"Can we get visibility into this?" - I will not act on this information. I want it to exist near me, like a plant.

"The data doesn't feel right." - The data contradicts what I already told my boss.

"Interesting." - I stopped listening at slide two.

"Can you make it more actionable?" - I don't know what I want. Iterate until I recognize it.

"We're a data-driven organization." - We have dashboards.

"We need to be more data-driven." - We also don't open them.

"Quick question." - Project.

"Super quick question." - Two projects and a definition fight.

"Can we add AI to this?" - I attended a webinar.

"What's our AI strategy here?" - I attended two webinars.

"Directionally accurate." - Wrong, but in a direction I like.

"Single source of truth." - My source. Of my truth.

"Let's align on definitions first." - This meeting will not survive. Nor will the next one. See you in Q3.

"Can you pull this real quick? Shouldn't take long." - I have already promised it to someone.

"Great analysis! Let's discuss next steps." - This will never be mentioned again.

"Great analysis! Really great work." - This will never be mentioned again, but warmly.

Over the last 6 weeks I read and categorized 237 data job posts from LinkedIn (not from the career tab), one by one. The headline finding: 0 out of 237 disclosed a salary. Not one. If you're wondering whether it's normal that you can't find pay info, it is, and it's not you.

Where the data comes from: I run a small data learning platform, and we collect and hand-review job posts from our LinkedIn network for our students. That's why I have this dataset. Our network skews international, so take the mix as one window on the market, not the whole market.

Second finding: "data job" is four different jobs. The skill lists barely overlap once you split by role:

• Data Analyst (43 posts): SQL 49%, Excel 49%, Power BI 33%, Python 30%. Yes, Excel ties SQL. The "Excel is dead" advice does not survive contact with actual job posts.
• Business Analyst (52 posts): requirements 62%, stakeholder management 33%, SQL 25%, Agile 23%. Mostly a communication job with a data layer.
• Data Engineer (70 posts): SQL 60%, Python 56%, pipelines/ETL ~45%, data modeling 21%. The most technically consistent role of the four.
• Data Scientist (65 posts): Python 62%, ML 32%, SQL 32%, statistics 18%, and LLM keywords already in 15% of posts.

Across everything: SQL appears in 42% of all posts and Python in 41%. Nothing else comes close. If you're starting from zero and want the highest floor: SQL first, Python second, then the tool your target role uses (Power BI or Excel for analyst roles, Airflow/dbt-land for engineering).

Other things that surprised me:

• ~10% of posts are agency recruiters, often without naming the end client.
• Remote is holding up: of the posts that state a work mode, more than half say remote, another chunk hybrid.
• Very few posts say "junior" or "senior" at all. Most don't state seniority, which cuts both ways: don't self-reject based on a title.

Happy to answer questions about the methodology if there's interest. I'll probably redo this monthly as the dataset grows.

Hi guys, i have a quick question.

Is it worth pursuing data analytics if i don't have a tech/math degree? I have a BBA and I'm willing to learn.

I'm just wondering if it's worth even following or if I'm setting myself up for a job search failure

The university I wanna apply to offer a double degree in these two Fields, is it worth it for today job market, or I should stick with engineering or computer science

collected by hitting the public json endpoints for greenhouse, ashby and lever across 998 companies, once a day, and diffing each posting against the previous snapshot. no auth needed, these are the boards the companies serve to their own careers pages.

what the diff has caught in 14 days:
salary_changed 19,371
department_changed 15,549
closed 7,292
title_changed 1,549
location_changed 1,086
reopened 301

55,294 postings seen total, 48,303 currently open. 23,898 of the open ones carry a pay band, which is 49.5%.

two things worth knowing if you want to do this yourself. greenhouse has no structured salary field at all, the range is written into the description html and it is double escaped, so a naive parse gets you nothing and a slightly less naive one gets you garbage. and a dollar sign is not a currency, i nearly recorded a taipei role in TWD as a $700k job.

the salary_changed rows are the ones i'd point at. nobody keeps the before, so as far as i know this is the only place the previous band exists once the company overwrites it.

built off a job tracker i run, link in the comments if the method is useful to anyone.

Background: Bachelor’s in Data Science + Master’s in Business Analytics (Distinction) from a UK uni. Currently working in sales at BT, trying to move into Marketing Analytics or Consumer Analytics.
I’m specifically interested in the **marketing + consumer side…**consumer behaviour, why people buy, brand perception, market research, campaign analysis etc.

I’ve done projects around sentiment analysis, consumer perception and market research, but I don’t have paid UK experience in analytics yet.
I’ve started the Google Analytics course and I’m also considering the Meta Marketing Analytics course.

