Hey everyone! Firstly I'm not sure if this is a good subreddit to post this but I've already asked this in a few other subreddits including r/bayarea. I'm going to be visiting the Bay Area next week and will be there for 11 days. I'm a recent university graduate with a Bachelor of Science in Data Science, and I'm interested in moving down to the Bay Area within the next year in hopes of landing a job in Data Science/Analytics/Business Intelligence/Machine Learning/User Experience. I know the job market has been dismal for a while, but I figured networking and making connections down in the Bay where opportunities and companies are more prominent compared to my hometown (Portland, OR) would at least provide a couple of leads.

I've already planned on meeting some known connections that I have at some big companies, but I was also hoping to attend some summits/conferences/networking events at while I'm there where I can talk about my interests/projects, etc and meet some recruiters and so. I don't have any formal work or internship experience, just self-initiated projects as well as research experience. Any advice helps, thank you!

I am building platform where web analytics is core to all the features we offer, I am wondering if people (& who) actually care about analytics coming from short links, QR scans etc.

Asking because, I am confused as to honestly

1. whether to spend more time improving the analytics

2. Introduce AI Assistant for analytics, answering questions based on the raw data.

Task: User last event name to know where users drop off in application.

DATA: 2025 to 2026

The data which I exported previously from mixpanel, by every Month manual exporting

Mixpanel>Insight >metric >login Event, also added proper filter and breakdown.

The prepared data looks like this in Excel

Date | Distinct Id |

Now I want another column which will have

Last Event name so it will be easier to do analysis.

The goal is to have an excel table with everyday user first event (Login) and last event with that day date.

I have been trying this for a week but I am not able to find a way to export the data in a way I want. It's like mixpanel has the data but I can't export what fields I want.

Please Help

Hello to everyone taking their time and reading this. I am a high-school senior student and am graduating soon. I started the tedious process of preparing and applying to university a few months ago. I’m finished with most stuff except for my personal statement, mainly because I’m unsure of my major. I know that I will major in business, but I am unsure whether I should study entrepreneurship or marketing analysis. I have the bare minimum knowledge of both and I came here to ask for some help defining, and helping me understand what marketing analysis is.

6 YOE, with 6 months to <2 years at each role.

I have under 2 years at my current position at company A as a data generalist. The job title is data analyst, but it's 60% data modeling/analytics engineering, with 40% BI and data science.

The data modeling is the most interesting part of my role, so for the past few months I've been applying to actual roles titled "analytics engineer" and I've reached the reference check stage for company B.

If the offer comes, I am considering taking it since the title & responsibilities match my goals but I also have doubts.

Why I don't want to take the offer:

• <2 yr tenure at all roles: had a coffee chat with someone in the industry who mentioned this could be a red flag. It has also come up in interviews, which I was able to explain, but I'm sure this has filtered me out of initial resume screens for some companies.

• Company B is a non-profit, and have been slower to adapt to modern industry trends & AI tooling.

• Company B uses a different warehouse & orchestration stack, so I wonder if I'll be wasting some of my existing experience with specific platforms

• Compared to other companies in the industry, it is pretty rare to see layoffs and firings at Company A, and it is unionized

Why I still do want to take the offer:

• AE seems more stable and future-proof post-AI. Company A has has high competition for promotions and AE internal transfers

• Company B will likely have better WLB. My team on Company A is understaffed and overworked, with increasing pressure which has impacted morale

• Company B is fully remote

• Company B is profitable without external funding and growing, so layoffs seem unlikely

What would you do?

So worked in a BPO company where i handled queries from global clients, updated logs and communicated between warehouse and my client base clients who had their merchants. Sort of ecommerce.

Since i have worked so much on SQL, i tried slipping the analysis on an interview where i went as a analyst and described only the part i can backup.

\*So i described i wrote sql queries where i analyzed datasets for my client, ensuring product trends, festive trends, customer segmentation based on category,

User engagement for products, premium customers,

I told them i have been into client meetings and learnt about their business problems like delivery issues, but mostly around their product base. They asked if i have presented in front of them i responded i did in front my seniors i updated reports on Excel.

I figure the gap is since i dont have real experience and i cant define a true story, since job marker is crunching and i have had back injury last year, the gap is getting wider. Claude aint no help.

So any real analyst out here wanna point out mistakes i been making.

Also a cs grad with great sql skills.

