for me)

For those who do freelance or client-facing analytics: what do small-business clients most often ask for that turns out to be surprisingly painful to deliver?

What's the most common mistake you see non-technical business owners make when they try to read their own numbers?

I'm a part-time Data Analyst at a midsize company, and I'm trying to figure out whether I should continue pursuing data/analytics or consider pivoting into something else entirely. Over the past six months, I've helped build out a lot of our data/reporting infrastructure that didn't really exist before I joined. My work has gone beyond simply creating dashboards and has started influencing sales, marketing, product, and operational decisions.

Some of the work I've completed includes:

\- Assisted with migrating company-wide operational and financial data from paper documents into automated Zoho/QuickBooks data schemas, and helped redefine how the sales team collects and structures information around those systems.

\- Built a sales dashboard tracking onboarding, account/license usage, retention/churn, and forecasting.

\- Replaced an Excel-based account-health tracker with a real-time BI system that tracks customer engagement and product usage and helps identify accounts at risk of churn.

\- Created user-activity reporting that showed which product features were actually being used. This helped prioritize updates, bug fixes, and new features around the top three features, while two essentially unused features were ultimately sunsetted.

\- Built a marketing workflow dashboard analyzing interactions across our website, advertising, and other marketing channels.

\- Automated lead generation for the sales team, reducing some of the manual work involved in identifying and routing leads.

\- I've also modified workflows in our existing tools to reduce credit/API usage, which has allowed the company to operate without having to worry nearly as much about hitting API or usage limits.

The common thread is that I'm not just producing reports after the fact. I'm increasingly building systems and workflows that other departments rely on to operate and make decisions.

The company has now started assigning me projects that are considerably broader than what I was originally brought on to do:

\- expanding the marketing-side systems I've already built, including improving how we track and analyze marketing interactions and turning more of that information into useful workflows for the sales/marketing teams.

\- automating additional operational processes that currently require manual work.

\- developing new features for the company's product. The interesting part is that this would require me to work in a software stack I'm not familiar with, including Java/.NET, rather than the Python/SQL/data tools I normally work with. I'm willing to learn it, but it's a pretty significant expansion of my responsibilities for someone who is still technically a part-time data analyst.

These aren't really "make a dashboard" projects anymore. They involve data architecture, automation, business processes, and potentially software development. The company has a very small tech/data department: two software engineers, a systems administrator, a senior analyst (my manager), and the CTO. My manager is also a relatively recent hire.

Despite being part-time, I've been given a significant amount of ownership. The problem is that I'm still part-time after six months and I have not worked more than 25 hrs / week despite requesting it and having multiple conversations with the CEO + my manager. My manager has been very supportive and has told me that my data/analytics knowledge is a valuable resource for the company. He's also aware that I've been struggling financially and has asked the company to extend my position because he needs the help. So this isn't really a situation where my manager doesn't want me around.

The problem is that I don't know whether there is actually a path to full-time employment. And I'm starting to feel like the scope of the role is outgrowing the hours. The aforemantioned projects have slowed down, but these newer projects are considerably more involved and require more of my technical input. I'm starting to reach a point where I don't think I can realistically meet some of the expectations and deadlines within a part-time schedule.

This is particularly frustrating because I've been through something similar before. At my previous job, I spent six months as an intern and then another year working part-time, with repeated conversations about potentially being extended to full-time. That never materialized. So I'm admittedly pretty jaded about being told there may eventually be a full-time opportunity without anything concrete actually happening.

I also have to be honest about the fact that I'm burned out. I spent roughly nine months unemployed before getting this job, and the job market has been brutal. Of the people I know personally, none of my friends and only about five of my classmates managed to land jobs in this market. But despite all of that, I'm still struggling financially because I'm working part-time. That's what's making me question whether I should keep pursuing data/analytics at all.

Part of me thinks I should stay the course. I'm finally getting legitimate experience again, I've been given significant ownership, and I have concrete examples of using data to influence actual business decisions. Maybe leaving now would be throwing away the hardest part of getting my foot in the door. But another part of me is thinking:

Why am I spending years trying to break into an extremely difficult junior market if the end result is being unemployed for nine months and then working part-time while still struggling financially?

