I’ve developed a tool that transforms a raw spreadsheet into a live analytics workspace without the need for a server (a fully browser-based service). You’ve probably seen similar examples; what do you think such a tool should be capable of? For instance, I’m currently storing the data specifically within the browser so that the LLM only retrieves the column names, but the user could share the analysis results with the LLM if they wish.

What are your thoughts on this? What advice would you give?

Hey all. I was debating going to nursing school to get a clinical license under my belt. My bachelor's is general studies in social sciences and humanities. Ive been a Medicaid Waiver Case manager and a CNA (PRN now) for over a year. Just pays horrible and I can't live like this anymore.

Im wondering if I should bother with getting at least my LPN(then likely RN>BSN) or instead just start learning SQL, Excel, and Tableau and start building a portfolio and apply for tons of jobs. How hard is it to get a job in Healthcare data analytics?

Wwyd?

Every time I try to set up a regular cadence for reviewing metrics it works great for like ten days and then something comes up, the rhythm breaks, and I'm back to doing ad hoc checks whenever something looks weird or someone asks a question. It's reactive and I know it's reactive but fixing it has been harder than expected.

The core problem seems to be that the routine I design is too ambitious. Daily dashboard review, weekly trend summaries, monthly deep dives. Sounds clean on paper. In practice the daily part eats time I don't have and I drop the whole thing.

Curious how others have structured this. Do you anchor reviews to specific events like end of sprint or start of week rather than a strict day count? Do you keep it minimal at first and only expand when the habit is actually locked in? I've heard people swear by tying it to something already on the calendar so it's not a separate commitment.

Also wondering if the tool matters here. Some dashboards make it easy to do a five minute gut check and others require enough clicking around that you just avoid opening them. I'm genuinely unsure whether the friction of the tool itself kills the habit more than the schedule design does.

What actually worked for you long term?

Where can I learn Git by doing it visually?

Searching for an enthusiastic Learner who can help me build a community in exchange for mentorship and earning post revenue generation.

I am looking for a Bachelor of Commerce graduate, eager to learn and grow his/her professional career.

should be hungry for knowledge and can spare at least 3 to 4 hours on weekends.

edit:
Look at it like an unpaid internship to start with but getting paid when the community and workshops generate revenue.
You will learn valuable skills, domain knowledge and get a chance to earn faster and more than a traditional job.

Anyone here who switched into data analytics recently? How did it go? Is it still worth getting into nowadays?

I’m considering a career switch, but people often say the market is overcrowded already.

Curious to hear real experiences from different countries and backgrounds:
- How long did it take you to get your first job?
- What skills/projects helped the most?
- Was your previous experience relevant?
- Do you still think data analytics is a good field to enter today, in 2026?

Especially interested in stories from people who switched recently, please share!

i've been enrolled in a bootcamp(data engineering) for about a year now and i'm confident in my skills atleast for entry level roles. i'm based in Ethiopia and i can say that there's almost no data engineering jobs here ,there're very few open positions for data analyst or scientists which requires atleast 4years experience and you know that remote jobs are even more competitive and struggling for entry levels. the only tech roles here seems to be backend devs,frontend and fullstack(there're tons of jobs ).what should i do ,i love data but the market is really bad here.
thanks

24 got tired of minimum wage jobs and want to get a degree. Which one is safer?

I was considering making an AI agent to create visualizations and what seemed like what should be an obvious question is turning into a not so obvious thing. How do you determine what the optimum visualization to present analytics? At its distilled simplest, when do you pick a pie chart over a column chart? What determines that? As you add more visualizations, what are the decision points for choosing different visualizations.

I'm not talking so much about decorations but how do you choose the best base visualization.

Is it actually true that, on average, an app loses 77% of its users within the first 3 days and around 90% within the first month?

Does that literally mean if I acquire 100 users today, only about 23 are still using the app after 3 days, and only around 10 remain after 30 days?

Are these real industry benchmarks, or are they based on specific categories like gaming apps?

Curious to know more about this.

Something I keep running into: a number lands in an exec report, someone senior says "that doesn't look right," and then the real test of your data setup begins. On some teams the answer is two days of tracing it backwards through queries, exports, and a spreadsheet from 2022 until someone finds the broken step. On others it's a few minutes of following the lineage from the report back to the source. Same problem, completely different outcome, and it has almost nothing to do with how good your BI tool looks.