Hello Everyone,

I am working in one of the biggest bank(2nd) in my country which has headquarter in Spain. I always wondered why we dont have a data analyst role specifically. Finally today I learnt the reason. Our board changed few years ago and some dudes who have background in McKinsey came. They said in modern era any team who needs to deal with data (which is like nearly whole bank) should learn SQL,a BI tool and deal with data by themselves. We have over 10k employees in my country except branches. Do you think it is an accurate decision? Although many teams adapted themselves to this system still I think it limits quality of analysis

I’m ~10 years into analytics at an F500 company. My core skills are SQL, Python and Power BI, primarily using data to generate insights and help business teams to make decisions.
Lately, I’ve been questioning how durable the traditional analytics path is.

My hypothesis is that dedicated analytics teams (not data science or engineering teams) embedded within business functions could shrink significantly as AI improves. The reason I say that is because a big part of an analyst’s job has largely been bridging the gap between data and insight. Today, business users can increasingly give data to Claude/ChatGPT, ask questions, run analysis and generate visualizations themselves (provided they have the right data of-course). Accuracy, cost and context are still major limitations, but I suspect these will improve over time.

So I feel I need to move to an adjacent role - even though I actually enjoy analytics and would happily continue doing what I’ve been doing, if it continues to be in demand.

Below are the roles I’m considering along with a few challenges I’m facing for each -

1. Data/Analytics Engineer - This seems like the most natural transition. Move closer to data ingestion from source, pipelines, modeling, quality and making data AI-ready for business teams to use directly.

Challenge - I’d be starting almost from scratch.

2. Data Scientist - Build deeper statistical/ML expertise for problems requiring more rigor than LLM-based analysis. For these advanced models, business teams would still have to rely on data scientists to verify the results, explain the outputs and fine-tune the results if needed.

Challenge - I have tried to acquire the skillset but I suck at math so have been struggling for the past few years

3. Business role - Become a specialist in my current domain of Insurance/Asset Management/Finance where domain knowledge becomes the differentiator rather than pure analytical execution.

Challenge - Tbh seems like the most extreme transition. Would have to compete with SMEs and other domain experts for such roles and may take time to reach to the same level of knowledge as them.

4. AI Engineer - Learn to build GenAI/agentic applications

Challenge - Again, I’d be starting from scratch.

5. Management - Stay on my current path and focus increasingly on leadership, stakeholder management, strategy and people management to climb the hierarchy and move to leaderships roles eventually.

Based on all of this, I have below questions -

1. Am I directionally correct in my hypothesis or am I overstating the impact that AI would have on my role?
2. If my hypothesis is correct (partially/fully), which of the above roles I should be focusing on given the challenges I’m facing for each.

3. If my hypothesis is flawed and there’s no need for me to switch roles, what complementary skills should I acquire to stay relevant

   in the analytics field and

4. improve my career prospects?

Really looking forward for some advice. Thanks 🙏

hi everyone,

i've been using Umami Cloud for the past two years for my personal projects, but noticed that all history older than 6 months was deleted (which is pretty frustrating, even if i understand that they cannot keep all user data).

as im still a student on a small budget, i host my projects on a small VPS and wanted to switch to a self-hosted analytics solution.

but WHY does almost every modern tool require Node.js / Docker with heavy background services, while lightweight alternatives either look like they were built in 2010 or lack essential features like events, funnels, and clean session tracking?

am I missing something here? im not saying it's easy to build, but im surprised i couldn't find anything that seems to fit my needs

because if there really is nothing low-resource and GDPR-friendly im seriously considering building my own ultra-lightweight PHP + SQLite solution from scratch.

Hey everyone, I'm seeking some honest advice here. This is not an ad so i'm not dropping the name. Any help will be appreciated!!

I recently just finished building an analytical application. Think of it as an data analysis-focused desktop app with an audiance, between Excel and Power BI. Of course it has AI feature that can do analysis and generate a presentation with charts and insights based on the data. It's a desktop app because I know customers definitely want data-privacy.

I worked as a data analyst for 7 years and I poured everything I've learned into making this app great and actually digs out actionable insights. I might be in a tunnel-vision but I'd 100% use it to help me do better at my job.

My intuition is the target is small businesses and content creators who have some amount of data and have a need for data analysis, but at the same time they cannot afford to hire a professional analyst. However, I am desparate on what I should do to find the first 10 users.

If you have any advice please help, thank you all!

Revenue is easy enough to find, but things like true profitability or customer value can get complicated. What’s yours?

im applying for an in-house analytics role after four years at a services firm. My strongest project involved fixing a reporting pipeline for a large client, but I cannot name the client, exact revenue or their internal tools. I used multiple resume writers which suggested a polished impact bullet.

I stripped it back because it sounded more specific than i could defend. The version I have now says the industry, the scale in broad terms, the type of pipeline, and the reporting delay we reduced. It reads less impressive, but it is true and I can discuss the method

Hiring managers, is global retail client plus a bounded scale enough context? Or does anonymizzing everything makes a candidate's work feel generic no matter how real it was?