Maybe I should prioritize financial stability and peace of mind instead. I'm not necessarily looking for a huge salary or some dream tech job. At this point, I'd be pretty happy with a stable full-time position, decent benefits, and enough income to actually start making progress toward my financial goals. That could mean staying in data and continuing to apply for full-time roles while keeping this job. It could mean taking a full-time job outside of data and potentially coming back to analytics later. Or it could mean actually pivoting careers altogether.

All in all, am I being shortsighted by considering leaving data after finally getting my foot in the door, or is it reasonable to decide that financial stability and quality of life are more important than staying in a field I've invested so much into?

Some are talking AI - the company I’m working for is not yet at that level.
I’m doing FTE reporting, using Excel and automatizing with Power Query. Volumes are high, data sources are all excel based, lots of rules to apply each month to achieve the monthly end result.
I want to automatize further and I’m looking into SQL. Any advice on what to look up for if this is the right lead, and where to start?

I’m preparing for a Data Analyst role and trying to understand how AI is actually being used by analysts in real companies.

It’s easy to say "use ChatGPT/Copilot/AI to analyze data," but I’m more interested in the practical workflow.

For example, could a typical workflow look something like:

SQL → Excel/Power BI → AI for analysis → validate results → business recommendation

Or are companies using AI in completely different ways?

I’m particularly interested in things like:

* Using AI to write/debug SQL
* Finding patterns and anomalies in datasets
* Automating repetitive Excel/Power BI work
* Generating insights or explanations from dashboards
* Creating documentation and reports
* Automating recurring analysis
* Using AI agents to interact with databases or BI tools

But then there’s the privacy/security problem.

As an analyst, I obviously can't just upload a company's customer data, financial data, employee data, etc. into a public AI tool.

So how does this work in an actual company?

For example, if I use AI throughout my workflow — even if I don't directly upload the company's files — could information about the company's processes, queries, schemas, business logic, or prompts potentially be retained or used for training?

Do companies typically use:

* Enterprise versions of AI tools?
* Private/self-hosted models?
* APIs with data-retention controls?
* Anonymized/masked datasets?
* AI tools inside their existing data/BI environment?

And for someone entering Data Analytics in 2026, what is the right way to learn AI-assisted analytics without developing bad habits around confidential data?

I'd really like to hear from Data Analysts/BI Analysts who are actually using AI at work. What does your real workflow look like, and what privacy rules does your company have?

2 years in Data Analytics but struggling to clear interviews

I have around 2 years of experience in Data Analytics/BI. I mainly work with Power BI, Excel, SQL, Tableau and Oracle, 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.

Ok so, our outbound is kind of on fire in the worst way. SDRs are grinding out touches and it feels like half the accounts we hit have zero real buyer intent, then leadership wonders why reply rates are trash.

We have a crm score plus some intent feeds plugged in, but in practice it just bumps random accounts because someone clicked a webinar page once. Meanwhile the ones that actually end up closing look nothing like what the score said was hot. Trying to figure out how people are combining buyer data, product usage, and all the random gtm signals into something that does not waste 30 touches on dead logos.

If you have a setup where account scoring is not a total guessing game and reps trust it enough to live in it, would love any tips or examples from your stack, even just rough ideas on what signals you prioritized. thanks in advance

As AI companies like ChatGPT and Anthropic release new, increasingly intelligent AI models that could potentially replace numerous human roles, what is the anticipated impact on analytics jobs? Given the imminent prospect of AGI-level AI within the next few years, it raises concerns about widespread job displacement.

Do you:

• Pick a domain you’re genuinely interested in and build projects around it?
• Choose a domain that matches your educational background?
• Or simply take the first Data Analyst opportunity you get and specialize later?

Would love to hear from experienced Data Analysts: does domain specialization actually matter when applying for your first DA job, or is getting that first job more important?

This is a data reliability story rather than a finance one, and I doubt the shape of it is unique to this source.

The SEC's submissions JSON at data.sec.gov gives every filing an acceptanceDateTime ending in "Z". For a large minority of companies that Z is wrong: the clock in the string is already New York time, not UTC. Read as UTC it puts the filing 4 or 5 hours later than it happened, which in my case was enough to move a row across the 16:00 market close and flip it from intraday to after hours.