So my first question is do these actually help on a CV, or are they mostly just nice to have like?
Also, what kind of projects would actually be worth building for my CV? Ideally something that can show I can do the actual job, not just another generic Kaggle project.

My second question is eould you guys recommend me focusing on:
Marketing analytics projects / portfolio
GA4 / Meta / SQL / Power BI etc.
CIM / MRS or other certifications
Getting a BA/Data Analyst role first and then moving into marketing
Something else entirely?

Iknow sponsorship can make things harder and companies may be less willing to take a chance on me. That’s unfortunately not something I can control, so I’m just trying to make myself as employable as possible.

I asked something similar here before but didn’t get much useful advice, so trying again

If you were in my position, what would you actually do over the next 3–6 months to land that first Marketing Analytics role? Honest/blunt answers appreciated.

Hi all. I just completed my bachelors in business administration and I’m looking forward for a masters program in business analytics. I have been working as an intern in a firm while managing my studies. I am very new to this field of analytics and would appreciate if someone guides me a little about where to start,what projects to look forward to and more about the same. I have no prior experience to coding or any language.

Been running basically all my analysis work through AI for the last 3 months. Some of it saved me hours every week, some of it embarrassed me in front of my manager. Since the "is AI actually useful or just hype" question comes up here every week, here's my list.

Do:

• set up metric definitions first. what "active customer", "churn", "revenue" actually mean in your business, with an example query for each
• dump every doc you have into it. schema docs, old validated queries, even you explaining the business in plain words. the AI is exactly as good as the context you build for it
• use it every day. it builds memory and learns from your corrections, my month 3 answers vs week 1 are not comparable
• be painfully descriptive. "gross revenue excluding refunds, calendar july, orders table" and not "how did revenue do last month"
• make it show the query before you trust the number
• start with questions you already know the answer to, that teaches you where it lies

Don't:

• don't trust a confident chart with no lineage. a 3 second answer looks finished and that does a lot of convincing
• don't dump your whole warehouse on it, curated beats volume. tell it which tables are deprecated
• don't let it define metrics on the fly, you'll get 3 different "active user" numbers in 3 sessions
• don't put an AI number in front of leadership without checking it once yourself. ask me how i know
• don't expect it to replace knowing your data model. it amplifies you, it doesn't substitute
• don't start from zero every session, keep the validated stuff and reuse it

The boring setup work in the first two weeks is the whole difference between "AI is a toy" and "AI now does 95% of my grunt work".

For context, what I used: Claude, ChatGPT, Supaboard and Power BI copilot(only the first week, then i moved to claude).

What's working for you guys?

Started logging my hours two months ago after a manager asked why a "simple update deck" took me a day and a half. Wanted a real answer instead of a guess.

What I found after 6 weeks of tracking: - 65% of deck time was formatting, alignment, and making charts match a template - 20% was actually pulling and checking the numbers - 15% was the part that mattered, deciding what story the data actually told

That ratio bothered me more than I expected. I'm supposedly the analytics person on my team, and most of my week was spent nudging text boxes.

One thing that helped a bit: building 3 or 4 locked master slide layouts (title, chart+takeaway, comparison table) and refusing to deviate from them for internal decks. Cut my formatting time down maybe 20%. Still not enough.

I've tried a couple AI drafting tools to auto-populate a first pass from raw data, results are mixed, some genuinely save time, others create more cleanup work than they save.

Curious what's actually working for people here. Are you using AI tools for the first draft, strict templates, something else entirely? Trying to figure out if this ratio is normal or if I'm just bad at this.

We can track almost everything now. Calls. Chats. CSAT. AHT. Transfers. Agent performance. QA scores. The problem is figuring out what in that pile of data is actually driving an outcome. Looking at conversation analytics and the gap seems pretty obvious. Sampling a small batch of calls gives you something to report on. But it can miss the weird stuff happening across thousands of conversations.

We’ve also been looking more into AI tools that analyze the actual conversations instead of relying on dashboards or sampled calls. In theory they can go through every conversation and find patterns tied to things like CSAT or handle time. Want to know how analytics teams handle this while growing. Are you still working from samples and dashboards or using AI to dig through the full conversation set and find patterns or root causes? My main concern is getting a neat sounding AI answer that falls apart once you check the actual calls. Being able to trace an insight back to the conversations behind it seems pretty important. Has anyone found these tools actually surface things your normal dashboards missed?

Quick one that's easy to miss. Blended CAC divides spend by all customers - new and repeat. Repeat buyers are cheap, so they drag the average down and flatter every channel.

Isolate just the new customers and the gap shows up. One channel I looked at: blended CAC ~$319, but a genuinely new customer cost ~$826. That's a 159% markup the dashboard never shows. A mostly-new-customer channel barely moved (~14%).