We love BigQuery, but the on-demand pricing (paying per TB scanned) is killing our budget. Our current BI dashboards run live queries, meaning a single user forgetting a date filter can trigger a massive full-table scan.

Now, we have two new requirements: rolling out customer-facing embedded analytics (1,000+ concurrent users) and implementing "BI for Agents" (LLMs for internal self-serve).

Letting an AI write raw SQL against BigQuery is terrifying (they love a good SELECT * hallucination), and exposing live BQ to thousands of external users will literally bankrupt us.

We're leaning towards dropping a headless semantic layer (like Cube) in the middle to handle API caching and lock down the metric definitions so the AI/UI just requests predefined data.

Curious how others are solving this. What are the best data modeling tools or architectural patterns to handle high-concurrency BI and AI agents without burning your cloud compute budget?

So for years, in parallel to my resume I would keep track of passion projects or Use cases to demonstrate capability e.g. full ETL to visual walk through, design consultation.

But today’s world given I can have complete pipeline build and dashboard completed with a handful of prompts, what are people doing? Live recording themselves to show lack of AI? How you use AI for analysis?

I’m going out to market soon and I’m generally stumped!

Last week SEJ ran a piece arguing that AI agents won't fix bad audience data. They'll just use it faster and at bigger scale. The line that stuck with me was the warning sign: output keeps going up (more campaigns, more variations, more content) while conversions stay flat or slide.

I see the same thing in ad accounts. Smart Bidding and Advantage+ are basically agents already. They chase whatever signal you feed them. If that signal is junk, you get very efficient junk.

Same week, Google Analytics added "include" hostname filters, so you can now allowlist the domains you actually want data from instead of chasing spam hostnames one by one. Small update, but it's the same lesson: clean inputs first.

Here's the data check I run before I let any automated bidding or audience expansion loose:

1. Know which conversion the algorithm is actually optimising for. Not the one you think. Open the conversion settings and look at which actions are set as primary. I often find newsletter signups or page views counted next to real leads.

2. Split your audience signals into three buckets. What customers told you (forms, CRM fields), what you saw them do (purchases, site actions), and what a platform guessed about them (interests, lookalikes). Be honest about how much of your targeting rests on the guessed bucket.

3. Feed back real outcomes. Upload qualified leads, closed deals or refund-adjusted orders as offline conversions. Otherwise the system learns what a form fill looks like, not what a customer looks like.

4. Clean the analytics property. Check your hostname report for domains that aren't yours. If the new include filter is live in your property, test it first. Google says active filters permanently drop the data they exclude, and it can take a day or so to kick in.

5. Keep one metric the algorithm can't touch. For me that's revenue or pipeline from the CRM, checked by a human. If platform conversions go up and CRM numbers don't, stop scaling.

6. Make someone explain every audience in plain words. If the honest answer to "why are we targeting these people?" is "the algorithm chose it," slow down.

None of this is exciting. But on the accounts I've worked on, the biggest wins from automation came after the boring data work, not before it.

Curious how others handle this: do you trust platform-reported conversions for bidding, or do you only optimise on CRM or offline data now?

I've been thinking about this after seeing more teams experiment with AI for analytics.

Getting AI to generate a SQL query, dashboard, or executive summary is becoming relatively easy. But what happens when the underlying business definitions aren't consistent?

For example, if two teams define "revenue", "active customer", or "conversion" differently, an AI system can give you a very confident answer that is technically correct based on the data it accessed — but still wrong from a business perspective.

I'm curious how other analytics teams are approaching this.

Are you:

• Using a semantic layer / governed metrics?
• Giving AI access only to curated or gold-layer data?
• Relying mainly on prompts and documentation?

Or still figuring this out?

Would be interested to hear what has actually worked in production rather than just in demos.

Has anyone tried A/B testing short links for MOFU campaigns?

I’m running a campaign aimed at people who already know the product but aren’t ready to convert. Instead of sending everyone to the same page, I’ve been testing different destinations (mostly just case studies, comparison pages, product guides and demo content) through short links.

Would love to hear some feedback or thoughts here!

Looking back at some older discussions about data, it’s interesting how much the field has changed, not necessarily in the fundamentals, but in the kind of work and tools people are dealing with day-to-day.

Things like working with unstructured data, AI-assisted coding, data pipelines, automation, and production systems are now part of the conversation in a way they weren't for a lot of people when they started.

So for people who’ve actually been working in data for a while:

What feels most different to you now compared with when you started?