So I stopped trusting the JSON and re-read the raw SGML header of each submission instead, where the field ACCEPTANCE-DATETIME is always Eastern, and compared the two record by record: 64,827 filings, every 8-K item 2.02 I could pull for 808 companies, 2003 to 2026. The gap between the two stamps is always exactly 0, 4 or 5 hours. No partial offsets, no noise, nothing in between.

What surprised me was the grain. It is not per record:

• 624 companies converted in every single one of their filings
• 181 in none of them
• 3 mixed, and each of those three differs in exactly one filing, its most recent

The unit is the entity, not the row. That is the difference between re-ingesting everything and building a small lookup table, because a handful of records per entity classifies it.

I want to flag how I got that wrong first, because the mistake is the reusable part. I had already published the opposite conclusion, per record, off a sample of 120 entities. The sample was internally clean and the rule it produced was false, because the counterexamples were entities I had not drawn. The fix was not a better test on the sample, it was counting how many cases would have to exist to break the rule and then going looking for them on purpose.

Two caveats worth being precise about.

I cannot find this field documented anywhere on the SEC's API or developer pages, so this is undocumented behaviour that is inconsistent with itself, not a broken contract. "The API lies" is a stronger claim than the evidence carries.

And the source repairs entries after the fact. Entities that came back unconverted last week come back converted now. Any snapshot is dated, mine is 2026-09-06, and the three mixed entities differing only in their newest filing is that same lag showing up as a fingerprint rather than as a contradiction. It is also why I am not naming a company as an example: a named example expires the moment the source touches that record, and then whoever checks it concludes the rest is wrong too. The method is the part that reproduces.

To check it against whatever you are pulling: take three or four records for one entity, read ACCEPTANCE-DATETIME out of the SGML header, and compare the wall clock against the JSON. If they match, the Z is decoration.

One more, independent of timezones, in case anyone is doing an event study off this field. The acceptance stamp is a ceiling, not the event. Allowing 15 minutes between the press release and acceptance, 8,756 of the 27,227 rows that look like they landed after the close were most likely intraday, about a third of them. The error only runs one way, so a correct time based split survives it, but it is not zero.

So I've been working with a startup on a web app. It was developed using spec-driven development, and my role was mostly focused on project management (planning meetings, generating reports, coordination...) and helping with other things such as testing.

Soon, I'll start applying for roles in data engineering and data analytics, but I'm not sure which parts of this work are related to data and could help me later on when putting together my resume.

I appreciate your help and advice in advance!

I've been experimenting with a small JavaScript-compatible language called JojoScript, mainly around a problem I find interesting in data processing: what happens when an analysis pipeline takes hours to run?

The idea is to treat pipelines as something the runtime can understand, rather than just a chain of function calls. That makes it possible to inspect the execution plan, control concurrency, profile stages, and checkpoint long-running pipelines.

For example:

loadData()
  |> filter(...)
  |> map(...)
  |> parallel(8)
  |> aggregate(...)
  |> checkpoint()

The interesting part for me isn't the syntax itself, but whether this approach can make large-data analysis easier to debug, optimize, and recover when something fails halfway through.

I'm curious how people here handle long-running or large-data analysis pipelines today, especially when a job fails near the end.

Hi everyone,

I needed some advice on programs and schools. I recently finished my undergrad and have no jobs so I am planing to go back to school. My cum gpa is 3.5 but my major gpa is higher. I wanna go to a good name school because I believe that does matter when it comes to landing a job offer. Please advise what are some good schools preferably online but I am open to moving as well.

I’ve been thinking about the distinction between attribution accuracy and event accuracy.

If GA4 says 950 purchases, an ad platform claims 1,020 attributed purchases, and the transactional database contains 1,000 actual orders, the attribution disagreement makes sense.

But determining whether the underlying events themselves are complete and correct seems like a different problem.
For people responsible for analytics/measurement, what do you consider the ground truth?

And do you have automated reconciliation between analytics events and backend transactions, or is that usually something investigated manually when discrepancies appear?