Lineage tends to get sold as a compliance/audit feature, which I think is why it gets skipped. But the value isn't the audit. It's trust (an analyst can see the path from source to dashboard instead of hoping), debugging (find the cause in the chain instead of interrogating everyone who touched it), and impact analysis (see what breaks before you change something upstream).

I'm not selling anything, just curious how others handle it. When a number gets challenged, what's your actual process to prove where it came from? Manual detective work, a lineage tool, tribal knowledge? And how long does it usually take?

Singapore shows up for this constantly because it is one of the largest data center hubs in the world. AWS, Google Cloud, Azure, Alibaba, and Tencent all run nodes there, so crawlers, scrapers, uptime monitors, and security scanners egress from Singapore IPs even when the operator sits somewhere else entirely.

For a Shopify store or app listing, the usual suspects are:

• AI crawlers pulling your content for training or for live answers
• Competitor tools scraping your pricing, features, and copy
• Platform and SEO crawlers hitting your pages on a schedule
• Uptime and security scanners

How I separate bots from humans before assuming anything:

• Session duration: near zero seconds is a bot
• Pages per session: one page and gone is a bot
• Conversion rate from that geo: if it is basically zero, that is your answer
• ASN / ISP: this is the strongest signal. If the traffic comes from a hosting provider ASN (a data center) rather than a consumer ISP, it is a machine. Most analytics tools let you segment or filter by this.

Practical move: filter data center ASNs out of your reporting so your real numbers stop lying to you, and stop optimizing for a country that never buys.

One nuance worth knowing: not all of it is junk.

Some of that crawling is LLMs reading your content, which is how you end up getting cited in AI answers later. It inflates your raw traffic, but it is not something to block outright if you care about being discoverable through AI.

Hello everyone,

I've been learning Data Analytics for some time now, but one of my biggest challenges has been staying consistent.

So far, I've completed Python and Statistics, and my goal is to become industry-ready within the next 4 to 4.5 months.

I still need to learn SQL, Power BI, and Excel, while also building a strong portfolio of projects.

I'd appreciate advice on how to structure my learning over the next 4.5 months. Specifically:

How would you categorize or prioritize these topics?

What sequence would you recommend for learning them?

How would you design a monthly and weekly study plan to stay consistent and make steady progress?

What are your practical tips for revision?

How do you balance revising previously learned concepts while continuing to learn new topics and building projects?

I'd really appreciate any insights, study strategies, or roadmaps that have worked for you. Thanks in advance!

I'm pretty new to integrating AI agents into my workflow, and I've really found value in using it for creating small diagrams and low-fidelity prototyping, something I used to spend a lot of time on across a lot of different programs. Though I still feel the tools are a little underbaked?

Ideally, I just want to feed an agent my confirmed requirements or a snapshot of a hand-drawn sketch and have it directly spit out a functional flowchart or basic interactive wireframe in one go. No context-switching and no rebuilding from scratch.
Is anyone actually pulling this off successfully right now? What tools, prompts, or custom GPTs/Agents are you using to bridge this gap?

I’m trying to decide between Performance Marketing and Data Analytics as a career in 2026.
My background: I have a BA in Economics , so I don’t have an engineering or computer science degree.

My priorities are:
Good long-term salary growth
Strong job opportunities over the next 5–10 years
Ability to work remotely or freelance in the future
A career that’s not likely to become obsolete because of AI

I’ve seen mixed opinions online. Some people say data analytics is oversaturated and entry-level jobs are very competitive. Others say performance marketing is becoming heavily automated by AI and ad platforms.

For those currently working in either field:
Which field has better job prospects in 2026?
Which is easier to break into without a technical degree?
Which has better salary growth over 5–10 years?
Which is more resilient to AI?
If you were starting from scratch in 2026, which would you choose and why?

I’d really appreciate honest insights from people working in these industries.

I’ve been a senior data analyst for about four years now, and my director has hinted that I’m next in line for a Head of Data or Analytics role when our current lead goes on maternity leave later this year.

I know the technical side inside out, but I have zero formal management experience. My company has offered to pay for a leadership course to help me prepare. I’ve been looking into the British Academy For Training & Development for their leadership and management training, as they seem to have some good executive education options here in the UK that focus on the actual business side of things.