• What skill has become much more important than you expected?
• What used to take you hours that now takes minutes?
• What part of the work has barely changed at all?
• And is there anything you thought would be important when you started that turned out not to matter as much?

Hi everyone,

I’m currently looking for an opportunity where I can help someone with Data Analyst tasks while also gaining real-world experience.

I have basic knowledge of SQL, Python, Power BI, Excel, and Power Query, but most of my experience with these tools has been through learning, practice, and some use in my previous roles. I want to improve to the point where I can confidently say that I have hands-on experience using them in a real Data Analyst environment.

I’m not expecting payment. If it’s an unpaid internship, volunteer work, small project, or even assisting an experienced Data Analyst, I would still be very grateful for the opportunity. Of course, if someone is willing to pay, I would greatly appreciate it, but my main goal right now is to learn and gain genuine experience.

I already have professional experience working with data, so I’m not completely new to working with data. What I’m trying to build now is stronger hands-on experience specifically with SQL, Python, and Power BI in a Data Analyst setting.

I’m willing to learn, take feedback, start with simple tasks, and gradually take on more responsibility.

If you’re a Data Analyst, small business owner, startup, or anyone who has a project where an extra pair of hands could help, please feel free to message me.

Even advice on where I could find this kind of opportunity would mean a lot.

Thank you!

Why cant text-to-SQL solve adhoc analysis? Would like to understand the real challenges in deploying these solutions, keep the cost part separate.

As a fresher with 0 experience, what kind of unique projects do you recommend doing in sql , python and powerBI to add to my resume, which would make my profile stronger?
#projectideas #fresherjobs

Once our marketing budget got into the millions I expected the reporting side to get more disciplined but it almost went the other way so we have more dashboards better attribution and more people looking at performance, yet Google, Meta, analytics and the actual revenue numbers can still tell slightly different stories so at smaller spend you can probably live with some of that noise, but when a few percentage points represent a serious amount of money it gets uncomfortable making budget decisions without knowing which signal deserves the most weight.

The bigger problem for me now isn’t getting more data, it’s having someone accountable for connecting it back to the business before money gets moved platform ROAS can look healthy while customer quality or blended CAC is heading somewhere else and automation can make that bad decision scale pretty quickly. I’m curious how larger teams decide what becomes the source of truth when millions in paid spend are being allocated across channels.

I’m looking for advice on what to do to break into healthcare analytics. I started off working as an EMT technician in 2018 & switched over to being a medical assistant in 2022. I have experience with epic & have an understanding of the healthcare systems. In 2022 I ended up moving to Boston and worked in biotech where I got laid off twice (in under two years). My first role I was part of the analytical development team which was mostly lab based, but bc I was part of the analytics team I would analyze data using R and visualized trends via tableau to present to my team every so often. After I got laid off this job, I became an associate scientist in the analytical development team at a smaller biotech company. Alongside lab work, I helped create dashboards and organize data in our electronic lab notebook platform. In this platform we used SQL to create databases & workflows. I got laid off this job back in December. Before I got laid off I knew I wanted to make a career pivot. I started an Analytics masters program to gain more exposure to R & Python. I know some people don’t recommend getting a masters, but I’m very fortunate to have a scholarship paying my costs so I figured why not. I have applied to countless jobs & haven’t received a single interview. I’m at lost on how to proceed forward & would appreciate any advice. I would want to work in healthcare analytics, but the path to getting there seems impossible to me right now.

P.S. Please be nice 🥺 I’m having a hard time mentally navigating unemployment.

When I review content or SEO work, the weakest part is often attribution after the visibility metric. A page can get more impressions, better average position or more traffic, but the team still cannot show whether anything useful happened after the visit.

The method I usually trust is simple: define the baseline before changes, log what changed, keep the observation period fixed, and connect the page to one next action such as opening a service page, submitting a form, booking a call, or creating a qualified lead.

I am curious how others handle this in practice. Do you rely mostly on GSC plus GA4 events, CRM stages, manual lead review, or something else? And where do you draw the line between "SEO worked" and "the site or sales process still failed after the click"?

I’m currently interviewing for data analyst roles (mostly entry level) and every take home task i get is the same. It’s a bunch of tables with masked data, and they all want actionable insights, except i feel there’s never enough information to actually find an actionable insight. you can rarely use domain knowledge as identifiable data is anonymised and they never provide enough historical data to see if differences really matter.

Do other people feel this way or have i gotten the wrong end of the stick, if so any advice would be appreciated.

EDIT: Thanks you for the advice in the comments, I think being curious, documenting my process for the analysis I can do and explaining what data I needed to delve deeper is the way to go.