The rule: the fewer new buyers a channel brings in, the more its blended CAC lies. Budget on the blended number and you're paying for growth you're not getting.

Anyone else splitting new vs repeat CAC by channel, or mostly working off blended? Curious how wide the gap runs in other accounts.

I’ve been thinking about this question a lot while looking for my first opportunity in Data Science / Data Analytics.

Is the ideal candidate someone with a perfect degree?

Someone with 3+ years of experience?

Someone who knows every tool listed in a job description?

Or is it someone who can identify a real business problem, build a solution, and explain how that solution can create business value?

I’m genuinely curious to hear what recruiters and hiring managers think.

Because this is what I’ve been trying to do.

Instead of building another basic ML project, I built a Customer Retention Intelligence System focused on a real business problem: customer churn and lost revenue.

The system can:

• Predict customers who are at risk of churning

• Segment customers based on their risk/value

• Estimate potential revenue at risk

• Identify customers worth prioritizing

• Generate personalized retention strategies/offers

My goal wasn't simply to say:

I built a machine-learning model

I wanted to answer:

A customer is likely to leave. Now what should the business actually do about it?

That mindset has pushed me to work beyond just Python, SQL and machine learning — into business thinking, customer analytics, experimentation, visualization, and decision-making.

I’ve also been consistently practicing Python and SQL, learning how to communicate analytical insights, and building projects around problems that companies actually face.

But despite putting in this work, I’m still looking for my opportunity to prove myself professionally.

So I want to ask the recruiters, hiring managers, founders, and experienced professionals here:

What makes someone an ideal entry-level candidate in your company?

• Is it technical skills?

• Problem-solving ability?

• Business understanding?

• Communication?

• Projects?

•Curiosity and willingness to learn?

•Or something else?

And if you were evaluating my profile, what would you want me to improve or demonstrate before considering me for a Data Scientist / Data Analyst opportunity?

I’m not looking for sympathy.

I’m looking for honest feedback, opportunities, and a chance to prove what I can do.

If you’re a recruiter or hiring manager who works with Data Science / Data Analytics / ML roles, I’d genuinely appreciate your perspective.

And if my profile sounds relevant to something you're hiring for, I’d love to connect.

Hi everyone! I’m looking for 3 professionals in People Analytics or HR Data Fields to help with a small research project for my CIPD Level 7 qualification.

My research is about People Analytics and what makes workforce data and insights actually useful in real-world decision-making, rather than just producing reports and dashboards.

It would involve 5–6 open-ended questions by email, taking around 10 minutes. No preparation needed, and everything will be fully anonymised.

If you work with HR/workforce data and would be happy to share your experience, I’d really appreciate it!

I’m also happy to return the favour by sharing some of my experience or taking part in your research/questionnaire if needed.

Please comment or DM me if interested. Thank you!

Hi everyone, I left my job at an audit firm a while back, where I specialized in asset management funds and private equity. I also have some experience in general corporate audit and financial analysis. I’m having trouble finding a new audit role, so I’d like to take this opportunity to become a data analyst—a field that has always appealed to me—while staying within the finance sector. I understand that I need to master tools like SQL, Power BI, and Python/R, and also grasp AI and its applications in finance. I’m not sure where to start. In your opinion, which courses or certifications would be useful for becoming a financial data analyst? I’m looking for a clear roadmap. Thanks for your help!

Hi everyone I have a Masters in Psychology and I absolutely love statistical analysis and research design

For the longest time I've wanted to be able to design real world projects that can create a huge impact from a psychological point of view

I'm good at identifying variables and hypothesising

But I have no clue as to how to begin with real code and python libraries to engineer analyse and present applicable insights from raw data

I would like to transition from therapeutics to behaviour science for businesses very soon

Can anyone please point out how that works and alif there are success stories from people starting as I am..

Thanks so much

i have an interview for a data and analytics role under deals advisory which i barely have an idea about since im a new cpa and there not so much material about the role

my long term goal is to land a job in ib/pe/corp dev

Tec-Do’s recent growth numbers caught my attention.
Revenue grew 40.5% in 2024 and 74.5% YoY in the first nine months of 2025. Customer count also increased from 2,908 to 5,022.

What interests me is that this growth didn’t come with collapsing profitability: gross margin remained above 82% and net margin was still 43%. That makes me wonder whether Tec-Do’s Useful AI capabilities are becoming more reusable as the business scales.

If the same data, models, agents, and workflows can support more customers and markets, revenue could grow faster than the underlying operating cost base.

I’m curious: could AI scalability actually be one of the main drivers behind this growth, rather than just customer acquisition?

I’ve spent the last several months building out the semantic-continuity side of long-running AI-assisted analytics work: durable context, current-state rollups, decision history, provenance/supersession, schema/query knowledge, and task-conditioned rehydration.