Has anyone done courses with them, or are there other alternatives you’d recommend for someone stepping up into a departmental lead role? I want something that actually teaches practical management, not just corporate buzzwords

I’m working with a midsized ecommerce company that needs to forecast demand 30-60 days out across multiple SKUs. Right now they’re using spreadsheets and basically guessing, which is starting to break down as they scale.

We’ve been looking at a few options:

Alteryx : seems really powerful but honestly feels more built for ETL work than predictive analytics. The learning curve looks steep for our analyst team.

Data Robot: the accuracy looks great but it’s pretty expensive and feels like overkill for what we need right now.

Pecan ai:came across this recently. The no-code angle is appealing and it connects to Snowflake which is a big plus. Anyone have experience with it?

Ideally we need something that:

* Our analysts can actually use without a data science background

* Handles time-series forecasting well

* Won’t take months to set up

* Doesn’t require a massive budget

What have you used for demand forecasting that actually worked? Would love to hear what’s been reliable for similar use cases.

I've been operating at a Principal level this year, and even more so recently, with a significant change in my role description after a restructure.

Anyone had success arguing for a promotion due to performance and responsibilities when a position didn't exist in the org chart?

Hi everyone.

I'm strongly considering a career in this field, but right now I only have a Bachelor's degree in Psychology and it's unlikely that I'll be able to get a graduate degree in STEM. At best, I can take some courses in SQL, Tableau, etc. or earn a certificate. I am also considering bootcamps.

BI/DA professionals in the U.S.: What relevant education did you have when you landed your first job? What type of education do you typically see BI/DA professionals have?

Thanks.

With the rise of AI what skillsets do you think professionals will need to carve and flesh out to remain competitive. Do you lean more into soft skills or technical?

My management recommended I learn “data analysis“ to progress in my career at my current company. I already use basic Excel databases and do basic Tableau analysis of some quality data, so I started taking more advanced classes and investigated SQL. I understand more or less what tools could be useful but my management said I also needed to learn how to interpret data and use it to understand a problem, as well as write a report. This is where it gets confusing for me.

I can’t find online courses specifically focusing on that, and the more I read about that the less I understand what skills I lack for data interpretation and reporting. I have two Masters of Research in applied social sciences and humanities, and I had to build for each of them a 1-2 years research project.

A big part of each research project was to establish which “data” (we didn’t call it that way but it was basically data) I needed, basically collecting, filtering, organising and cleaning up the information before turning it into stats and charts and drawing hypothesis and conclusions from this based on my knowledge of the topic and on further research into new “data”. The second research project in particular was quite number heavy since I had to lead a small experiment with numerical results which changed depending on the parameters of the experiments.

I asked my management to tell me what skills I need on top of what I had learned through academic research and they gave me a very confusing and vague response, basically saying that this is not the same as data analysis because data analysis is a computer science, not a human science and academia is about theory while we work with real life data (my data was real at university), so I need to take a class that will teach me how to make sense of the numbers.

I am extremely confused and I may not have understood what is analytical reporting and data interpretation because I really don’t understand what are the skills I need! What is so different on a day to day basis from the core of academic research?

We run most of our audiences out of mParticle. Meta and Google take the syncs without much fuss. What I cannot get a straight answer on is what happens when those same segments get pushed into streaming TV advertising. Does the audience carry over with a match rate worth talking about, or does most of it fall off the second it leaves the walled gardens.

A big piece of this is how much identity survives. mParticle resolves a user across web and app for us. I have no read on how much of that holds up once it hits a CTV platform running mostly on device graphs and IP. If half the audience goes unmatched then the targeting falls apart before measurement even enters the picture.

The other piece is attribution. Say I get the audience over there cleanly. Can I still tie exposure back to conversions, or is post-view attribution on CTV still vague compared to paid socials?

Has any one of you run a CDP to CTV activation that held up on both ends. Also wanna know what platform took the audience and whether the match rate and back-end attribution were good enough to continue spending on.

Hey guys, I’m an Information Systems major with minors in finance and data science, and I’ll be starting my senior year soon. I really want to pivot into the business analyst field post-grad, but I want to make sure my resume actually stands out.

For those of you working as BAs: what specific skills, tools, or projects should I focus on developing over the next few months to make myself competitive?

Open to any and all advice. Thanks!

I've been working from home for years and am totally spoiled. I realized that a flexible schedule and very little required interactions with others is a need for me going forward. I'm transitioning into healthcare tech/analytics and curious what positions/companies should I focus on while job searching to fit my requirements?