That part is working pretty well.

The next problem is almost a consequence of that success: there can be a lot of useful context available, but I don’t necessarily want all of it active for the rest of a long-running session. I’ve started thinking about this as a separate runtime/context-efficiency layer.

A simple example:

A = stable instructions/scaffold

B = durable project context

C = intermediate exploration, tool output, code, temporary reasoning

D = validated analytical state

During analysis I may need A+B+C+D. Once I move into synthesis/writing, I may only need A+B+D. Semantically, that seems straightforward: C did its job, D preserves what matters, and the deeper evidence remains reconstructable. Operationally, it gets less obvious.

What happens to prompt-cache reuse when I restructure context?

When is selective pruning better than native compaction?

When is it cheaper or safer to keep a long session alive versus reconstruct a cleaner active context?

How much do cache TTL, context ordering, model limits, and runtime behavior change the answer?

And are the economics actually meaningful once you measure the whole workflow rather than theoretical token savings?

That’s what I’m starting to investigate now.

I made a simple visual showing how I’m separating semantic continuity from runtime/context efficiency. I’ll put it in the first comment since I can’t attach it to the post.

For people operating long-running agent workflows at meaningful scale:

• Where are you deliberately restructuring or pruning active context? Are phase/workflow boundaries useful?
• How are you balancing stable-prefix/cache reuse against removing intermediate context that has finished its job?
• What have you learned about restart/cold-cache economics versus simply continuing a growing session?
• Which caching, compaction, state, or runtime behaviors mattered in practice that weren’t obvious at first?
• What am I not asking about that I probably will be six months from now?

I’ve read a fair amount of the published material available. I’m especially interested in the things people learned from running real systems that never made it into the docs or blog posts.

Not looking for the usual answers: better visuals, Gartner's "leader" badge, or "it's cheaper than Tableau." I get all that. But a lot of companies still end up paying real money for it (Premium/Fabric capacity, licenses, training, migrations) when there are free or cheaper tools that do similar things.

I want to understand the real reason a company decides Power BI specifically is worth that spend — not a feature list, something about how that decision actually happens inside a company.

If you've seen this decision get made where you work, or made it yourself, what's the real reason? Even something small or weird nobody usually says out loud would help.

I've been interning at medium sized engineering company for the summer. Although I am studying computer science, my internship was not computer science related (long story), but for the past month I've been working on a Power BI dashboard for a specific department.

I found the work fun and rewarding, and since the company had no dashboards at all previous to the ones I made, other departments now want their own. For that reason, they are hiring me part-time while I am in school to build out these dashboards.

​For *reasons*, there is a chance that Power BI will not be the tool we use for dashboards going forward, instead we need a self-hosted, open source alternative.

From the research I've done, metabase and superset are the two big dogs in the space, with superset requiring the users to be more technically literate. The people for whom I will be making these dashboards do not know SQL and are not very technically literate. So my question is this, for those who have used these platforms, do they offer the same visualizations as Power BI and are they easy to customize and use for non technical people? Or should I look elsewhere, like the python library streamlit where I will have much more customizability options?

I am new to data visualisation in the business sphere, so if there are alternatives I have not considered, please let me know.

I’m working currently but looking for a data analyst or business analyst role in another city thats way more competitive.

I have almost 3 YOE (2 full time and 1 for other), and a masters degree in analytics from a reputable institution. I don’t know if I need any certifications or project’s or what to do to stand out. I work for a university currently and that doesn’t seem to help me when I’m applying to enterprise level roles, I feel like I’m not taken seriously. How do I stand out?

I forgot to add that I’ve been applying since the end of January now (350 apps) and I tailor every resume to the job.

I wanted to ask opinion of people on exposing everything a data team owes to Claude with a context layer explaining what is what with data lineage.

If a company has its own Claude subscriptionand then everything is exposed to Claude - report logic, metadata/dictionary of tables, logic/code of derived tables, operations of our pipelines etc via excel/markdown/html files explaning the whole context and then instructions Claude to not make assumptions/to ask verification questions (basically add safeguard rules in English in its instructions manual) then you expose this to end users company wide.

what is the issue with the idea? (if cost is not an issue)

Hello everyone, hope yall good

So did anyone worked with gaming data like (DAU , MAU,...) like what tips u have and any recommendations to give like YT channel or smtg like that and where can I get this kind of data....ADIOS

Everyone always mentions that in today's rough job market, networking is key to improving your chances to landing a job. However, what does that really look like?

If I were to see a job posting that I believe I am a good fit for, however no one in my immediate network works at this company, would you guys recommend I reach out to linkedin mutuals who do work at this company to attempt to get a referral?